A SNoW based supertagger with application to NP chunking

Abstract

Supertagging is the tagging process of assigning the correct elementary tree of LTAG, or the correct supertag, to each word of an input sentence. In this paper we propose to use supertags to expose syntactic dependencies which are unavailable with POS tags. We first propose a novel method of applying Sparse Network of Winnow (SNoW) to sequential models. Then we use it to construct a supertagger that uses long distance syntactical dependencies, and the supertagger achieves an accuracy of 92.41%. We apply the supertagger to NP chunking. The use of supertags in NP chunking gives rise to almost absolute increase (from 92.03% to 92.95%) in F-score under Transformation Based Learning(TBL) frame. The surpertagger described here provides an effective and efficient way to exploit syntactic information.