Empirical learning of natural language processing tasks

Abstract

Language learning has thus far not been a hot application for machine-learning (ML) research. This limited attention for work on empirical learning of language knowledge and behaviour from text and speech data seems unjustified. After all, it is becoming apparent that empirical learning of Natural Language Processing (NLP) can alleviate NLP's all-time main problem, viz. the knowledge acquisition bottleneck: empirical ML methods such as rule induction, top down induction of decision trees, lazy learning, inductive logic programming, and some types of neural network learning, seem to be excellently suited to automatically induce exactly that knowledge that is hard to gather by hand. In this paper we address the question why NLP is an interesting application for empirical ML, and provide a brief overview of current work in this area.