Automatic keyphrase annotation of scientific documents using Wikipedia and genetic algorithms. (June 2013)

• Record Type: Journal Article
• Authors: Joorabchi, Arash; Mahdi, Abdulhussain E.
• Is Part Of: Journal of information science. Volume 39:Number 3(2013)
• Page Start: 410
• Page End: 426
• Publication Date: 2013-06
• Subjects: genetic algorithms -- keyphrase annotation -- keyphrase indexing -- metadata generation -- scientific digital libraries -- subject metadata -- text mining -- Wikipedia; Information science -- Periodicals; Information science; Periodicals; 020.5
• DOI: 10.1177/0165551512472138
• Languages: English
• ISSNs: 0165-5515

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. This article describes a machine learning-based keyphrase annotation method for scientific documents that utilizes Wikipedia as a thesaurus for candidate selection from documents' content. We have devised a set of 20 statistical, positional and semantical features for candidate phrases to capture and reflect various properties of those candidates that have the highest keyphraseness probability. We first introduce a simple unsupervised method for ranking and filtering the most probable keyphrases, and then evolve it into a novel supervised method using genetic algorithms. We have evaluated the performance of both methods on a third-party dataset of research papers. Reported experimental results show that the performance of our proposed methods, measured in terms of consistency with human annotators, is on a par with that achieved by humans and outperforms rival supervised and unsupervised methods.