Difference between revisions of "Automatic Subject Metadata Generation for Scientific Documents Using Wikipedia and Genetic Algorithms"
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− | '''Automatic Subject Metadata Generation for Scientific Documents Using Wikipedia and Genetic Algorithms''' - scientific work related to Wikipedia quality published in 2012, written by Arash Joorabchi and Abdulhussain E. Mahdi. | + | {{Infobox work |
+ | | title = Automatic Subject Metadata Generation for Scientific Documents Using Wikipedia and Genetic Algorithms | ||
+ | | date = 2012 | ||
+ | | authors = [[Arash Joorabchi]]<br />[[Abdulhussain E. Mahdi]] | ||
+ | | doi = 10.1007/978-3-642-33876-2_6 | ||
+ | | link = https://dl.acm.org/citation.cfm?id=2413951 | ||
+ | }} | ||
+ | '''Automatic Subject Metadata Generation for Scientific Documents Using Wikipedia and Genetic Algorithms''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Arash Joorabchi]] and [[Abdulhussain E. Mahdi]]. | ||
== Overview == | == Overview == | ||
− | Topical annotation of documents with keyphrases is a proven method for revealing the subject of scientific and research documents. However, scientific documents that are manually annotated with keyphrases are in the minority. This paper describes a machine learning-based automatic keyphrase annotation method for scientific documents, which utilizes Wikipedia as a thesaurus for candidate selection from documents' content and deploys genetic algorithms to learn a model for ranking and filtering the most probable keyphrases. Reported experimental results show that the performance of method, evaluated in terms of inter-consistency with human annotators, is on a par with that achieved by humans and outperforms rival supervised methods. | + | Topical annotation of documents with keyphrases is a proven method for revealing the subject of scientific and research documents. However, scientific documents that are manually annotated with keyphrases are in the minority. This paper describes a machine learning-based automatic keyphrase annotation method for scientific documents, which utilizes [[Wikipedia]] as a thesaurus for candidate selection from documents' content and deploys genetic algorithms to learn a model for ranking and filtering the most probable keyphrases. Reported experimental results show that the performance of method, evaluated in terms of inter-consistency with human annotators, is on a par with that achieved by humans and outperforms rival supervised methods. |
+ | |||
+ | == Embed == | ||
+ | === Wikipedia Quality === | ||
+ | <code> | ||
+ | <nowiki> | ||
+ | Joorabchi, Arash; Mahdi, Abdulhussain E.. (2012). "[[Automatic Subject Metadata Generation for Scientific Documents Using Wikipedia and Genetic Algorithms]]". Springer, Berlin, Heidelberg. DOI: 10.1007/978-3-642-33876-2_6. | ||
+ | </nowiki> | ||
+ | </code> | ||
+ | |||
+ | === English Wikipedia === | ||
+ | <code> | ||
+ | <nowiki> | ||
+ | {{cite journal |last1=Joorabchi |first1=Arash |last2=Mahdi |first2=Abdulhussain E. |title=Automatic Subject Metadata Generation for Scientific Documents Using Wikipedia and Genetic Algorithms |date=2012 |doi=10.1007/978-3-642-33876-2_6 |url=https://wikipediaquality.com/wiki/Automatic_Subject_Metadata_Generation_for_Scientific_Documents_Using_Wikipedia_and_Genetic_Algorithms |journal=Springer, Berlin, Heidelberg}} | ||
+ | </nowiki> | ||
+ | </code> | ||
+ | |||
+ | === HTML === | ||
+ | <code> | ||
+ | <nowiki> | ||
+ | Joorabchi, Arash; Mahdi, Abdulhussain E.. (2012). &quot;<a href="https://wikipediaquality.com/wiki/Automatic_Subject_Metadata_Generation_for_Scientific_Documents_Using_Wikipedia_and_Genetic_Algorithms">Automatic Subject Metadata Generation for Scientific Documents Using Wikipedia and Genetic Algorithms</a>&quot;. Springer, Berlin, Heidelberg. DOI: 10.1007/978-3-642-33876-2_6. | ||
+ | </nowiki> | ||
+ | </code> | ||
+ | |||
+ | |||
+ | |||
+ | [[Category:Scientific works]] |
Latest revision as of 09:09, 9 November 2019
Authors | Arash Joorabchi Abdulhussain E. Mahdi |
---|---|
Publication date | 2012 |
DOI | 10.1007/978-3-642-33876-2_6 |
Links | Original |
Automatic Subject Metadata Generation for Scientific Documents Using Wikipedia and Genetic Algorithms - scientific work related to Wikipedia quality published in 2012, written by Arash Joorabchi and Abdulhussain E. Mahdi.
Overview
Topical annotation of documents with keyphrases is a proven method for revealing the subject of scientific and research documents. However, scientific documents that are manually annotated with keyphrases are in the minority. This paper describes a machine learning-based automatic keyphrase annotation method for scientific documents, which utilizes Wikipedia as a thesaurus for candidate selection from documents' content and deploys genetic algorithms to learn a model for ranking and filtering the most probable keyphrases. Reported experimental results show that the performance of method, evaluated in terms of inter-consistency with human annotators, is on a par with that achieved by humans and outperforms rival supervised methods.
Embed
Wikipedia Quality
Joorabchi, Arash; Mahdi, Abdulhussain E.. (2012). "[[Automatic Subject Metadata Generation for Scientific Documents Using Wikipedia and Genetic Algorithms]]". Springer, Berlin, Heidelberg. DOI: 10.1007/978-3-642-33876-2_6.
English Wikipedia
{{cite journal |last1=Joorabchi |first1=Arash |last2=Mahdi |first2=Abdulhussain E. |title=Automatic Subject Metadata Generation for Scientific Documents Using Wikipedia and Genetic Algorithms |date=2012 |doi=10.1007/978-3-642-33876-2_6 |url=https://wikipediaquality.com/wiki/Automatic_Subject_Metadata_Generation_for_Scientific_Documents_Using_Wikipedia_and_Genetic_Algorithms |journal=Springer, Berlin, Heidelberg}}
HTML
Joorabchi, Arash; Mahdi, Abdulhussain E.. (2012). "<a href="https://wikipediaquality.com/wiki/Automatic_Subject_Metadata_Generation_for_Scientific_Documents_Using_Wikipedia_and_Genetic_Algorithms">Automatic Subject Metadata Generation for Scientific Documents Using Wikipedia and Genetic Algorithms</a>". Springer, Berlin, Heidelberg. DOI: 10.1007/978-3-642-33876-2_6.