Difference between revisions of "(Digital) Goodies from the Erc Wishing Well: Babelnet, Babelfy, Video Games with a Purpose and the Wikipedia Bitaxonomy"
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== Overview == | == Overview == | ||
Multilinguality is a key feature of today’s Web, and it is this feature that authors leverage and exploit in research work at the Sapienza University of Rome’s Linguistic Computing Laboratory, which Author am going to overview and showcase in this talk. Author will start by presenting BabelNet 2.5 (Navigli and Ponzetto, 2012), available at http://babelnet.org, a very large [[multilingual]] encyclopedic dictionary and semantic network, which covers 50 languages and provides both lexicographic and encyclopedic knowledge for all the open-class parts of speech, thanks to the seamless integration of [[WordNet]], [[Wikipedia]], Wiktionary, OmegaWiki, [[Wikidata]] and the Open Multilingual WordNet. In order to construct the BabelNet network, authors extract at different stages: from WordNet, all available word senses (as concepts) and all the lexical and semantic pointers between synsets (as relations); from Wikipedia, all the Wikipages (i.e., Wikipages, as concepts) and semantically unspecified relations from their hyperlinks. WordNet and Wikipedia overlap both in terms of concepts and relations: this overlap makes the merging between the two resources possible, enabling the creation of a unified knowledge resource. In order to enable multilinguality, authors collect the lexical realizations of the available concepts in [[different language]]s. Finally, authors connect the multilingual Babel synsets by establishing semantic relations between them. Next, Author will presentBabelfy (Moro et al., 2014), available athttp://babelfy.o rg, a unified approach that leverages BabelNet to perform Word Sense Disambiguation (WSD) and Entity Linking in arbitrary languages, with performance on both tasks on a par with, or surpassing, those of task-specific state-of-the-art supervised systems. Babelfy works in three steps: first, given a lexicalized semantic network, authors associate with each vertex, i.e., either concept or [[named entity]], a semantic signature, that is, a set of related vertices. This is a preliminary step which needs to be performed only once, independently of the input text. Second, given a text, authors extract all the linkable fragments from this text and, for each of them, list the possible meanings according to the semantic network. Third, authors create a graph-based semantic interpretation of the whole text by linking the candidate meanings of the extracted fragments using the previously-computed semantic signatures. Authors then extract a dense subgraph of this representation and select the best candidate meaning for each fragment. Authors experiments show state-of-the-art performances on both WSD and EL on 6 different datasets, including a multilingual setting. In the third part of the talk Author will present two novel approaches to large-scale knowledge acquisition and validation developed in my lab. Author will first introduce video games with a purpose (Vannella et al., 2014), a novel, powerful paradigm for the large scale acquisition and validation of knowledge and data (http://knowledgeforge.org). Authors demonstrate that converting games with a purpose into more traditional video games provides a fun component that motivates players to annotate for free, thereby significantly lowering annotation costs below that of crowdsourcing. Moreover, authors show that video games with a purpose produce higher-quality annotations than crowdsourcing. | Multilinguality is a key feature of today’s Web, and it is this feature that authors leverage and exploit in research work at the Sapienza University of Rome’s Linguistic Computing Laboratory, which Author am going to overview and showcase in this talk. Author will start by presenting BabelNet 2.5 (Navigli and Ponzetto, 2012), available at http://babelnet.org, a very large [[multilingual]] encyclopedic dictionary and semantic network, which covers 50 languages and provides both lexicographic and encyclopedic knowledge for all the open-class parts of speech, thanks to the seamless integration of [[WordNet]], [[Wikipedia]], Wiktionary, OmegaWiki, [[Wikidata]] and the Open Multilingual WordNet. In order to construct the BabelNet network, authors extract at different stages: from WordNet, all available word senses (as concepts) and all the lexical and semantic pointers between synsets (as relations); from Wikipedia, all the Wikipages (i.e., Wikipages, as concepts) and semantically unspecified relations from their hyperlinks. WordNet and Wikipedia overlap both in terms of concepts and relations: this overlap makes the merging between the two resources possible, enabling the creation of a unified knowledge resource. In order to enable multilinguality, authors collect the lexical realizations of the available concepts in [[different language]]s. Finally, authors connect the multilingual Babel synsets by establishing semantic relations between them. Next, Author will presentBabelfy (Moro et al., 2014), available athttp://babelfy.o rg, a unified approach that leverages BabelNet to perform Word Sense Disambiguation (WSD) and Entity Linking in arbitrary languages, with performance on both tasks on a par with, or surpassing, those of task-specific state-of-the-art supervised systems. Babelfy works in three steps: first, given a lexicalized semantic network, authors associate with each vertex, i.e., either concept or [[named entity]], a semantic signature, that is, a set of related vertices. This is a preliminary step which needs to be performed only once, independently of the input text. Second, given a text, authors extract all the linkable fragments from this text and, for each of them, list the possible meanings according to the semantic network. Third, authors create a graph-based semantic interpretation of the whole text by linking the candidate meanings of the extracted fragments using the previously-computed semantic signatures. Authors then extract a dense subgraph of this representation and select the best candidate meaning for each fragment. Authors experiments show state-of-the-art performances on both WSD and EL on 6 different datasets, including a multilingual setting. In the third part of the talk Author will present two novel approaches to large-scale knowledge acquisition and validation developed in my lab. Author will first introduce video games with a purpose (Vannella et al., 2014), a novel, powerful paradigm for the large scale acquisition and validation of knowledge and data (http://knowledgeforge.org). Authors demonstrate that converting games with a purpose into more traditional video games provides a fun component that motivates players to annotate for free, thereby significantly lowering annotation costs below that of crowdsourcing. Moreover, authors show that video games with a purpose produce higher-quality annotations than crowdsourcing. | ||
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| + | == Embed == | ||
| + | === Wikipedia Quality === | ||
| + | <code> | ||
| + | <nowiki> | ||
| + | Navigli, Roberto. (2014). "[[(Digital) Goodies from the Erc Wishing Well: Babelnet, Babelfy, Video Games with a Purpose and the Wikipedia Bitaxonomy]]".DOI: 10.3115/v1/W14-4711. | ||
| + | </nowiki> | ||
| + | </code> | ||
| + | |||
| + | === English Wikipedia === | ||
| + | <code> | ||
| + | <nowiki> | ||
| + | {{cite journal |last1=Navigli |first1=Roberto |title=(Digital) Goodies from the Erc Wishing Well: Babelnet, Babelfy, Video Games with a Purpose and the Wikipedia Bitaxonomy |date=2014 |doi=10.3115/v1/W14-4711 |url=https://wikipediaquality.com/wiki/(Digital)_Goodies_from_the_Erc_Wishing_Well:_Babelnet,_Babelfy,_Video_Games_with_a_Purpose_and_the_Wikipedia_Bitaxonomy}} | ||
| + | </nowiki> | ||
| + | </code> | ||
| + | |||
| + | === HTML === | ||
| + | <code> | ||
| + | <nowiki> | ||
| + | Navigli, Roberto. (2014). &quot;<a href="https://wikipediaquality.com/wiki/(Digital)_Goodies_from_the_Erc_Wishing_Well:_Babelnet,_Babelfy,_Video_Games_with_a_Purpose_and_the_Wikipedia_Bitaxonomy">(Digital) Goodies from the Erc Wishing Well: Babelnet, Babelfy, Video Games with a Purpose and the Wikipedia Bitaxonomy</a>&quot;.DOI: 10.3115/v1/W14-4711. | ||
| + | </nowiki> | ||
| + | </code> | ||
Revision as of 11:32, 14 December 2019
Authors | Roberto Navigli |
|---|---|
Publication date | 2014 |
DOI | 10.3115/v1/W14-4711 |
Links | Original |
(Digital) Goodies from the Erc Wishing Well: Babelnet, Babelfy, Video Games with a Purpose and the Wikipedia Bitaxonomy - scientific work related to Wikipedia quality published in 2014, written by Roberto Navigli.
Overview
Multilinguality is a key feature of today’s Web, and it is this feature that authors leverage and exploit in research work at the Sapienza University of Rome’s Linguistic Computing Laboratory, which Author am going to overview and showcase in this talk. Author will start by presenting BabelNet 2.5 (Navigli and Ponzetto, 2012), available at http://babelnet.org, a very large multilingual encyclopedic dictionary and semantic network, which covers 50 languages and provides both lexicographic and encyclopedic knowledge for all the open-class parts of speech, thanks to the seamless integration of WordNet, Wikipedia, Wiktionary, OmegaWiki, Wikidata and the Open Multilingual WordNet. In order to construct the BabelNet network, authors extract at different stages: from WordNet, all available word senses (as concepts) and all the lexical and semantic pointers between synsets (as relations); from Wikipedia, all the Wikipages (i.e., Wikipages, as concepts) and semantically unspecified relations from their hyperlinks. WordNet and Wikipedia overlap both in terms of concepts and relations: this overlap makes the merging between the two resources possible, enabling the creation of a unified knowledge resource. In order to enable multilinguality, authors collect the lexical realizations of the available concepts in different languages. Finally, authors connect the multilingual Babel synsets by establishing semantic relations between them. Next, Author will presentBabelfy (Moro et al., 2014), available athttp://babelfy.o rg, a unified approach that leverages BabelNet to perform Word Sense Disambiguation (WSD) and Entity Linking in arbitrary languages, with performance on both tasks on a par with, or surpassing, those of task-specific state-of-the-art supervised systems. Babelfy works in three steps: first, given a lexicalized semantic network, authors associate with each vertex, i.e., either concept or named entity, a semantic signature, that is, a set of related vertices. This is a preliminary step which needs to be performed only once, independently of the input text. Second, given a text, authors extract all the linkable fragments from this text and, for each of them, list the possible meanings according to the semantic network. Third, authors create a graph-based semantic interpretation of the whole text by linking the candidate meanings of the extracted fragments using the previously-computed semantic signatures. Authors then extract a dense subgraph of this representation and select the best candidate meaning for each fragment. Authors experiments show state-of-the-art performances on both WSD and EL on 6 different datasets, including a multilingual setting. In the third part of the talk Author will present two novel approaches to large-scale knowledge acquisition and validation developed in my lab. Author will first introduce video games with a purpose (Vannella et al., 2014), a novel, powerful paradigm for the large scale acquisition and validation of knowledge and data (http://knowledgeforge.org). Authors demonstrate that converting games with a purpose into more traditional video games provides a fun component that motivates players to annotate for free, thereby significantly lowering annotation costs below that of crowdsourcing. Moreover, authors show that video games with a purpose produce higher-quality annotations than crowdsourcing.
Embed
Wikipedia Quality
Navigli, Roberto. (2014). "[[(Digital) Goodies from the Erc Wishing Well: Babelnet, Babelfy, Video Games with a Purpose and the Wikipedia Bitaxonomy]]".DOI: 10.3115/v1/W14-4711.
English Wikipedia
{{cite journal |last1=Navigli |first1=Roberto |title=(Digital) Goodies from the Erc Wishing Well: Babelnet, Babelfy, Video Games with a Purpose and the Wikipedia Bitaxonomy |date=2014 |doi=10.3115/v1/W14-4711 |url=https://wikipediaquality.com/wiki/(Digital)_Goodies_from_the_Erc_Wishing_Well:_Babelnet,_Babelfy,_Video_Games_with_a_Purpose_and_the_Wikipedia_Bitaxonomy}}
HTML
Navigli, Roberto. (2014). "<a href="https://wikipediaquality.com/wiki/(Digital)_Goodies_from_the_Erc_Wishing_Well:_Babelnet,_Babelfy,_Video_Games_with_a_Purpose_and_the_Wikipedia_Bitaxonomy">(Digital) Goodies from the Erc Wishing Well: Babelnet, Babelfy, Video Games with a Purpose and the Wikipedia Bitaxonomy</a>".DOI: 10.3115/v1/W14-4711.
