Using Inductive Logic Programming to Automate the Construction of Natural Language Parsers (1995)
Designing computer systems to understand natural language input is a difficult task. In recent years there has been considerable interest in corpus-based methods for constructing natural language parsers. These empirical approaches replace hand-crafted grammars with linguistic models acquired through automated training over language corpora. A common thread among such methods to date is the use of propositional or probablistic representations for the learned knowledge. This dissertation presents an alternative approach based on techniques from a subfield of machine learning known as inductive logic programming (ILP). ILP, which investigates the learning of relational (first-order) rules, provides an empirical method for acquiring knowledge within traditional, symbolic parsing frameworks.

This dissertation details the architecture, implementation and evaluation of CHILL a computer system for acquiring natural language parsers by training over corpora of parsed text. CHILL treats language acquisition as the learning of search-control rules within a logic program that implements a shift-reduce parser. Control rules are induced using a novel ILP algorithm which handles difficult issues arising in the induction of search-control heuristics. Both the control-rule framework and the induction algorithm are crucial to CHILL's success.

The main advantage of CHILL over propositional counterparts is its flexibility in handling varied representations. CHILL has produced parsers for various analyses including case-role mapping, detailed syntactic parse trees, and a logical form suitable for expressing first-order database queries. All of these tasks are accomplished within the same framework, using a single, general learning method that can acquire new syntactic and semantic categories for resolving ambiguities.

Experimental evidence from both aritificial and real-world corpora demonstrate that CHILL learns parsers as well or better than previous artificial neural network or probablistic approaches on comparable tasks. In the database query domain, which goes beyond the scope of previous empirical approaches, the learned parser outperforms an existing hand-crafted system. These results support the claim that ILP techniques as implemented in CHILL represent a viable alternative with significant potential advantages over neural-network, propositional, and probablistic approaches to empirical parser construction.

PhD Thesis, Department of Computer Sciences, The University of Texas at Austin.

John M. Zelle Ph.D. Alumni john zelle [at] wartburg edu