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ABSTRACT: Automatic keyphrase annotation of scientific documents using Wikipedia and genetic algorithms

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.

via Automatic keyphrase annotation of scientific documents using Wikipedia and genetic algorithms.

Brian S McGowan, PhD

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Brian is a research scientist and educational technologist. He helped transform Pfizer’s Medical Education Group and previously served in educational leadership roles at HealthAnswers, Inc.; Acumentis, LLC.; Cephalon; and Wyeth. He taught graduate medical education programs at Arcadia University for 10 years. Dr. McGowan recently authored the book "#socialQI: Simple Solutions for Improving Your Healthcare" and has been invited to speak internationally on the subject of information flow, technology, and learning in healthcare.

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