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 nontechnical 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.
REF implemented a fully automated system to extract and rank definitions based on genetic algorithms and genetic programming.
Evolutionary algorithms for definition extraction
In Proceedings of the 1st International Workshop on Definition Extraction, RANLP-09. 18 September 2009, Borovets
Computer Science