AN EFFICIENT PARSING MODEL FOR UNIFICATION CATEGORIAL GRAMMAR WITH OBJECT-ORIENTED KNOWLEDGE REPRESENTATION AND SELECTION SETS
Li Li, Barrett R. Bryant · International Journal of Artificial Intelligence Tools · 1998
This paper describes an object-oriented lexical representation language based on Unification Categorial Grammar (UCG) that encodes linguistic and semantic information uniformly as classes and objects and an efficient bottom-up parsing method for UCG using selection sets technique. The lexical representation language, implemented in the logic and object-oriented programming language LIFE, introduces several new information sharing mechanisms to enable natural, declarative, modular and economial construction of large and complex computational lexicons. The selection sets are deduced from a transformation between UCG and Context-Free Grammar (CFG) and used to reduce search space for the table-driven algorithm. The experimental tests on a spoken English corpus show that the hierarchical lexicon achieves a dramatic reduction on redundant information and that selection sets significantly improve parsing UCG with a polynomial time complexity.