A Maximum-Entropy-Inspired Parser

Abstract

We present a new parser for parsing down to Penn tree-bank style parse trees that achieves 90.1 % average precision/recall for sentences of length 40 and less, and 89.5 % for sentences of length 100 and less when trMned and tested on the previously established [5,9,10,15,17] "stan-dard " sections of the Wall Street Journal tree-bank. This represents a 13 % decrease in er-ror rate over the best single-parser results on this corpus [9]. The major technical innova-tion is tire use of a "ma~ximum-entropy-inspired" model for conditioning and smoothing that let us successfully to test and combine many differ-ent conditioning events. We also present some partial results showing the effects of different conditioning information, including a surpris-ing 2 % improvement due to guessing the lexical head's pre-terminal before guessing the lexical head. 1