https://wikipediaquality.com/api.php?action=feedcontributions&user=Camila&feedformat=atomWikipedia Quality - User contributions [en]2024-03-28T19:52:05ZUser contributionsMediaWiki 1.30.0https://wikipediaquality.com/index.php?title=Syntax_Analyzer_a_Selectivity_Estimation_Technique_Applied_on_Wikipedia_Xml_Data_Set&diff=25404Syntax Analyzer a Selectivity Estimation Technique Applied on Wikipedia Xml Data Set2020-09-22T19:21:27Z<p>Camila: + infobox</p>
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<div>{{Infobox work<br />
| title = Syntax Analyzer a Selectivity Estimation Technique Applied on Wikipedia Xml Data Set<br />
| date = 2013<br />
| authors = [[Muath Alrammal]]<br />[[Gaétan Hains]]<br />
| doi = 10.1109/DeSE.2013.10<br />
| link = http://ieeexplore.ieee.org/document/7041083/<br />
}}<br />
'''Syntax Analyzer a Selectivity Estimation Technique Applied on Wikipedia Xml Data Set''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Muath Alrammal]] and [[Gaétan Hains]].<br />
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== Overview ==<br />
Querying large volume of XML data represents a bottleneck for several computationally intensive applications. A fast and accurate selectivity estimation mechanism is of practical importance because selectivity estimation plays a fundamental role in XML query performance. Recently proposed techniques are all based on some forms of structure synopses that could be time consuming to build and not effective for summarizing complex structure relationships. Precisely, current techniques do not handle or process efficiently the large text nodes exist in some data sets as [[Wikipedia]]. To overcome this limitation, authors extend previous work [12] that is a stream-based selectivity estimation technique to process efficiently the English data set of Wikipedia. The content of XML text nodes in Wikipedia contains a massive amount of real-life information that techniques bring closer to practical and efficient everyday use. Extensive experiments on Wikipedia data sets (with different sizes) show that technique achieves a remarkable accuracy and reasonable performance.</div>Camilahttps://wikipediaquality.com/index.php?title=Introduction_to_Anatomy_on_Wikipedia&diff=25403Introduction to Anatomy on Wikipedia2020-09-22T19:20:19Z<p>Camila: Embed for English Wikipedia, HTML</p>
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<div>{{Infobox work<br />
| title = Introduction to Anatomy on Wikipedia<br />
| date = 2017<br />
| authors = [[Thomas Stephen Ledger]]<br />
| doi = 10.1111/joa.12640<br />
| link = http://onlinelibrary.wiley.com/doi/10.1111/joa.12640/full<br />
}}<br />
'''Introduction to Anatomy on Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Thomas Stephen Ledger]].<br />
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== Overview ==<br />
Wikipedia (www.wikipedia.com) is the largest encyclopaedia in existence. Of over five million English-language articles, about 6000 relate to Anatomy, which are viewed roughly 30 million times monthly. No work parallels the amount of attention, scope or interdisciplinary layout of [[Wikipedia]], and it offers a unique opportunity to improve the anatomical literacy of the masses. Anatomy on Wikipedia is introduced from an editor's perspective. Article contributors, content, layout and accuracy are discussed, with a view to demystifying editing for anatomy professionals. A final request for edits or on-site feedback from anatomy professionals is made.<br />
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Ledger, Thomas Stephen. (2017). "[[Introduction to Anatomy on Wikipedia]]".DOI: 10.1111/joa.12640. <br />
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{{cite journal |last1=Ledger |first1=Thomas Stephen |title=Introduction to Anatomy on Wikipedia |date=2017 |doi=10.1111/joa.12640 |url=https://wikipediaquality.com/wiki/Introduction_to_Anatomy_on_Wikipedia}}<br />
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Ledger, Thomas Stephen. (2017). &amp;quot;<a href="https://wikipediaquality.com/wiki/Introduction_to_Anatomy_on_Wikipedia">Introduction to Anatomy on Wikipedia</a>&amp;quot;.DOI: 10.1111/joa.12640. <br />
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</code></div>Camilahttps://wikipediaquality.com/index.php?title=Keeping_Up_on_Current_Events!_a_Case_Study_of_Newcomers_to_Wikipedia&diff=25402Keeping Up on Current Events! a Case Study of Newcomers to Wikipedia2020-09-22T19:19:12Z<p>Camila: Embed</p>
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<div>{{Infobox work<br />
| title = Keeping Up on Current Events! a Case Study of Newcomers to Wikipedia<br />
| date = 2018<br />
| authors = [[Ang Li]]<br />[[Rosta Farzan]]<br />
| doi = 10.1007/978-3-030-01129-1_22<br />
| link = https://link.springer.com/chapter/10.1007%2F978-3-030-01129-1_22<br />
}}<br />
'''Keeping Up on Current Events! a Case Study of Newcomers to Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2018, written by [[Ang Li]] and [[Rosta Farzan]].<br />
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== Overview ==<br />
Online production communities such as [[Wikipedia]] and OpenStreetMap play an important role in connecting the public with major events in society. The popularity of a major event, together with the popularity of online communities brings the general public to collaborate on and co-create knowledge about the event. The high level of interest in capturing what draws the attention of society can particularly help online production communities meet one of the essential challenges they face: attracting and retaining newcomers. In this work, authors explore how newcomers in such communities respond to knowledge production around major societal events. Analysis of the participation of 506 newcomers to Wikipedia articles related to three highly popular events shows that the popularity of the events attracts a new wave of users to the online community. These newcomers provide valuable contributions to the community, however, at a differing level depending on their initial motivation and experiences. Those participants who joined the online community solely to contribute to one topic or event are more likely to face challenges in contribution and leave Wikipedia after limited contribution. Authors discuss factors and patterns of newcomers’ early and longer-term participation in Wikipedia in relation to three popular events.<br />
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Li, Ang; Farzan, Rosta. (2018). "[[Keeping Up on Current Events! a Case Study of Newcomers to Wikipedia]]".DOI: 10.1007/978-3-030-01129-1_22. <br />
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{{cite journal |last1=Li |first1=Ang |last2=Farzan |first2=Rosta |title=Keeping Up on Current Events! a Case Study of Newcomers to Wikipedia |date=2018 |doi=10.1007/978-3-030-01129-1_22 |url=https://wikipediaquality.com/wiki/Keeping_Up_on_Current_Events!_a_Case_Study_of_Newcomers_to_Wikipedia}}<br />
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Li, Ang; Farzan, Rosta. (2018). &amp;quot;<a href="https://wikipediaquality.com/wiki/Keeping_Up_on_Current_Events!_a_Case_Study_of_Newcomers_to_Wikipedia">Keeping Up on Current Events! a Case Study of Newcomers to Wikipedia</a>&amp;quot;.DOI: 10.1007/978-3-030-01129-1_22. <br />
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<div>'''Policy and Participation on Social Media: the Cases of Youtube, Facebook, and Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Laura Stein]].<br />
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== Overview ==<br />
This article examines media participation in the domain of user policies. Author adapt Arnstein's typology of participation as a tool for recognizing specific participatory forms and the levels of power they afford. Applying this tool to user policy documents highlights an important dimension of how social media platforms position user participation and the common policy mechanisms structuring and delimiting participation online. While YouTube and [[Facebook]] policies offer minimal participation over site content and governance, [[Wikipedia]] offers maximal participation. Moreover, understanding the terms of participation inscribed in user policies facilitates both more informed choices about user involvement in online platforms and advocacy for more equitable usage terms in policy, law, and practice.</div>Camilahttps://wikipediaquality.com/index.php?title=Vector_Embedding_of_Wikipedia_Concepts_and_Entities&diff=25400Vector Embedding of Wikipedia Concepts and Entities2020-09-22T19:15:37Z<p>Camila: Embed</p>
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<div>{{Infobox work<br />
| title = Vector Embedding of Wikipedia Concepts and Entities<br />
| date = 2017<br />
| authors = [[Ehsan Sherkat]]<br />[[Evangelos E. Milios]]<br />
| doi = 10.1007/978-3-319-59569-6_50<br />
| link = https://link.springer.com/content/pdf/10.1007%2F978-3-319-59569-6_50.pdf<br />
| plink = https://arxiv.org/pdf/1702.03470<br />
}}<br />
'''Vector Embedding of Wikipedia Concepts and Entities''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Ehsan Sherkat]] and [[Evangelos E. Milios]].<br />
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== Overview ==<br />
Using deep learning for different machine learning tasks such as word embedding has recently gained a lot of researchers’ attention. Word embedding is the task of mapping words or phrases to a low dimensional numerical vector. In this paper, authors use deep learning to embed [[Wikipedia]] concepts and entities. The English version of Wikipedia contains more than five million pages, which suggest its capability to cover many English entities, phrases, and concepts. Each Wikipedia page is considered as a concept. Some concepts correspond to entities, such as a person’s name, an organization or a place. Contrary to word embedding, Wikipedia concepts embedding is not ambiguous, so there are different vectors for concepts with similar surface form but different mentions. Authors proposed several approaches and evaluated their performance based on Concept Analogy and Concept Similarity tasks. The results show that proposed approaches have the performance comparable and in some cases even higher than the state-of-the-art methods.<br />
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Sherkat, Ehsan; Milios, Evangelos E.. (2017). "[[Vector Embedding of Wikipedia Concepts and Entities]]". Springer, Cham. DOI: 10.1007/978-3-319-59569-6_50. <br />
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{{cite journal |last1=Sherkat |first1=Ehsan |last2=Milios |first2=Evangelos E. |title=Vector Embedding of Wikipedia Concepts and Entities |date=2017 |doi=10.1007/978-3-319-59569-6_50 |url=https://wikipediaquality.com/wiki/Vector_Embedding_of_Wikipedia_Concepts_and_Entities |journal=Springer, Cham}}<br />
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Sherkat, Ehsan; Milios, Evangelos E.. (2017). &amp;quot;<a href="https://wikipediaquality.com/wiki/Vector_Embedding_of_Wikipedia_Concepts_and_Entities">Vector Embedding of Wikipedia Concepts and Entities</a>&amp;quot;. Springer, Cham. DOI: 10.1007/978-3-319-59569-6_50. <br />
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<div>{{Infobox work<br />
| title = A Wikipedia Wizard and Blogger<br />
| date = 2007<br />
| authors = [[Tiago Villanueva]]<br />
| doi = 10.1136/bmj.335.7615.s66<br />
| link = https://www.bmj.com/content/335/7615/s66<br />
}}<br />
'''A Wikipedia Wizard and Blogger''' - scientific work related to [[Wikipedia quality]] published in 2007, written by [[Tiago Villanueva]].<br />
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== Overview ==<br />
Bertalan Mesko combines being an editor and administrator of [[Wikipedia]] with medical student studies</div>Camilahttps://wikipediaquality.com/index.php?title=An_Aesthetic_for_Deliberating_Online:_Thinking_Through_%E2%80%9CUniversal_Pragmatics%E2%80%9D_and_%E2%80%9CDialogism%E2%80%9D_with_Reference_to_Wikipedia&diff=25398An Aesthetic for Deliberating Online: Thinking Through “Universal Pragmatics” and “Dialogism” with Reference to Wikipedia2020-09-22T19:09:52Z<p>Camila: + Embed</p>
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<div>{{Infobox work<br />
| title = An Aesthetic for Deliberating Online: Thinking Through “Universal Pragmatics” and “Dialogism” with Reference to Wikipedia<br />
| date = 2012<br />
| authors = [[Nicholas Cimini]]<br />[[Jennifer Burr]]<br />
| doi = 10.1080/01972243.2012.669448<br />
| link = http://www.tandfonline.com/doi/abs/10.1080/01972243.2012.669448?queryID=%24%7BresultBean.queryID%7D<br />
}}<br />
'''An Aesthetic for Deliberating Online: Thinking Through “Universal Pragmatics” and “Dialogism” with Reference to Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Nicholas Cimini]] and [[Jennifer Burr]].<br />
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== Overview ==<br />
In this article authors examine contributions to [[Wikipedia]] through the prism of two divergent critical theorists: Jurgen Habermas and Mikhail Bakhtin. Authors show that, in slightly dissimilar ways, these theorists came to consider an “aesthetic for democracy” Hirschkop 1999 or template for deliberative relationships that privileges relatively free and unconstrained dialogue to which every speaker has equal access and without authoritative closure. Authors employ Habermas's theory of “universal pragmatics” and Bakhtin's “dialogism” for analyses of contributions on Wikipedia for its entry on stem cells and transhumanism and show that the decision to embrace either unified or pluralistic forms of deliberation is an empirical matter to be judged in sociohistorical context, as opposed to what normative theories insist on. Authors conclude by stressing the need to be attuned to the complexity and ambiguity of deliberative relations online.<br />
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Cimini, Nicholas; Burr, Jennifer. (2012). "[[An Aesthetic for Deliberating Online: Thinking Through “Universal Pragmatics” and “Dialogism” with Reference to Wikipedia]]". Taylor & Francis Group. DOI: 10.1080/01972243.2012.669448. <br />
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{{cite journal |last1=Cimini |first1=Nicholas |last2=Burr |first2=Jennifer |title=An Aesthetic for Deliberating Online: Thinking Through “Universal Pragmatics” and “Dialogism” with Reference to Wikipedia |date=2012 |doi=10.1080/01972243.2012.669448 |url=https://wikipediaquality.com/wiki/An_Aesthetic_for_Deliberating_Online:_Thinking_Through_“Universal_Pragmatics”_and_“Dialogism”_with_Reference_to_Wikipedia |journal=Taylor & Francis Group}}<br />
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Cimini, Nicholas; Burr, Jennifer. (2012). &amp;quot;<a href="https://wikipediaquality.com/wiki/An_Aesthetic_for_Deliberating_Online:_Thinking_Through_“Universal_Pragmatics”_and_“Dialogism”_with_Reference_to_Wikipedia">An Aesthetic for Deliberating Online: Thinking Through “Universal Pragmatics” and “Dialogism” with Reference to Wikipedia</a>&amp;quot;. Taylor & Francis Group. DOI: 10.1080/01972243.2012.669448. <br />
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<div>{{Infobox work<br />
| title = Wikiparq: a Tabulated Wikipedia Resource Using the Parquet Format<br />
| date = 2016<br />
| authors = [[Marcus Klang]]<br />[[Pierre Nugues]]<br />
| link = http://portal.research.lu.se/portal/files/7671984/31_Paper_3.pdf<br />
}}<br />
'''Wikiparq: a Tabulated Wikipedia Resource Using the Parquet Format''' - scientific work related to [[Wikipedia quality]] published in 2016, written by [[Marcus Klang]] and [[Pierre Nugues]].<br />
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== Overview ==<br />
Wikipedia has become one of the most popular resources in [[natural language processing]] and it is used in quantities of applications. However, [[Wikipedia]] requires a substantial pre-processing step before it can be used. For instance, its set of nonstandardized annotations, referred to as the wiki markup, is language-dependent and needs specific parsers from language to language, for English, French, Italian, etc. In addition, the intricacies of the different Wikipedia resources: main article text, [[categories]], wikidata, [[infoboxes]], scattered into the article document or in different files make it difficult to have global view of this outstanding resource. In this paper, authors describe WikiParq, a unified format based on the Parquet standard to tabulate and package the Wikipedia corpora. In combination with Spark, a map-reduce computing framework, and the SQL query language, WikiParq makes it much easier to write database queries to extract specific information or subcorpora from Wikipedia, such as all the first paragraphs of the articles in French, or all the articles on persons in Spanish, or all the articles on persons that have versions in French, English, and Spanish. WikiParq is available in six [[language versions]] and is potentially extendible to all the languages of Wikipedia. The WikiParq files are downloadable as tarball archives from this location: http://semantica.cs.lth.se/wikiparq/. (Less)<br />
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Klang, Marcus; Nugues, Pierre. (2016). "[[Wikiparq: a Tabulated Wikipedia Resource Using the Parquet Format]]". European Language Resources Association (ELRA). <br />
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{{cite journal |last1=Klang |first1=Marcus |last2=Nugues |first2=Pierre |title=Wikiparq: a Tabulated Wikipedia Resource Using the Parquet Format |date=2016 |url=https://wikipediaquality.com/wiki/Wikiparq:_a_Tabulated_Wikipedia_Resource_Using_the_Parquet_Format |journal=European Language Resources Association (ELRA)}}<br />
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Klang, Marcus; Nugues, Pierre. (2016). &amp;quot;<a href="https://wikipediaquality.com/wiki/Wikiparq:_a_Tabulated_Wikipedia_Resource_Using_the_Parquet_Format">Wikiparq: a Tabulated Wikipedia Resource Using the Parquet Format</a>&amp;quot;. European Language Resources Association (ELRA). <br />
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</code></div>Camilahttps://wikipediaquality.com/index.php?title=Major_Barbara_on_Chinese_Wikipedia_and_in_Microblogs&diff=25396Major Barbara on Chinese Wikipedia and in Microblogs2020-09-22T19:05:08Z<p>Camila: + cat.</p>
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<div>{{Infobox work<br />
| title = Major Barbara on Chinese Wikipedia and in Microblogs<br />
| date = 2016<br />
| authors = [[Kay Li]]<br />
| doi = 10.1007/978-3-319-41003-6_9<br />
| link = https://link.springer.com/content/pdf/10.1007%2F978-3-319-41003-6_9.pdf<br />
}}<br />
'''Major Barbara on Chinese Wikipedia and in Microblogs''' - scientific work related to [[Wikipedia quality]] published in 2016, written by [[Kay Li]].<br />
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== Overview ==<br />
Unforeseen by their authors, literary works acquire their own contemporaries and are transposed into new media: film, [[Wikipedia]], and microblogs. The next generation of Shaw readers in China are not theatergoers, nor filmgoers, but youths surfing the Internet. China is negotiating between a respect for its long cultural history and the cultural challenges accompanying its intense, rapid economic development. This chapter examines how Major Barbara is highlighted in current Chinese social media, via microblogs and Wikipedias, to see how Shaw’s works are read by the younger generation in China selectively, according to contextual circumstances.<br />
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Li, Kay. (2016). "[[Major Barbara on Chinese Wikipedia and in Microblogs]]". Palgrave Macmillan, Cham. DOI: 10.1007/978-3-319-41003-6_9. <br />
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{{cite journal |last1=Li |first1=Kay |title=Major Barbara on Chinese Wikipedia and in Microblogs |date=2016 |doi=10.1007/978-3-319-41003-6_9 |url=https://wikipediaquality.com/wiki/Major_Barbara_on_Chinese_Wikipedia_and_in_Microblogs |journal=Palgrave Macmillan, Cham}}<br />
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Li, Kay. (2016). &amp;quot;<a href="https://wikipediaquality.com/wiki/Major_Barbara_on_Chinese_Wikipedia_and_in_Microblogs">Major Barbara on Chinese Wikipedia and in Microblogs</a>&amp;quot;. Palgrave Macmillan, Cham. DOI: 10.1007/978-3-319-41003-6_9. <br />
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[[Category:Scientific works]]<br />
[[Category:Chinese Wikipedia]]</div>Camilahttps://wikipediaquality.com/index.php?title=Controversy_Goes_Online_:_Schizophrenia_Genetics_on_Wikipedia&diff=25395Controversy Goes Online : Schizophrenia Genetics on Wikipedia2020-09-22T19:03:34Z<p>Camila: Int.links</p>
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<div>'''Controversy Goes Online : Schizophrenia Genetics on Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2016, written by [[Sally Wyatt]], [[Anna Harris]] and [[Susan E. Kelly]].<br />
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== Overview ==<br />
Scientific controversy is increasingly played out via the internet, a technology that is simultaneously content, medium and research infrastructure. Here authors analyse material from [[Wikipedia]], focusing on schizophrenia genetics. Authors find that citation and curation of scientific resources follow a negotiated, ad hoc adherence to Wikipedia rules, are based on limited access to scientific literature, and thus lead to a partially constructed ‘review’ of the science that excludes non-professionals. Given its policies and systems for developing neutral, evidence-based articles, one would not expect to find controversy on Wikipedia, yet authors find traces. Scientific ambiguity about schizophrenia genetics lends itself to multiple ways of curating resources, and the infrastructure of online spaces enables the practices behind curation work to become visible in new ways. Authors argue that not only does Wikipedia make scientific controversy visible to a wider range of people, it is also involved in the production of knowledge.</div>Camilahttps://wikipediaquality.com/index.php?title=Document_Controversy_Classification_based_on_the_Wikipedia_Category_Structure&diff=25394Document Controversy Classification based on the Wikipedia Category Structure2020-09-22T19:00:34Z<p>Camila: Document Controversy Classification based on the Wikipedia Category Structure - new page</p>
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<div>'''Document Controversy Classification based on the Wikipedia Category Structure''' - scientific work related to Wikipedia quality published in 2015, written by Micha l Jankowski-Lorek and Kazimierz Zieliński.<br />
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== Overview ==<br />
Dispute and controversy are parts of culture and cannot be omitted on the Internet (where it becomes more anonymous). There have been many studies on controversy, especially on social networks such as Wikipedia. This free on-line encyclopedia has become a very popular data source among many researchers studying behavior or natural language processing. This paper presents using the category structure of Wikipedia to determine the controversy of a single article. This is the first part of the proposed system for classification of topic controversy score for any given text.</div>Camilahttps://wikipediaquality.com/index.php?title=Challenges_of_Mathematical_Information_Retrievalin_the_Ntcir-11_Math_Wikipedia_Task&diff=25393Challenges of Mathematical Information Retrievalin the Ntcir-11 Math Wikipedia Task2020-09-22T18:58:30Z<p>Camila: Embed</p>
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<div>{{Infobox work<br />
| title = Challenges of Mathematical Information Retrievalin the Ntcir-11 Math Wikipedia Task<br />
| date = 2015<br />
| authors = [[Moritz Schubotz]]<br />[[Abdou Youssef]]<br />[[Volker Markl]]<br />[[Howard S. Cohl]]<br />
| doi = 10.1145/2766462.2767787<br />
| link = http://dl.acm.org/citation.cfm?doid=2766462.2767787<br />
}}<br />
'''Challenges of Mathematical Information Retrievalin the Ntcir-11 Math Wikipedia Task''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Moritz Schubotz]], [[Abdou Youssef]], [[Volker Markl]] and [[Howard S. Cohl]].<br />
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== Overview ==<br />
Mathematical Information Retrieval concerns retrieving information related to a particular mathematical concept. The NTCIR-11 Math Task develops an evaluation test collection for document sections retrieval of scientific articles based on human generated topics. Those topics involve a combination of formula patterns and keywords. In addition, the optional [[Wikipedia]] Task provides a test collection for retrieval of individual mathematical formula from Wikipedia based on search topics that contain exactly one formula pattern. Authors developed a framework for automatic query generation and immediate evaluation. This paper discusses dataset preparation, topic generation and evaluation methods, and summarizes the results of the participants, with a special focus on the Wikipedia Task.<br />
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Schubotz, Moritz; Youssef, Abdou; Markl, Volker; Cohl, Howard S.. (2015). "[[Challenges of Mathematical Information Retrievalin the Ntcir-11 Math Wikipedia Task]]".DOI: 10.1145/2766462.2767787. <br />
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{{cite journal |last1=Schubotz |first1=Moritz |last2=Youssef |first2=Abdou |last3=Markl |first3=Volker |last4=Cohl |first4=Howard S. |title=Challenges of Mathematical Information Retrievalin the Ntcir-11 Math Wikipedia Task |date=2015 |doi=10.1145/2766462.2767787 |url=https://wikipediaquality.com/wiki/Challenges_of_Mathematical_Information_Retrievalin_the_Ntcir-11_Math_Wikipedia_Task}}<br />
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Schubotz, Moritz; Youssef, Abdou; Markl, Volker; Cohl, Howard S.. (2015). &amp;quot;<a href="https://wikipediaquality.com/wiki/Challenges_of_Mathematical_Information_Retrievalin_the_Ntcir-11_Math_Wikipedia_Task">Challenges of Mathematical Information Retrievalin the Ntcir-11 Math Wikipedia Task</a>&amp;quot;.DOI: 10.1145/2766462.2767787. <br />
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<div>'''Wikilit: Collecting the Wiki and Wikipedia Literature''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Phoebe Ayers]] and [[Reid Priedhorsky]].<br />
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== Overview ==<br />
This workshop has three key goals. First, authors will examine existing and proposed systems for collecting and analyzing the research literature about wikis. Second, authors will discuss the challenges in building such a system and will engage participants to design a sustainable collaborative system to achieve this goal. Finally, authors will provide a forum to build upon ongoing wiki community discussions about problems and opportunities in finding and sharing the wiki research literature.</div>Camilahttps://wikipediaquality.com/index.php?title=Bitcoin_Meets_Google_Trends_and_Wikipedia_:_Quantifying_the_Relationship_Between_Phenomena_of_the_Internet_Era&diff=24037Bitcoin Meets Google Trends and Wikipedia : Quantifying the Relationship Between Phenomena of the Internet Era2020-05-08T10:31:36Z<p>Camila: wikilinks</p>
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<div>'''Bitcoin Meets Google Trends and Wikipedia : Quantifying the Relationship Between Phenomena of the Internet Era''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Ladislav Kristoufek]].<br />
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== Overview ==<br />
Digital currencies have emerged as a new fascinating phenomenon in the financial markets. Recent events on the most popular of the digital currencies – BitCoin – have risen crucial questions about behavior of its exchange rates and they offer a field to study dynamics of the market which consists practically only of speculative traders with no fundamentalists as there is no fundamental value to the currency. In the paper, authors connect two phenomena of the latest years – digital currencies, namely BitCoin, and search queries on [[Google]] Trends and [[Wikipedia]] – and study their relationship. Authors show that not only are the search queries and the prices connected but there also exists a pronounced asymmetry between the effect of an increased interest in the currency while being above or below its trend value.</div>Camilahttps://wikipediaquality.com/index.php?title=Discovering_Missing_Wikipedia_Inter-Language_Links_by_Means_of_Cross-Lingual_Word_Sense_Disambiguation&diff=24036Discovering Missing Wikipedia Inter-Language Links by Means of Cross-Lingual Word Sense Disambiguation2020-05-08T10:30:20Z<p>Camila: cats.</p>
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<div>{{Infobox work<br />
| title = Discovering Missing Wikipedia Inter-Language Links by Means of Cross-Lingual Word Sense Disambiguation<br />
| date = 2012<br />
| authors = [[Els Lefever]]<br />[[Veronique Hoste]]<br />[[Martine De Cock]]<br />
| link = http://www.lrec-conf.org/proceedings/lrec2012/pdf/508_Paper.pdf<br />
}}<br />
'''Discovering Missing Wikipedia Inter-Language Links by Means of Cross-Lingual Word Sense Disambiguation''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Els Lefever]], [[Veronique Hoste]] and [[Martine De Cock]].<br />
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== Overview ==<br />
Wikipedia is a very popular online [[multilingual]] encyclopedia that contains millions of articles covering most written languages. [[Wikipedia]] pages contain monolingual hypertext links to other pages, as well as inter-language links to the corresponding pages in other languages. These inter-language links, however, are not always complete.<br />
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Lefever, Els; Hoste, Veronique; Cock, Martine De. (2012). "[[Discovering Missing Wikipedia Inter-Language Links by Means of Cross-Lingual Word Sense Disambiguation]]". European Language Resources Association (ELRA). <br />
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{{cite journal |last1=Lefever |first1=Els |last2=Hoste |first2=Veronique |last3=Cock |first3=Martine De |title=Discovering Missing Wikipedia Inter-Language Links by Means of Cross-Lingual Word Sense Disambiguation |date=2012 |url=https://wikipediaquality.com/wiki/Discovering_Missing_Wikipedia_Inter-Language_Links_by_Means_of_Cross-Lingual_Word_Sense_Disambiguation |journal=European Language Resources Association (ELRA)}}<br />
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Lefever, Els; Hoste, Veronique; Cock, Martine De. (2012). &amp;quot;<a href="https://wikipediaquality.com/wiki/Discovering_Missing_Wikipedia_Inter-Language_Links_by_Means_of_Cross-Lingual_Word_Sense_Disambiguation">Discovering Missing Wikipedia Inter-Language Links by Means of Cross-Lingual Word Sense Disambiguation</a>&amp;quot;. European Language Resources Association (ELRA). <br />
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[[Category:Scientific works]]</div>Camilahttps://wikipediaquality.com/index.php?title=Effective_Ontology_Learning_:_Concepts%27_Hierarchy_Building_Using_Plain_Text_Wikipedia&diff=24035Effective Ontology Learning : Concepts' Hierarchy Building Using Plain Text Wikipedia2020-05-08T10:28:55Z<p>Camila: + wikilinks</p>
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<div>'''Effective Ontology Learning : Concepts' Hierarchy Building Using Plain Text Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Khalida Bensidi Ahmed]] and [[Adil Toumouh]].<br />
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== Overview ==<br />
Ontologies stand in the heart of the Semantic Web. Nevertheless, heavyweight or formal ontologies’ engineering is being commonly judged to be a tough exercise which requires time and heavy costs. Ontology Learning is thus a solution for this exigency and an approach for the ‘knowledge acquisition bottleneck’. Since texts are massively available everywhere, making up of experts’ knowledge and their know-how, it is of great value to capture the knowledge existing within such texts. Authors approach is thus an interesting research work which tries to answer the challenge of creating concepts’ hierarchies from textual data. The significance of such a solution stems from the idea by which authors take advantage of the [[Wikipedia]] encyclopedia to achieve some good quality results.</div>Camilahttps://wikipediaquality.com/index.php?title=Vandalism_Detection_in_Wikipedia:_a_Bag-Of-Words_Classifier_Approach&diff=24034Vandalism Detection in Wikipedia: a Bag-Of-Words Classifier Approach2020-05-08T10:26:47Z<p>Camila: Infobox work</p>
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<div>{{Infobox work<br />
| title = Vandalism Detection in Wikipedia: a Bag-Of-Words Classifier Approach<br />
| date = 2010<br />
| authors = [[Amit Belani]]<br />
| link = https://seer.ufmg.br/index.php/jidm/article/download/140/96<br />
| plink = https://arxiv.org/abs/1001.0700<br />
}}<br />
'''Vandalism Detection in Wikipedia: a Bag-Of-Words Classifier Approach''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Amit Belani]].<br />
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== Overview ==<br />
A bag-of-words based probabilistic classifier is trained using regularized logistic regression to detect vandalism in the [[English Wikipedia]]. Isotonic regression is used to calibrate the class membership probabilities. Learning curve, [[reliability]], ROC, and cost analysis are performed.</div>Camilahttps://wikipediaquality.com/index.php?title=Coreference_in_Wikipedia:_Main_Concept_Resolution&diff=24033Coreference in Wikipedia: Main Concept Resolution2020-05-08T10:23:58Z<p>Camila: Embed</p>
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<div>{{Infobox work<br />
| title = Coreference in Wikipedia: Main Concept Resolution<br />
| date = 2016<br />
| authors = [[Abbas Ghaddar]]<br />[[Phillippe Langlais]]<br />
| doi = 10.18653/v1/K16-1023<br />
| link = http://aclweb.org/anthology/K16-1023<br />
}}<br />
'''Coreference in Wikipedia: Main Concept Resolution''' - scientific work related to [[Wikipedia quality]] published in 2016, written by [[Abbas Ghaddar]] and [[Phillippe Langlais]].<br />
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== Overview ==<br />
Wikipedia is a resource of choice exploited in many NLP applications, yet authors are not aware of recent attempts to adapt coreference resolution to this resource. In this work, authors revisit a seldom studied task which consists in identifying in a [[Wikipedia]] article all the mentions of the main concept being described. Authors show that by exploiting the Wikipedia markup of a document, as well as links to external knowledge bases such as Freebase, authors can acquire useful information on entities that helps to classify mentions as coreferent or not. Authors designed a classifier which drastically outperforms fair baselines built on top of state-of-the-art coreference resolution systems. Authors also measure the benefits of this classifier in a full coreference resolution pipeline applied to Wikipedia texts.<br />
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{{cite journal |last1=Ghaddar |first1=Abbas |last2=Langlais |first2=Phillippe |title=Coreference in Wikipedia: Main Concept Resolution |date=2016 |doi=10.18653/v1/K16-1023 |url=https://wikipediaquality.com/wiki/Coreference_in_Wikipedia:_Main_Concept_Resolution}}<br />
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Ghaddar, Abbas; Langlais, Phillippe. (2016). &amp;quot;<a href="https://wikipediaquality.com/wiki/Coreference_in_Wikipedia:_Main_Concept_Resolution">Coreference in Wikipedia: Main Concept Resolution</a>&amp;quot;.DOI: 10.18653/v1/K16-1023. <br />
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</code></div>Camilahttps://wikipediaquality.com/index.php?title=Assessing_Wikipedia-Based_Cross-Language_Retrieval_Models&diff=24032Assessing Wikipedia-Based Cross-Language Retrieval Models2020-05-08T10:21:19Z<p>Camila: + embed code</p>
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<div>{{Infobox work<br />
| title = Assessing Wikipedia-Based Cross-Language Retrieval Models<br />
| date = 2014<br />
| authors = [[Benjamin Roth]]<br />
| link = http://www.diva-portal.org/smash/record.jsf?pid=diva2:887070<br />
| plink = https://arxiv.org/pdf/1401.2258.pdf<br />
}}<br />
'''Assessing Wikipedia-Based Cross-Language Retrieval Models''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Benjamin Roth]].<br />
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== Overview ==<br />
This work compares concept models for cross-language retrieval: First, authors adapt probabilistic Latent Semantic Analysis (pLSA) for [[multilingual]] documents. Experiments with different weighting schemes show that a weighting method favoring documents of similar length in both language sides gives best results. Considering that both monolingual and multilingual Latent Dirichlet Allocation (LDA) behave alike when applied for such documents, authors use a training corpus built on [[Wikipedia]] where all documents are length-normalized and obtain improvements over previously reported scores for LDA. Another focus of work is on model combination. For this end authors include Explicit Semantic Analysis (ESA) in the experiments. Authors observe that ESA is not competitive with LDA in a query based retrieval task on CLEF 2000 data. The combination of [[machine translation]] with concept models increased performance by 21.1% map in comparison to machine translation alone. Machine translation relies on parallel corpora, which may not be available for many language pairs. Authors further explore how much [[cross-lingual]] information can be carried over by a specific information source in Wikipedia, namely linked text. The best results are obtained using a language modeling approach, entirely without information from parallel corpora. The need for smoothing raises interesting questions on soundness and efficiency. Link models capture only a certain kind of information and suggest weighting schemes to emphasize particular words. For a combined model, another interesting question is therefore how to integrate different weighting schemes. Using a very simple combination scheme, authors obtain results that compare favorably to previously reported results on the CLEF 2000 dataset.<br />
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Roth, Benjamin. (2014). "[[Assessing Wikipedia-Based Cross-Language Retrieval Models]]".<br />
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{{cite journal |last1=Roth |first1=Benjamin |title=Assessing Wikipedia-Based Cross-Language Retrieval Models |date=2014 |url=https://wikipediaquality.com/wiki/Assessing_Wikipedia-Based_Cross-Language_Retrieval_Models}}<br />
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Roth, Benjamin. (2014). &amp;quot;<a href="https://wikipediaquality.com/wiki/Assessing_Wikipedia-Based_Cross-Language_Retrieval_Models">Assessing Wikipedia-Based Cross-Language Retrieval Models</a>&amp;quot;.<br />
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</code></div>Camilahttps://wikipediaquality.com/index.php?title=The_Quality_of_Content_in_Open_Online_Collaboration_Platforms:_Approaches_to_Nlp-Supported_Information_Quality_Management_in_Wikipedia&diff=24031The Quality of Content in Open Online Collaboration Platforms: Approaches to Nlp-Supported Information Quality Management in Wikipedia2020-05-08T10:20:17Z<p>Camila: Adding infobox</p>
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<div>{{Infobox work<br />
| title = The Quality of Content in Open Online Collaboration Platforms: Approaches to Nlp-Supported Information Quality Management in Wikipedia<br />
| date = 2014<br />
| authors = [[Oliver Ferschke]]<br />
| link = http://tuprints.ulb.tu-darmstadt.de/4092/<br />
}}<br />
'''The Quality of Content in Open Online Collaboration Platforms: Approaches to Nlp-Supported Information Quality Management in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Oliver Ferschke]].<br />
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== Overview ==<br />
Over the past decade, the paradigm of the World Wide Web has shifted from static web pages towards participatory and collaborative content production. The main properties of this user generated content are a low publication threshold and little or no editorial control. While this has improved the variety and timeliness of the available information, it causes an even higher variance in quality than the already heterogeneous quality of traditional web content. [[Wikipedia]] is the prime example for a successful, large-scale, collaboratively created resource that reflects the spirit of the open collaborative content creation paradigm.</div>Camilahttps://wikipediaquality.com/index.php?title=Wikitology:_a_Novel_Hybrid_Knowledge_Base_Derived_from_Wikipedia&diff=24030Wikitology: a Novel Hybrid Knowledge Base Derived from Wikipedia2020-05-08T10:17:28Z<p>Camila: Infobox work</p>
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<div>{{Infobox work<br />
| title = Wikitology: a Novel Hybrid Knowledge Base Derived from Wikipedia<br />
| date = 2010<br />
| authors = [[Tim Finin]]<br />[[Zareen Syed]]<br />
| link = https://dl.acm.org/citation.cfm?id=2019936<br />
}}<br />
'''Wikitology: a Novel Hybrid Knowledge Base Derived from Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Tim Finin]] and [[Zareen Syed]].<br />
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== Overview ==<br />
World knowledge may be available in different forms such as relational databases, triple stores, link graphs, meta-data and free text. Human minds are capable of understanding and reasoning over knowledge represented in different ways and are influenced by different social, contextual and environmental factors. By following a similar model, authors have integrated a variety of knowledge sources in a novel way to produce a single hybrid knowledge base i.e., Wikitology, enabling applications to better access and exploit knowledge hidden in different forms.</div>Camilahttps://wikipediaquality.com/index.php?title=Research_Guides:_Wikipedia:_Using_Wikipedia&diff=24029Research Guides: Wikipedia: Using Wikipedia2020-05-08T10:14:28Z<p>Camila: Cats.</p>
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<div>{{Infobox work<br />
| title = Research Guides: Wikipedia: Using Wikipedia<br />
| date = 2014<br />
| authors = [[Joel Cohen]]<br />
| link = http://libguides.canisius.edu/wikipedia/use<br />
}}<br />
'''Research Guides: Wikipedia: Using Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Joel Cohen]].<br />
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== Overview ==<br />
This guide will help you to use [[Wikipedia]] in an appropriate way to get background and bibliographic information.<br />
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{{cite journal |last1=Cohen |first1=Joel |title=Research Guides: Wikipedia: Using Wikipedia |date=2014 |url=https://wikipediaquality.com/wiki/Research_Guides:_Wikipedia:_Using_Wikipedia}}<br />
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Cohen, Joel. (2014). &amp;quot;<a href="https://wikipediaquality.com/wiki/Research_Guides:_Wikipedia:_Using_Wikipedia">Research Guides: Wikipedia: Using Wikipedia</a>&amp;quot;.<br />
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[[Category:Scientific works]]</div>Camilahttps://wikipediaquality.com/index.php?title=Extracting_Imperatives_from_Wikipedia_Article_for_Deletion_Discussions&diff=24028Extracting Imperatives from Wikipedia Article for Deletion Discussions2020-05-08T10:12:28Z<p>Camila: + Infobox work</p>
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<div>{{Infobox work<br />
| title = Extracting Imperatives from Wikipedia Article for Deletion Discussions<br />
| date = 2014<br />
| authors = [[Fiona Mao]]<br />[[Robert E. Mercer]]<br />[[Lu Xiao]]<br />
| doi = 10.3115/v1/W14-2117<br />
| link = http://aclweb.org/anthology/W/W14/W14-2117.pdf<br />
}}<br />
'''Extracting Imperatives from Wikipedia Article for Deletion Discussions''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Fiona Mao]], [[Robert E. Mercer]] and [[Lu Xiao]].<br />
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== Overview ==<br />
Wikipedia contains millions of articles, collaboratively produced. If an article is controversial, an online “Article for Deletion” (AfD) discussion is held to determine whether the article should be deleted. It is open to any user to participate and make a comment or argue an opinion. Some of these comments and arguments can be counter-arguments, attacks in Dung’s (1995) argumentation terminology. Here, authors consider the extraction of one type of attack, the directive speech act formed as an imperative.</div>Camilahttps://wikipediaquality.com/index.php?title=%E2%80%98Anyone_Can_Edit%E2%80%99,_Not_Everyone_Does:_Wikipedia%E2%80%99s_Infrastructure_and_the_Gender_Gap&diff=24027‘Anyone Can Edit’, Not Everyone Does: Wikipedia’s Infrastructure and the Gender Gap2020-05-08T10:09:34Z<p>Camila: + embed code</p>
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| title = ‘Anyone Can Edit’, Not Everyone Does: Wikipedia’s Infrastructure and the Gender Gap<br />
| date = 2017<br />
| authors = [[Heather Ford]]<br />[[Judy Wajcman]]<br />
| doi = 10.1177/0306312717692172<br />
| link = http://journals.sagepub.com/doi/full/10.1177/0306312717692172<br />
}}<br />
'''‘Anyone Can Edit’, Not Everyone Does: Wikipedia’s Infrastructure and the Gender Gap''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Heather Ford]] and [[Judy Wajcman]].<br />
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== Overview ==<br />
Feminist STS has long established that science’s provenance as a male domain continues to define what counts as knowledge and expertise. [[Wikipedia]], arguably one of the most powerful sources of information today, was initially lauded as providing the opportunity to rebuild knowledge institutions by providing greater representation of multiple groups. However, less than ten percent of [[Wikipedia editors]] are women. At one level, this imbalance in contributions and therefore content is yet another case of the masculine culture of technoscience. This is an important argument and, in this article, authors examine the empirical research that highlights these issues. Authors main objective, however, is to extend current accounts by demonstrating that Wikipedia’s infrastructure introduces new and less visible sources of gender disparity. In sum, aim here is to present a consolidated analysis of the gendering of Wikipedia.<br />
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Ford, Heather; Wajcman, Judy. (2017). "[[‘Anyone Can Edit’, Not Everyone Does: Wikipedia’s Infrastructure and the Gender Gap]]". SAGE PublicationsSage UK: London, England. DOI: 10.1177/0306312717692172. <br />
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{{cite journal |last1=Ford |first1=Heather |last2=Wajcman |first2=Judy |title=‘Anyone Can Edit’, Not Everyone Does: Wikipedia’s Infrastructure and the Gender Gap |date=2017 |doi=10.1177/0306312717692172 |url=https://wikipediaquality.com/wiki/‘Anyone_Can_Edit’,_Not_Everyone_Does:_Wikipedia’s_Infrastructure_and_the_Gender_Gap |journal=SAGE PublicationsSage UK: London, England}}<br />
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Ford, Heather; Wajcman, Judy. (2017). &amp;quot;<a href="https://wikipediaquality.com/wiki/‘Anyone_Can_Edit’,_Not_Everyone_Does:_Wikipedia’s_Infrastructure_and_the_Gender_Gap">‘Anyone Can Edit’, Not Everyone Does: Wikipedia’s Infrastructure and the Gender Gap</a>&amp;quot;. SAGE PublicationsSage UK: London, England. DOI: 10.1177/0306312717692172. <br />
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</code></div>Camilahttps://wikipediaquality.com/index.php?title=Conversation_Support_System_for_People_with_Language_Disorders_%E2%80%94_Making_Topic_Lists_from_Wikipedia&diff=24026Conversation Support System for People with Language Disorders — Making Topic Lists from Wikipedia2020-05-08T10:07:16Z<p>Camila: Adding wikilinks</p>
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<div>'''Conversation Support System for People with Language Disorders — Making Topic Lists from Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Yasuko Yamane]], [[Hidenobu Ishida]], [[Fumio Hattori]] and [[Kiyoshi Yasuda]].<br />
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== Overview ==<br />
A conversation support system for people with language disorders is proposed. Although the existing conversation support system "Raku-raku Jiyu Kaiwa" (Easy Free Conversation) is effective, it has insufficient topic words and a rigid topic list structure. To solve these problems, this paper proposes a method that makes topic lists from [[Wikipedia]]'s millions of topic words. Experiments using the proposed topic list showed that subject utterances increased and the variety of spoken topics was expanded.</div>Camilahttps://wikipediaquality.com/index.php?title=Radial_History_Flow:_Direct_Visualization_of_Author_Dynamics_in_Wikipedia&diff=24025Radial History Flow: Direct Visualization of Author Dynamics in Wikipedia2020-05-08T10:05:12Z<p>Camila: Category</p>
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<div>{{Infobox work<br />
| title = Radial History Flow: Direct Visualization of Author Dynamics in Wikipedia<br />
| date = 2015<br />
| authors = [[Taeil Jeon]]<br />[[Jihyun Lee]]<br />[[Won Ho Lee]]<br />[[Wonjong Rhee]]<br />[[Bongwon Suh]]<br />
| link = https://s3-eu-west-1.amazonaws.com/pfigshare-u-files/2600384/3_EuroVis_radial.pdf<br />
}}<br />
'''Radial History Flow: Direct Visualization of Author Dynamics in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Taeil Jeon]], [[Jihyun Lee]], [[Won Ho Lee]], [[Wonjong Rhee]] and [[Bongwon Suh]].<br />
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== Overview ==<br />
In this paper, authors propose Radial History Flow, which enhances History Flow by enabling comparison of author dynamics shown in the revision histories of [[Wikipedia]] articles. The key objective of this study is to unravel the complex relationships among the authors by linking two authors when one has a history of revising the other’s contribution on the article. Using the revision history dataset of the Wikipedia article on ‘electric energy’, authors perform a case study and demonstrate the feasibility of Radial History Flow.<br />
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Jeon, Taeil; Lee, Jihyun; Lee, Won Ho; Rhee, Wonjong; Suh, Bongwon. (2015). "[[Radial History Flow: Direct Visualization of Author Dynamics in Wikipedia]]".<br />
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{{cite journal |last1=Jeon |first1=Taeil |last2=Lee |first2=Jihyun |last3=Lee |first3=Won Ho |last4=Rhee |first4=Wonjong |last5=Suh |first5=Bongwon |title=Radial History Flow: Direct Visualization of Author Dynamics in Wikipedia |date=2015 |url=https://wikipediaquality.com/wiki/Radial_History_Flow:_Direct_Visualization_of_Author_Dynamics_in_Wikipedia}}<br />
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Jeon, Taeil; Lee, Jihyun; Lee, Won Ho; Rhee, Wonjong; Suh, Bongwon. (2015). &amp;quot;<a href="https://wikipediaquality.com/wiki/Radial_History_Flow:_Direct_Visualization_of_Author_Dynamics_in_Wikipedia">Radial History Flow: Direct Visualization of Author Dynamics in Wikipedia</a>&amp;quot;.<br />
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[[Category:Scientific works]]</div>Camilahttps://wikipediaquality.com/index.php?title=Using_Dynamic_Markov_Compression_to_Detect_Vandalism_in_the_Wikipedia&diff=24024Using Dynamic Markov Compression to Detect Vandalism in the Wikipedia2020-05-08T10:04:03Z<p>Camila: + Infobox work</p>
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<div>{{Infobox work<br />
| title = Using Dynamic Markov Compression to Detect Vandalism in the Wikipedia<br />
| date = 2009<br />
| authors = [[Kelly Y. Itakura]]<br />[[Charles L. A. Clarke]]<br />
| doi = 10.1145/1571941.1572146<br />
| link = https://dl.acm.org/citation.cfm?id=1571941.1572146<br />
}}<br />
'''Using Dynamic Markov Compression to Detect Vandalism in the Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2009, written by [[Kelly Y. Itakura]] and [[Charles L. A. Clarke]].<br />
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== Overview ==<br />
Authors apply the Dynamic Markov Compression model to detect spam edits in the [[Wikipedia]]. The method appears to outperform previous efforts based on compression models, providing performance comparable to methods based on manually constructed rules.</div>Camilahttps://wikipediaquality.com/index.php?title=Mining_Translation_Pairs_with_Learnt_Patterns_from_Wikipedia&diff=24023Mining Translation Pairs with Learnt Patterns from Wikipedia2020-05-08T10:01:47Z<p>Camila: Adding infobox</p>
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<div>{{Infobox work<br />
| title = Mining Translation Pairs with Learnt Patterns from Wikipedia<br />
| date = 2015<br />
| authors = [[Duan Jianyon]]<br />
| link = http://en.cnki.com.cn/Article_en/CJFDTOTAL-MESS201502025.htm<br />
}}<br />
'''Mining Translation Pairs with Learnt Patterns from Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Duan Jianyon]].<br />
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== Overview ==<br />
Bilingual translation pairs play an import role in many NLP applications,such as cross language [[information retrieval]] and [[machine translation]].The translation of proper names,out of vocabulary words,idioms and technical terminologies is one of the key factors that affect the performance of the systems.However,these translations can hardly be found in the traditional bilingual dictionary.This paper proposes a new method to automatically extract high quality translation pairs from [[Wikipedia]] based on the wide area coverage and data structure,the method not only can learn common patterns,but also learn many patterns that can hardly be found by human beings.The method contains three steps:1)extract translation pairs from the language toolbox of the Wikipedia.They can be heuristic for the next step;2)learn patterns of translation pairs with the knowledge of PAT-Array gained from the previous work;3)extract other translation pairs automatically using the learned patterns.Authors experimental results show the accuracy can reach 90.4%.</div>Camilahttps://wikipediaquality.com/index.php?title=With_a_Little_Help_from_My_Neighbors:_Person_Name_Linking_Using_the_Wikipedia_Social_Network&diff=24022With a Little Help from My Neighbors: Person Name Linking Using the Wikipedia Social Network2020-05-08T10:00:10Z<p>Camila: Basic information on With a Little Help from My Neighbors: Person Name Linking Using the Wikipedia Social Network</p>
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<div>'''With a Little Help from My Neighbors: Person Name Linking Using the Wikipedia Social Network''' - scientific work related to Wikipedia quality published in 2016, written by Johanna Geiß and Michael Gertz.<br />
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== Overview ==<br />
Driven by the popularity of social networks, there has been an increasing interest in employing such networks in the context of named entity linking. In this paper, authors present a novel approach to person name disambiguation and linking that uses a large-scale social network extracted from the English Wikipedia. First, possible candidate matches for an ambiguous person name are determined. With each candidate match, a network substructure is associated. Based on the similarity between these network substructures and the latent network of an ambiguous person name in a document, authors propose an efficient ranking method to resolve the ambiguity. Authors demonstrate the effectiveness of approach, resulting in an overall precision of over 96% for disambiguating person names and linking them to real world entities.</div>Camilahttps://wikipediaquality.com/index.php?title=Wigipedia:_Visual_Editing_of_Semantic_Data_in_Wikipedia&diff=24021Wigipedia: Visual Editing of Semantic Data in Wikipedia2020-05-08T09:57:11Z<p>Camila: + Infobox work</p>
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<div>{{Infobox work<br />
| title = Wigipedia: Visual Editing of Semantic Data in Wikipedia<br />
| date = 2011<br />
| authors = [[Svetlin BostandjievJohn]]<br />[[Gretarsson Christopher]]<br />
| link = http://ceur-ws.org/Vol-694/paper3.pdf<br />
| plink = https://www.researchgate.net/profile/Tobias_Hoellerer/publication/267411996_WiGiPedia_Visual_Editing_of_Semantic_Data_in_Wikipedia/links/55eab90908aeb6516265ecbf.pdf<br />
}}<br />
'''Wigipedia: Visual Editing of Semantic Data in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Svetlin BostandjievJohn]] and [[Gretarsson Christopher]].<br />
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== Overview ==<br />
Wikipedia is emerging as the dominant global knowledge repos-itory. Recently, large numbers of [[Wikipedia]] users have col-laborated to produce more [[structured information]] in the on-line encyclopedia. For example, the information found in ta-bles, [[categories]] and [[infoboxes]]. Infoboxes contain key-valuepairs, manually appended to articles based on the unstructuredtext therein. The wiki contains some structured informationwhich can be crawled by [[DBpedia]] [2], which attempts to orga-nize wiki data into into a database of subject-predicate-objecttriples. By leveraging this data authors generate an interface, whichwe call WiGipedia, embedded on every Wikipedia article asan interactive graph visualization where entities represent ar-ticles, categories and relational entities, with typed edges be-tween them. This intelligent web interface is designed to sim-plify the elicitation of semantically structured information inWikipedia (Figure 1). Authors motivation is to both inform theuser of interesting contextual information pertaining to the cur-rent article, and to provide a simple way to introduce and/orrepair semantic relations between wiki articles. User actionsresult in improved accuracy and consistency of structured dataspread across multiple articles. WiGipedia provides users withan intuitive interface that allows single-click Wikipedia editswithout knowledge of the Wikipedia markup language, tem-plates, etc. An online demo of the interface can be found atwww.wigipedia-online.com.</div>Camilahttps://wikipediaquality.com/index.php?title=Extracting_Structured_Information_from_Wikipedia_Articles_to_Populate_Infoboxes&diff=24020Extracting Structured Information from Wikipedia Articles to Populate Infoboxes2020-05-08T09:55:06Z<p>Camila: + cat.</p>
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<div>{{Infobox work<br />
| title = Extracting Structured Information from Wikipedia Articles to Populate Infoboxes<br />
| date = 2010<br />
| authors = [[Dustin Lange]]<br />[[Christoph Böhm]]<br />[[Felix Naumann]]<br />
| doi = 10.1145/1871437.1871698<br />
| link = http://dl.acm.org/citation.cfm?doid=1871437.1871698<br />
| plink = https://www.semanticscholar.org/paper/Extracting-structured-information-from-Wikipedia-to-Lange-B%C3%B6hm/295bc470c79b43c07ae50a427e6a4a92041e85d3/figure/4<br />
}}<br />
'''Extracting Structured Information from Wikipedia Articles to Populate Infoboxes''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Dustin Lange]], [[Christoph Böhm]] and [[Felix Naumann]].<br />
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== Overview ==<br />
Roughly every third [[Wikipedia]] article contains an infobox - a table that displays important facts about the subject in attribute-value form. The schema of an infobox, i.e., the attributes that can be expressed for a concept, is defined by an infobox template. Often, authors do not specify all template attributes, resulting in incomplete [[infoboxes]]. With iPopulator, authors introduce a system that automatically populates infoboxes of Wikipedia articles by extracting attribute values from the article's text. In contrast to prior work, iPopulator detects and exploits the structure of attribute values to independently extract value parts. Authors have tested iPopulator on the entire set of infobox templates and provide a detailed analysis of its effectiveness. For instance, authors achieve an average extraction precision of 91% for 1,727 distinct infobox template attributes.<br />
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Lange, Dustin; Böhm, Christoph; Naumann, Felix. (2010). "[[Extracting Structured Information from Wikipedia Articles to Populate Infoboxes]]".DOI: 10.1145/1871437.1871698. <br />
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{{cite journal |last1=Lange |first1=Dustin |last2=Böhm |first2=Christoph |last3=Naumann |first3=Felix |title=Extracting Structured Information from Wikipedia Articles to Populate Infoboxes |date=2010 |doi=10.1145/1871437.1871698 |url=https://wikipediaquality.com/wiki/Extracting_Structured_Information_from_Wikipedia_Articles_to_Populate_Infoboxes}}<br />
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Lange, Dustin; Böhm, Christoph; Naumann, Felix. (2010). &amp;quot;<a href="https://wikipediaquality.com/wiki/Extracting_Structured_Information_from_Wikipedia_Articles_to_Populate_Infoboxes">Extracting Structured Information from Wikipedia Articles to Populate Infoboxes</a>&amp;quot;.DOI: 10.1145/1871437.1871698. <br />
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[[Category:Scientific works]]</div>Camilahttps://wikipediaquality.com/index.php?title=Readers_are_Not_Free-Riders:_Reading_as_a_Form_of_Participation_on_Wikipedia&diff=24019Readers are Not Free-Riders: Reading as a Form of Participation on Wikipedia2020-05-08T09:52:12Z<p>Camila: + cat.</p>
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<div>{{Infobox work<br />
| title = Readers are Not Free-Riders: Reading as a Form of Participation on Wikipedia<br />
| date = 2010<br />
| authors = [[Judd Antin]]<br />[[Coye Cheshire]]<br />
| doi = 10.1145/1718918.1718942<br />
| link = http://dl.acm.org/citation.cfm?id=1718942<br />
| plink = https://www.researchgate.net/profile/Coye_Cheshire/publication/220878713_Readers_are_not_free-riders_Reading_as_a_form_of_participation_on_Wikipedia/links/0c96051a8e72b88e31000000.pdf<br />
}}<br />
'''Readers are Not Free-Riders: Reading as a Form of Participation on Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Judd Antin]] and [[Coye Cheshire]].<br />
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== Overview ==<br />
The success of [[Wikipedia]] as a large-scale collaborative effort has spurred researchers to examine the motivations and behaviors of Wikipedia's participants. However, this research has tended to focus on active involvement rather than more common forms of participation such as reading. In this paper authors argue that Wikipedia's readers should not all be characterized as free-riders -- individuals who knowingly choose to take advantage of others' effort. Furthermore, authors illustrate how readers provide a valuable service to Wikipedia. Finally, authors use the notion of legitimate peripheral participation to argue that reading is a gateway activity through which newcomers learn about Wikipedia. Authors find support for arguments in the results of a survey of Wikipedia usage and knowledge. Implications for future research and design are discussed.<br />
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{{cite journal |last1=Antin |first1=Judd |last2=Cheshire |first2=Coye |title=Readers are Not Free-Riders: Reading as a Form of Participation on Wikipedia |date=2010 |doi=10.1145/1718918.1718942 |url=https://wikipediaquality.com/wiki/Readers_are_Not_Free-Riders:_Reading_as_a_Form_of_Participation_on_Wikipedia}}<br />
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Antin, Judd; Cheshire, Coye. (2010). &amp;quot;<a href="https://wikipediaquality.com/wiki/Readers_are_Not_Free-Riders:_Reading_as_a_Form_of_Participation_on_Wikipedia">Readers are Not Free-Riders: Reading as a Form of Participation on Wikipedia</a>&amp;quot;.DOI: 10.1145/1718918.1718942. <br />
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[[Category:Scientific works]]</div>Camilahttps://wikipediaquality.com/index.php?title=A_Comparison_of_Approaches_for_Geospatial_Entity_Extraction_from_Wikipedia&diff=24018A Comparison of Approaches for Geospatial Entity Extraction from Wikipedia2020-05-08T09:50:43Z<p>Camila: Starting an article - A Comparison of Approaches for Geospatial Entity Extraction from Wikipedia</p>
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<div>'''A Comparison of Approaches for Geospatial Entity Extraction from Wikipedia''' - scientific work related to Wikipedia quality published in 2010, written by Daryl Woodward, Jeremy Witmer and Jugal K. Kalita.<br />
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== Overview ==<br />
Authors target in this paper the challenge of extracting geospatial data from the article text of the English Wikipedia. Authors present the results of a Hidden Markov Model (HMM) based approach to identify location-related named entities in the corpus of Wikipedia articles, which are primarily about battles and wars due to their high geospatial content. The HMM NER process drives a geocoding and resolution process, whose goal is to determine the correct coordinates for each place name (often referred to as grounding). Authors compare results to a previously developed data structure and algorithm for disambiguating place names that can have multiple coordinates. Authors demonstrate an overall f-measure of 79.63% identifying and geocoding place names. Finally, authors compare the results of the HMM-driven process to earlier work using a Support Vector Machine.</div>Camilahttps://wikipediaquality.com/index.php?title=Wikilda:_Towards_More_Effective_Knowledge_Acquisition_in_Topic_Models_Using_Wikipedia&diff=24017Wikilda: Towards More Effective Knowledge Acquisition in Topic Models Using Wikipedia2020-05-08T09:49:23Z<p>Camila: + links</p>
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<div>'''Wikilda: Towards More Effective Knowledge Acquisition in Topic Models Using Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Swapnil Hingmire]], [[Sutanu Chakraborti]], [[Girish Keshav Palshikar]] and [[Abhay Sodani]].<br />
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== Overview ==<br />
Towards the goal of enhancing interpretability of Latent Dirichlet Allocation (LDA) topics, authors propose WikiLDA, an enhancement to LDA using [[Wikipedia]] concepts. In WikiLDA, initially, for each document in a corpus authors "sprinkle" (append) its most relevant Wikipedia concepts. Authors then use Generalized Polya Urn (GPU) to incorporate word-word, word-concept, and concept-concept semantic [[relatedness]] into the generative process of LDA. As the most probable concepts from inferred topics can be referred on Wikipedia, the topics are likely to become more interpretable and hence more usable in acquiring domain knowledge from humans for various text mining tasks (e.g. eliciting topic labels for text classification). Empirical results show that a projection of documents by WikiLDA in a semantically enriched and coherent topic space leads to improved performance in text classification like tasks, especially in domains where the classes are hard to separate.</div>Camilahttps://wikipediaquality.com/index.php?title=Extracting_the_Main_Path_of_Historic_Events_from_Wikipedia&diff=24016Extracting the Main Path of Historic Events from Wikipedia2020-05-08T09:47:22Z<p>Camila: cat.</p>
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<div>{{Infobox work<br />
| title = Extracting the Main Path of Historic Events from Wikipedia<br />
| date = 2017<br />
| authors = [[Benjamin Cabrera]]<br />[[Barbara König]]<br />
| doi = 10.1007/978-3-319-90312-5_5<br />
| link = https://link.springer.com/chapter/10.1007/978-3-319-90312-5_5<br />
}}<br />
'''Extracting the Main Path of Historic Events from Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Benjamin Cabrera]] and [[Barbara König]].<br />
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== Overview ==<br />
The large online encyclopedia “[[Wikipedia]]” has become a valuable information resource. However, its large size and the interconnectedness of its pages can make it easy to get lost in detail and difficult to gain a good overview of a topic. As a solution authors propose a procedure to extract, summarize, and visualize large [[categories]] of historic Wikipedia articles. At the heart of this procedure authors apply the method of main path analysis—originally developed for citation networks—to a modified network of linked Wikipedia articles. Beside the aggregation method itself, authors describe data mining process of the Wikipedia datasets and the considerations that guided the visualization of the article networks. Finally, authors present web app that allows to experiment with the procedure on an arbitrary Wikipedia category.<br />
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{{cite journal |last1=Cabrera |first1=Benjamin |last2=König |first2=Barbara |title=Extracting the Main Path of Historic Events from Wikipedia |date=2017 |doi=10.1007/978-3-319-90312-5_5 |url=https://wikipediaquality.com/wiki/Extracting_the_Main_Path_of_Historic_Events_from_Wikipedia |journal=Springer, Cham}}<br />
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Cabrera, Benjamin; König, Barbara. (2017). &amp;quot;<a href="https://wikipediaquality.com/wiki/Extracting_the_Main_Path_of_Historic_Events_from_Wikipedia">Extracting the Main Path of Historic Events from Wikipedia</a>&amp;quot;. Springer, Cham. DOI: 10.1007/978-3-319-90312-5_5. <br />
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[[Category:Scientific works]]</div>Camilahttps://wikipediaquality.com/index.php?title=Acquisition_of_Medical_Terminology_for_Ukrainian_from_Parallel_Corpora_and_Wikipedia&diff=24015Acquisition of Medical Terminology for Ukrainian from Parallel Corpora and Wikipedia2020-05-08T09:45:13Z<p>Camila: + embed code</p>
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<div>{{Infobox work<br />
| title = Acquisition of Medical Terminology for Ukrainian from Parallel Corpora and Wikipedia<br />
| date = 2015<br />
| authors = [[Thierry Hamon]]<br />[[Natalia Grabar]]<br />
| link = http://ceur-ws.org/Vol-1495/paper_9.pdf<br />
}}<br />
'''Acquisition of Medical Terminology for Ukrainian from Parallel Corpora and Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Thierry Hamon]] and [[Natalia Grabar]].<br />
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== Overview ==<br />
The increasing availability of parallel bilingual corpora and of automatic methods and tools for their processing makes it possible to build linguistic and terminological resources for low-resourced languages. Authors propose to exploit various corpora available in several languages in order to build bilingual and trilingual terminologies. Typically, terminology information extracted in French and English is associated with the corresponding units in the Ukrainian corpus thanks to the [[multilingual]] transfer. According to the used approaches, precision of thetermextractionvariesbetween0.454and 0.966, while the quality of the interlingual relations varies between 0.309 and 0.965. The resource built contains 4,588 medical termsinUkrainianandtheir34,267relations with French and English terms.<br />
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Hamon, Thierry; Grabar, Natalia. (2015). "[[Acquisition of Medical Terminology for Ukrainian from Parallel Corpora and Wikipedia]]".<br />
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{{cite journal |last1=Hamon |first1=Thierry |last2=Grabar |first2=Natalia |title=Acquisition of Medical Terminology for Ukrainian from Parallel Corpora and Wikipedia |date=2015 |url=https://wikipediaquality.com/wiki/Acquisition_of_Medical_Terminology_for_Ukrainian_from_Parallel_Corpora_and_Wikipedia}}<br />
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Hamon, Thierry; Grabar, Natalia. (2015). &amp;quot;<a href="https://wikipediaquality.com/wiki/Acquisition_of_Medical_Terminology_for_Ukrainian_from_Parallel_Corpora_and_Wikipedia">Acquisition of Medical Terminology for Ukrainian from Parallel Corpora and Wikipedia</a>&amp;quot;.<br />
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</code></div>Camilahttps://wikipediaquality.com/index.php?title=Patterns_of_Creation_and_Usage_of_Wikipedia_Content&diff=24014Patterns of Creation and Usage of Wikipedia Content2020-05-08T09:42:51Z<p>Camila: Embed for English Wikipedia, HTML</p>
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<div>{{Infobox work<br />
| title = Patterns of Creation and Usage of Wikipedia Content<br />
| date = 2012<br />
| authors = [[Andrea Capiluppi]]<br />[[Ana Claudia Duarte Pimentel]]<br />[[Cornelia Boldyreff]]<br />
| doi = 10.1109/WSE.2012.6320537<br />
| link = http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6320537<br />
| plink = https://www.researchgate.net/profile/Andrea_Capiluppi/publication/261486367_Patterns_of_creation_and_usage_of_Wikipedia_content/links/5461eb7a0cf2c1a63c007736.pdf<br />
}}<br />
'''Patterns of Creation and Usage of Wikipedia Content''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Andrea Capiluppi]], [[Ana Claudia Duarte Pimentel]] and [[Cornelia Boldyreff]].<br />
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== Overview ==<br />
Wikipedia is the largest online service storing user-generated content. Its pages are open to anyone for addition, deletion and modifications, and the effort of contributors is recorded and can be tracked in time.<br />
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{{cite journal |last1=Capiluppi |first1=Andrea |last2=Pimentel |first2=Ana Claudia Duarte |last3=Boldyreff |first3=Cornelia |title=Patterns of Creation and Usage of Wikipedia Content |date=2012 |doi=10.1109/WSE.2012.6320537 |url=https://wikipediaquality.com/wiki/Patterns_of_Creation_and_Usage_of_Wikipedia_Content}}<br />
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Capiluppi, Andrea; Pimentel, Ana Claudia Duarte; Boldyreff, Cornelia. (2012). &amp;quot;<a href="https://wikipediaquality.com/wiki/Patterns_of_Creation_and_Usage_of_Wikipedia_Content">Patterns of Creation and Usage of Wikipedia Content</a>&amp;quot;.DOI: 10.1109/WSE.2012.6320537. <br />
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</code></div>Camilahttps://wikipediaquality.com/index.php?title=Research_Guides:_Edit-A-Thon_2018:_Editing_Wikipedia&diff=24013Research Guides: Edit-A-Thon 2018: Editing Wikipedia2020-05-08T09:40:17Z<p>Camila: Cats.</p>
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<div>{{Infobox work<br />
| title = Research Guides: Edit-A-Thon 2018: Editing Wikipedia<br />
| date = 2017<br />
| authors = [[Rachael Clark]]<br />
| link = https://guides.lib.wayne.edu/edit-a-thon_2018/editing<br />
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'''Research Guides: Edit-A-Thon 2018: Editing Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Rachael Clark]].<br />
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== Overview ==<br />
Materials to support the [[Wikipedia]] Edit-a-Thon for the Sesquicentennial Celebration event at the Wayne State University Library System.<br />
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{{cite journal |last1=Clark |first1=Rachael |title=Research Guides: Edit-A-Thon 2018: Editing Wikipedia |date=2017 |url=https://wikipediaquality.com/wiki/Research_Guides:_Edit-A-Thon_2018:_Editing_Wikipedia}}<br />
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Clark, Rachael. (2017). &amp;quot;<a href="https://wikipediaquality.com/wiki/Research_Guides:_Edit-A-Thon_2018:_Editing_Wikipedia">Research Guides: Edit-A-Thon 2018: Editing Wikipedia</a>&amp;quot;.<br />
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[[Category:Scientific works]]</div>Camilahttps://wikipediaquality.com/index.php?title=Unsupervised_Relation_Extraction_by_Mining_Wikipedia_Texts_Using_Information_from_the_Web&diff=24012Unsupervised Relation Extraction by Mining Wikipedia Texts Using Information from the Web2020-05-08T09:38:01Z<p>Camila: + category</p>
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<div>{{Infobox work<br />
| title = Unsupervised Relation Extraction by Mining Wikipedia Texts Using Information from the Web<br />
| date = 2009<br />
| authors = [[Yulan Yan]]<br />[[Naoaki Okazaki]]<br />[[Yutaka Matsuo]]<br />[[Zhenglu Yang]]<br />[[Mitsuru Ishizuka]]<br />
| doi = 10.3115/1690219.1690289<br />
| link = http://dl.acm.org/citation.cfm?id=1690289<br />
}}<br />
'''Unsupervised Relation Extraction by Mining Wikipedia Texts Using Information from the Web''' - scientific work related to [[Wikipedia quality]] published in 2009, written by [[Yulan Yan]], [[Naoaki Okazaki]], [[Yutaka Matsuo]], [[Zhenglu Yang]] and [[Mitsuru Ishizuka]].<br />
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== Overview ==<br />
This paper presents an unsupervised relation extraction method for discovering and enhancing relations in which a specified concept in [[Wikipedia]] participates. Using respective characteristics of Wikipedia articles and Web corpus, authors develop a clustering approach based on combinations of patterns: dependency patterns from dependency analysis of texts in Wikipedia, and surface patterns generated from highly redundant information related to the Web. Evaluations of the proposed approach on two different domains demonstrate the superiority of the pattern combination over existing approaches. Fundamentally, method demonstrates how deep linguistic patterns contribute complementarily with Web surface patterns to the generation of various relations.<br />
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Yan, Yulan; Okazaki, Naoaki; Matsuo, Yutaka; Yang, Zhenglu; Ishizuka, Mitsuru. (2009). "[[Unsupervised Relation Extraction by Mining Wikipedia Texts Using Information from the Web]]". Association for Computational Linguistics. DOI: 10.3115/1690219.1690289. <br />
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{{cite journal |last1=Yan |first1=Yulan |last2=Okazaki |first2=Naoaki |last3=Matsuo |first3=Yutaka |last4=Yang |first4=Zhenglu |last5=Ishizuka |first5=Mitsuru |title=Unsupervised Relation Extraction by Mining Wikipedia Texts Using Information from the Web |date=2009 |doi=10.3115/1690219.1690289 |url=https://wikipediaquality.com/wiki/Unsupervised_Relation_Extraction_by_Mining_Wikipedia_Texts_Using_Information_from_the_Web |journal=Association for Computational Linguistics}}<br />
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Yan, Yulan; Okazaki, Naoaki; Matsuo, Yutaka; Yang, Zhenglu; Ishizuka, Mitsuru. (2009). &amp;quot;<a href="https://wikipediaquality.com/wiki/Unsupervised_Relation_Extraction_by_Mining_Wikipedia_Texts_Using_Information_from_the_Web">Unsupervised Relation Extraction by Mining Wikipedia Texts Using Information from the Web</a>&amp;quot;. Association for Computational Linguistics. DOI: 10.3115/1690219.1690289. <br />
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[[Category:Scientific works]]</div>Camilahttps://wikipediaquality.com/index.php?title=Writing_Radical_Lives:_Undergraduates_Publishing_Activist_Biographies_on_Wikipedia&diff=24011Writing Radical Lives: Undergraduates Publishing Activist Biographies on Wikipedia2020-05-08T09:36:50Z<p>Camila: New work - Writing Radical Lives: Undergraduates Publishing Activist Biographies on Wikipedia</p>
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<div>'''Writing Radical Lives: Undergraduates Publishing Activist Biographies on Wikipedia''' - scientific work related to Wikipedia quality published in 2012, written by Linda S. Watts.<br />
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== Overview ==<br />
This essay explores an instructional application of Wikipedia within the context of an undergraduate capstone course in historical studies, entitled “Revisiting the Weather Underground.” Author wanted student research writing to find a wider audience than the classroom, so devised an assignment which called upon students in this senior seminar to research, write, and publish (via Wikipedia) biographies of individuals associated with this antiwar, anti-imperialist organization. Course membership included both traditional-aged college students and returning students. None had prior experience with social history, biography, publishing, or writing/editing on Wikipedia. Despite the fact that all participants (and Author include myself here) had a steep learning curve when it came to the technology necessary to address a reading public through Wikipedia, the students rose to the challenge. The use of Wikipedia as venue shaped the manner in which students thought about their biographical subjects (some of whom could conceivably – and do, in fact – read and respond to the biographies), their subject matter (the Weather Underground), their audience (which included Wikipedia readers and editors internationally), their responsibilities as researchers to be accountable for their characterizations of others’ life stories, their accountability in sourcing information, and their sense of authorship (which all needed to learn to share with strangers encountered through Wikipedia). In reflecting on the assignment, students valued the experience as authentic scholarly communication and lasting historical learning. The featured assignment demanded close partnerships among students, faculty, librarians, educational technologists, and Wikipedia editors/administrators, and served also to dramatize the perils and possibilities of shared inquiry.</div>Camilahttps://wikipediaquality.com/index.php?title=Wikipedia_and_Neurological_Disorders&diff=24010Wikipedia and Neurological Disorders2020-05-08T09:34:32Z<p>Camila: + infobox</p>
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<div>{{Infobox work<br />
| title = Wikipedia and Neurological Disorders<br />
| date = 2015<br />
| authors = [[Francesco Brigo]]<br />[[Stanley C. Igwe]]<br />[[Raffaele Nardone]]<br />[[Piergiorgio Lochner]]<br />[[Frediano Tezzon]]<br />[[Willem M. Otte]]<br />
| doi = 10.1016/j.jocn.2015.02.006<br />
| link = http://www.sciencedirect.com/science/article/pii/S0967586815000995<br />
}}<br />
'''Wikipedia and Neurological Disorders''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Francesco Brigo]], [[Stanley C. Igwe]], [[Raffaele Nardone]], [[Piergiorgio Lochner]], [[Frediano Tezzon]] and [[Willem M. Otte]].<br />
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== Overview ==<br />
Abstract Authors aim was to evaluate [[Wikipedia]] page visits in relation to the most common neurological disorders by determining which factors are related to peaks in Wikipedia searches for these conditions. Millions of people worldwide use the internet daily as a source of health information. Wikipedia is a popular free online encyclopedia used by patients and physicians to search for health-related information. The following Wikipedia articles were considered: Alzheimer’s disease; Amyotrophic lateral sclerosis; Dementia; Epilepsy; Epileptic seizure; Migraine; Multiple sclerosis; Parkinson’s disease; Stroke; Traumatic brain injury. Authors analyzed information regarding the total article views for 90 days and the rank of these articles among all those available in Wikipedia. Authors determined the highest search volume peaks to identify possible relation with online news headlines. No relation between incidence or prevalence of neurological disorders and the search volume for the related articles was found. Seven out of 10 neurological conditions showed relations in search volume peaks and news headlines. Six out of these seven peaks were related to news about famous people suffering from neurological disorders, especially those from showbusiness. Identification of discrepancies between disease burden and health seeking behavior on Wikipedia is useful in the planning of public health campaigns. Celebrities who publicly announce their neurological diagnosis might effectively promote awareness programs, increase public knowledge and reduce stigma related to diagnoses of neurological disorders.</div>Camilahttps://wikipediaquality.com/index.php?title=Wikipedia_Uses_in_Learning_Design:_a_Literature_Review&diff=23686Wikipedia Uses in Learning Design: a Literature Review2020-02-20T08:48:21Z<p>Camila: infobox</p>
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<div>{{Infobox work<br />
| title = Wikipedia Uses in Learning Design: a Literature Review<br />
| date = 2012<br />
| authors = [[Maria Zoumpatianou]]<br />
| link = http://earthlab.uoi.gr/theste/index.php/theste/article/download/109/76<br />
}}<br />
'''Wikipedia Uses in Learning Design: a Literature Review''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Maria Zoumpatianou]].<br />
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== Overview ==<br />
This paper is a literature review report concerning educational uses of [[Wikipedia]] in the first 10 years of its existence. The aim of the work is the tracing and the presentation of published and validated educational applications of Wikipedia in a manner that could inform learning design by teachers or researchers. For the review, 24 scientific publications, retrieved from research databases and concerning educational applications of Wikipedia were analyzed. The review reveals a variety of learning uses of Wikipedia in several contexts, knowledge fields, education level and students’ ages. In addition, the study shows a variety of expected learning outcomes and a pleiad of student tasks in the learning activities. Both, the learning outcomes and the students’ tasks are organized in general types with the aim of constituting patterns for new learning designs. The findings are commented and possible future directions of research are described.</div>Camilahttps://wikipediaquality.com/index.php?title=Hypernyms_Through_Intra-Article_Organization_in_Wikipedia&diff=23685Hypernyms Through Intra-Article Organization in Wikipedia2020-02-20T08:46:46Z<p>Camila: Adding infobox</p>
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<div>{{Infobox work<br />
| title = Hypernyms Through Intra-Article Organization in Wikipedia<br />
| date = 2018<br />
| authors = [[Disha Shrivastava]]<br />[[Sreyash Kenkre]]<br />[[Santosh Penubothula]]<br />
| link = https://econpapers.repec.org/paper/nadwpaper/20180012.htm<br />
| plink = http://arxiv.org/pdf/1809.00414.pdf<br />
}}<br />
'''Hypernyms Through Intra-Article Organization in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2018, written by [[Disha Shrivastava]], [[Sreyash Kenkre]] and [[Santosh Penubothula]].<br />
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== Overview ==<br />
Authors introduce a new measure for unsupervised hypernym detection and directionality. The motivation is to keep the measure computationally light and portatable across languages. Authors show that the relative physical location of words in explanatory articles captures the directionality property. Further, the phrases in section titles of articles about the word, capture the [[semantic similarity]] needed for hypernym detection task. Authors experimentally show that the combination of [[features]] coming from these two simple [[measures]] suffices to produce results comparable with the best unsupervised measures in terms of the average precision.</div>Camilahttps://wikipediaquality.com/index.php?title=Bookmark_Recommendation_in_Social_Bookmarking_Services_Using_Wikipedia&diff=23684Bookmark Recommendation in Social Bookmarking Services Using Wikipedia2020-02-20T08:44:51Z<p>Camila: + Infobox work</p>
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<div>{{Infobox work<br />
| title = Bookmark Recommendation in Social Bookmarking Services Using Wikipedia<br />
| date = 2013<br />
| authors = [[Takumi Yoshida]]<br />[[Ushio Inoue]]<br />
| doi = 10.1109/ICIS.2013.6607898<br />
| link = http://ieeexplore.ieee.org/xpl/abstractAuthors.jsp?reload=true&amp;arnumber=6607898<br />
}}<br />
'''Bookmark Recommendation in Social Bookmarking Services Using Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Takumi Yoshida]] and [[Ushio Inoue]].<br />
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== Overview ==<br />
Social bookmarking systems allow users to attach freely chosen keywords as tags to bookmarks of web pages. These tags are used to recommend relevant bookmarks to other users. However, there is no guarantee that every user get enough bookmark recommended, because of the diversity of tags. In this paper, authors propose a personalized recommender system using [[Wikipedia]]. Authors system extends a tag set to find similar users and relevant bookmarks by using the Wikipedia category database. The experimental results show that significant increase of relevant bookmarks recommended without notable increase of the noise.</div>Camilahttps://wikipediaquality.com/index.php?title=Wikilit:_Collecting_the_Wiki_and_Wikipedia_Literature&diff=23683Wikilit: Collecting the Wiki and Wikipedia Literature2020-02-20T08:43:13Z<p>Camila: Information about: Wikilit: Collecting the Wiki and Wikipedia Literature</p>
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<div>'''Wikilit: Collecting the Wiki and Wikipedia Literature''' - scientific work related to Wikipedia quality published in 2011, written by Phoebe Ayers and Reid Priedhorsky.<br />
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== Overview ==<br />
This workshop has three key goals. First, authors will examine existing and proposed systems for collecting and analyzing the research literature about wikis. Second, authors will discuss the challenges in building such a system and will engage participants to design a sustainable collaborative system to achieve this goal. Finally, authors will provide a forum to build upon ongoing wiki community discussions about problems and opportunities in finding and sharing the wiki research literature.</div>Camilahttps://wikipediaquality.com/index.php?title=Scale-Free_Topology_of_the_Interlanguage_Links_in_Wikipedia&diff=23682Scale-Free Topology of the Interlanguage Links in Wikipedia2020-02-20T08:41:34Z<p>Camila: Infobox</p>
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<div>{{Infobox work<br />
| title = Scale-Free Topology of the Interlanguage Links in Wikipedia<br />
| date = 2009<br />
| authors = [[Łukasz Bolikowski]]<br />
| link = http://files.eric.ed.gov/fulltext/EJ1075481.pdf<br />
| plink = https://arxiv.org/abs/0904.0564<br />
}}<br />
'''Scale-Free Topology of the Interlanguage Links in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2009, written by [[Łukasz Bolikowski]].<br />
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== Overview ==<br />
The interlanguage links in [[Wikipedia]] connect pages on the same subject written in [[different language]]s. In theory, each connected component should be a clique and cover one topic. However, incoherent edits and obvious mistakes result in topic coalescence, yielding a non-trivial topology that is studied in this paper. Authors show that the component size distribution obeys the power law, and authors explain anomalies in the distribution as results of certain edit conventions. Next, authors propose a method of filtering out the cliques and study basic properties of the resulting skeleton, which turns out to be scale-free.</div>Camilahttps://wikipediaquality.com/index.php?title=Named_Entity_Network_based_on_Wikipedia&diff=23681Named Entity Network based on Wikipedia2020-02-20T08:40:04Z<p>Camila: + Embed</p>
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<div>{{Infobox work<br />
| title = Named Entity Network based on Wikipedia<br />
| date = 2009<br />
| authors = [[Sameer Maskey]]<br />[[Wisam Dakka]]<br />
| link = http://www.cs.columbia.edu/~smaskey/papers/nenet.pdf<br />
}}<br />
'''Named Entity Network based on Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2009, written by [[Sameer Maskey]] and [[Wisam Dakka]].<br />
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== Overview ==<br />
Named Entities (NEs) play an important role in many natural language and speech processing tasks. A resource that identifies relations between NEs could potentially be very useful. Authors present such automatically generated knowledge resource from [[Wikipedia]], Named Entity Network (NE-NET), that provides a list of related Named Entities (NEs) and the degree of relation for any given NE. Unlike some manually built knowledge resource, NE-NET has a wide coverage consisting of 1.5 million NEs represented as nodes of a graph with 6.5 million arcs relating them. NE-NET also provides the ranks of the related NEs using a simple ranking function that authors propose. In this paper, authors present NE-NET and experiments showing how NE-NET can be used to improve the retrieval of spoken (Broadcast News) and text documents.<br />
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Maskey, Sameer; Dakka, Wisam. (2009). "[[Named Entity Network based on Wikipedia]]".<br />
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{{cite journal |last1=Maskey |first1=Sameer |last2=Dakka |first2=Wisam |title=Named Entity Network based on Wikipedia |date=2009 |url=https://wikipediaquality.com/wiki/Named_Entity_Network_based_on_Wikipedia}}<br />
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Maskey, Sameer; Dakka, Wisam. (2009). &amp;quot;<a href="https://wikipediaquality.com/wiki/Named_Entity_Network_based_on_Wikipedia">Named Entity Network based on Wikipedia</a>&amp;quot;.<br />
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</code></div>Camilahttps://wikipediaquality.com/index.php?title=On_the_Evolution_of_Wikipedia&diff=23680On the Evolution of Wikipedia2020-02-20T08:38:01Z<p>Camila: + Embed</p>
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<div>{{Infobox work<br />
| title = On the Evolution of Wikipedia<br />
| date = 2007<br />
| authors = [[Rodrigo B. Almeida]]<br />[[Barzan Mozafari]]<br />[[Junghoo Cho]]<br />
| link = http://www.icwsm.org/papers/paper2.html<br />
}}<br />
'''On the Evolution of Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2007, written by [[Rodrigo B. Almeida]], [[Barzan Mozafari]] and [[Junghoo Cho]].<br />
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== Overview ==<br />
A recent phenomenon on the Web is the emergence and proliferation of new social media systems allowing social interaction between people. One of the most popular of these systems is [[Wikipedia]] that allows users to create content in a collaborative way. Despite its current popularity, not much is known about how users interact with Wikipedia and how it has evolved over time. In this paper authors aim to provide a first, extensive study of the user behavior on Wikipedia and its evolution. Compared to prior studies, work differs in several ways. First, previous studies on the analysis of the user workloads (for systems such as peer-to-peer systems [10] and Web servers [2]) have mainly focused on understanding the users who are accessing information. In contrast, Wikipedia’s provides us with the opportunity to understand how users create and maintain information since it provides the complete evolution history of its content. Second, the main focus of prior studies is evaluating the implication of the user workloads on the system performance, while study is trying to understand the evolution of the data corpus and the user behavior themselves. Authors main findings include that (1) the evolution and updates of Wikipedia is governed by a self-similar process, not by the Poisson process that has been observed for the general Web [4, 6] and (2) the exponential growth of Wikipedia is mainly driven by its rapidly increasing user base, indicating the importance of its open editorial policy for its current success. Authors also find that (3) the number of updates made to the Wikipedia articles exhibit a power-law distribution, but the distribution is less skewed than those obtained from other studies.<br />
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Almeida, Rodrigo B.; Mozafari, Barzan; Cho, Junghoo. (2007). "[[On the Evolution of Wikipedia]]".<br />
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{{cite journal |last1=Almeida |first1=Rodrigo B. |last2=Mozafari |first2=Barzan |last3=Cho |first3=Junghoo |title=On the Evolution of Wikipedia |date=2007 |url=https://wikipediaquality.com/wiki/On_the_Evolution_of_Wikipedia}}<br />
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Almeida, Rodrigo B.; Mozafari, Barzan; Cho, Junghoo. (2007). &amp;quot;<a href="https://wikipediaquality.com/wiki/On_the_Evolution_of_Wikipedia">On the Evolution of Wikipedia</a>&amp;quot;.<br />
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</code></div>Camilahttps://wikipediaquality.com/index.php?title=Keyword_Extraction_for_Mining_Meaningful_Learning-Contents_on_the_Web_Using_Wikipedia&diff=23679Keyword Extraction for Mining Meaningful Learning-Contents on the Web Using Wikipedia2020-02-20T08:35:05Z<p>Camila: Adding infobox</p>
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<div>{{Infobox work<br />
| title = Keyword Extraction for Mining Meaningful Learning-Contents on the Web Using Wikipedia<br />
| date = 2014<br />
| authors = [[Tetsuya Toyota]]<br />[[Yuan Sun]]<br />
| doi = 10.1109/FIE.2014.7044344<br />
| link = http://ieeexplore.ieee.org/iel7/7017968/7043978/07044344.pdf<br />
}}<br />
'''Keyword Extraction for Mining Meaningful Learning-Contents on the Web Using Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Tetsuya Toyota]] and [[Yuan Sun]].<br />
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== Overview ==<br />
The purpose of this paper is to provide a solution of extracting appropriate keywords to identify meaningful learning-contents on the Web. There are some issues in identifying documents that have learning content. Firstly, the documents need to be identified according to the learning area of a student's school year. Secondly, the documents need to be identified according to the learning area that the student is now studying or studied. In this paper, authors present a method of extracting keywords for mining meaningful learning-contents using [[Wikipedia]]. At first, authors select the articles in Wikipedia with the arbitrary input keyword of learning items. Then, authors select other Wikipedia's articles related to the articles selected by the first process, using links and [[categories]] of Wikipedia. Furthermore, authors calculate degrees of association between the articles and the keywords using PF-IBF, and put the degree on each keyword. Finally, authors screen the keywords using his/her curriculum guideline to adjust the keywords to the learning area of the student's school year. In the next step, authors are planning to develop a method of screening keywords according to each student's ability, so that authors can select more appropriate keywords for each student.</div>Camilahttps://wikipediaquality.com/index.php?title=An_Unsupervised_Approach_to_Biography_Production_Using_Wikipedia&diff=23678An Unsupervised Approach to Biography Production Using Wikipedia2020-02-20T08:32:59Z<p>Camila: + embed code</p>
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<div>{{Infobox work<br />
| title = An Unsupervised Approach to Biography Production Using Wikipedia<br />
| date = 2008<br />
| authors = [[Fadi Biadsy]]<br />[[Julia Hirschberg]]<br />[[Elena Filatova]]<br />
| doi = 10.7916/D80C543Q<br />
| link = http://www.aclweb.org/anthology/P08-1092<br />
}}<br />
'''An Unsupervised Approach to Biography Production Using Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2008, written by [[Fadi Biadsy]], [[Julia Hirschberg]] and [[Elena Filatova]].<br />
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== Overview ==<br />
Authors describe an unsupervised approach to multi-document sentence-extraction based summarization for the task of producing biographies. Authors utilize [[Wikipedia]] to automatically construct a corpus of biographical sentences and TDT4 to construct a corpus of non-biographical sentences. Authors build a biographical-sentence classifier from these corpora and an SVM regression model for sentence ordering from the Wikipedia corpus. Authors evaluate work on the DUC2004 evaluation data and with human judges. Overall, system significantly outperforms all systems that participated in DUC2004, according to the ROUGE-L metric, and is preferred by human subjects.<br />
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Biadsy, Fadi; Hirschberg, Julia; Filatova, Elena. (2008). "[[An Unsupervised Approach to Biography Production Using Wikipedia]]". ACL/HTL 2008. DOI: 10.7916/D80C543Q. <br />
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{{cite journal |last1=Biadsy |first1=Fadi |last2=Hirschberg |first2=Julia |last3=Filatova |first3=Elena |title=An Unsupervised Approach to Biography Production Using Wikipedia |date=2008 |doi=10.7916/D80C543Q |url=https://wikipediaquality.com/wiki/An_Unsupervised_Approach_to_Biography_Production_Using_Wikipedia |journal=ACL/HTL 2008}}<br />
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Biadsy, Fadi; Hirschberg, Julia; Filatova, Elena. (2008). &amp;quot;<a href="https://wikipediaquality.com/wiki/An_Unsupervised_Approach_to_Biography_Production_Using_Wikipedia">An Unsupervised Approach to Biography Production Using Wikipedia</a>&amp;quot;. ACL/HTL 2008. DOI: 10.7916/D80C543Q. <br />
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