Paper: A Maximum-Entropy-Inspired Parser

ACL ID A00-2018
Title A Maximum-Entropy-Inspired Parser
Venue Annual Conference of the North American Chapter of the Association for Computational Linguistics
Session Main Conference
Year 2000
Authors

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 ...