Evolutionary algorithms for definition extraction

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.