Matches in ScholarlyData for { ?s ?p Topical annotation of documents with keyphrases is a proven method for revealing the subject of scientific and research documents to both human readers and information retrieval systems, such as search and knowledge man-agement engines. However, scientific documents that are manually annotated with keyphrases are in the minority. This paper describes a new machine learn-ing-based automatic keyphrase annotation method for scientific documents, which utilizes Wikipedia as a thesaurus for candidate selection from docu-ments’ content and deploys genetic algorithms to learn a model for ranking and filtering the most probable keyphrases. We have evaluated the performance of this method on a third-party dataset of research papers. 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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- 61 abstract "Topical annotation of documents with keyphrases is a proven method for revealing the subject of scientific and research documents to both human readers and information retrieval systems, such as search and knowledge man-agement engines. However, scientific documents that are manually annotated with keyphrases are in the minority. This paper describes a new machine learn-ing-based automatic keyphrase annotation method for scientific documents, which utilizes Wikipedia as a thesaurus for candidate selection from docu-ments’ content and deploys genetic algorithms to learn a model for ranking and filtering the most probable keyphrases. We have evaluated the performance of this method on a third-party dataset of research papers. 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.".