Automatic subject metadata generation for scientific documents using wikipedia and genetic algorithms
|Automatic subject metadata generation for scientific documents using wikipedia and genetic algorithms|
|Author(s)||Joorabchi A., Mahdi A.E.|
|Published in||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Keyword(s)||genetic algorithms, keyphrase annotation, keyphrase indexing, scientific digital libraries, subject metadata, text mining, Wikipedia (Extra: Annotation methods, Candidate selection, Key-phrase, Metadata generation, Scientific documents, Text mining, Wikipedia, Data mining, Digital libraries, Genetic algorithms, Knowledge engineering, Knowledge management, Websites, Metadata)|
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Automatic subject metadata generation for scientific documents using wikipedia and genetic algorithms is a 2012 conference paper written in English by Joorabchi A., Mahdi A.E. and published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
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 our method, evaluated in terms of inter-consistency with human annotators, is on a par with that achieved by humans and outperforms rival supervised methods.
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