Learning Semantic Grammars with Constructive Inductive Logic Programming

Abstract

Automating the construction of semantic grammars is a difficult and interesting problem for machine learning. This paper shows how the semantic-grammar acquisition problem can be viewed as the learning of search-control heuristics in a logic program. Appropriate control rules are learned using a new first-order induction algorithm that automatically invents useful syntactic and semantic categories. Empirical results show that the learned parsers generalize well to novel sentences and out-perform previous approaches based on connectionist techniques. Introduction Designing computer systems to "understand" natural language input is a difficult task. The laboriously hand-crafted computational grammars supporting natural language applications are often inefficient, incomplete and ambiguous. The difficulty in constructing adequate grammars is an example of the "knowledge acquisition bottleneck" which has motivated much research in machine learning. While numerous researchers have studied ...