Automatic grammar rule extraction and ranking for definitions

Abstract

Learning texts contain much implicit knowledge which is ideally presented to the learner in a structured manner - a typical example being definitions of terms in the text, which would ideally be presented separately as a glossary for easy access. The problem is that manual extraction of such information can be tedious and time consuming. In this paper we describe two experiments carried out to enable the automated extraction of definitions from non-technical learning texts using evolutionary algorithms. A genetic programming approach is used to learn grammatical rules helpful in discriminating between definitions and non-definitions, after which, a genetic algorithm is used to learn the relative importance of these features, thus enabling the ranking of candidate sentences in order of confidence. The results achieved are promising, and we show that it is possible for a Genetic Program to automatically learn similar rules derived by a human linguistic expert and for a Genetic Algorithm to then give a weighted score to those rules so as to rank extracted definitions in order of confidence in an effective manner.