Automatic keyphrase annotation of scientific documents using Wikipedia and genetic algorithms

From WikiPapers
Jump to: navigation, search

Automatic keyphrase annotation of scientific documents using Wikipedia and genetic algorithms is a 2013 journal article written in English by Joorabchi A., Mahdi A.E. and published in Journal of Information Science.

[edit] 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.

[edit] References

This publication has 0 references. Only those references related to wikis are included here:

 ;

Cited by

Probably, this publication is cited by others, but there are no articles available for them in WikiPapers. Cited 4 time(s)