Adapting Stochastic Output for Rule-Based Semantics

Abstract

The current tendency in Natural Language Processing is to use statistical methods in order to build NLP applications. In this context I explore whether a stochastic LFG-like grammar for English can be used as the input to a rule-based semantic system, in the place of the original rule-based English LFG grammar. Integrating the stochastic grammar requires creating a set of ordered rewrite rules to augment and reconfigure the output of the stochastic grammar. The results are promising in that the missing features can be reconstructed to provide sufficiently rich input to the semantic component. As a result, the advantages of both sides are combined. On the one hand, one can make use of the significant time-saving effects of a stochastic grammar; on the other hand, the combined approach does not lack any of the information compared to the rule-based system.