Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/25279
Title: Evolutionary algorithms for definition extraction
Authors: Borg, Claudia
Rosner, Mike
Pace, Gordon J.
Keywords: Genetic algorithms
Genetic programming (Computer science)
Rule-based programming
Web-based instruction -- Design
Issue Date: 2009
Publisher: Association for Computational Linguistics
Citation: Borg, C., Rosner, M., & Pace, G. (2009). Evolutionary algorithms for definition extraction. 1st Workshop on Definition Extraction, Borovets. 26-32.
Abstract: Books and other text-based learning material contain implicit information which can aid the learner but which usually can only be accessed through a semantic analysis of the text. Definitions of new concepts appearing in the text are one such instance. If extracted and presented to the learner in form of a glossary, they can provide an excellent reference for the study of the main text. One way of extracting definitions is by reading through the text and annotating definitions manually — a tedious and boring job. In this paper, we explore the use of machine learning to extract definitions from non-technical texts, reducing human expert input to a minimum. We report on experiments we have conducted on the use of genetic programming to learn the typical linguistic forms of definitions and a genetic algorithm to learn the relative importance of these forms. Results are very positive, showing the feasibility of exploring further the use of these techniques in definition extraction. The genetic program is able to learn similar rules derived by a human linguistic expert, and the genetic algorithm is able to rank candidate definitions in an order of confidence.
URI: https://www.um.edu.mt/library/oar//handle/123456789/25279
ISBN: 9789544520137
Appears in Collections:Scholarly Works - FacICTCS

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