Performance Evaluation of Supertagging for Partial Parsing

Abstract

In previous work we introduced the idea of supertagging as a means of improving the efficiency of a lexicalized grammar parser. In this paper, we present supertagging in conjunction with a lightweight dependency analyzer as a robust and efficient partial parser. The present work is significant for two reasons. First, we have vastly improved our results; 92{\%} accurate for supertag disambiguation using lexical information, larger training corpus and smoothing techniques. Second, we show how supertagging can be used for partial parsing and provide detailed evaluation results for detecting noun chunks, verb chunks, preposition phrase attachment and a variety of other linguistic constructions. Using supertag representation, we achieve a recall rate of 93.0{\%} and a precision rate of 91.8{\%} for noun chunking, improving on the best known result for noun chunking.