A Maximum-Entropy-Inspired Parser

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A Maximum-Entropy-Inspired Parser (1999)

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by Eugene Charniak

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971 - 19 self

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@INPROCEEDINGS{Charniak99amaximum-entropy-inspired,
    author = {Eugene Charniak},
    title = {A Maximum-Entropy-Inspired Parser},
    booktitle = {},
    year = {1999},
    pages = {132--139}
}

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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 trained 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 error rate over the best single-parser results on this corpus [9]. The major technical innova- tion is the use of a "maximum-entropy-inspired" model for conditioning and smoothing that let us successfully to test and combine many different conditioning events. We also present some partial results showing the effects of different conditioning information, including a surprising 2% improvement due to guessing the lexical head's pre-terminal before guessing the lexical head.

Keyphrases

maximum-entropy-inspired parser lexical head wall street journal tree bank maximum-entropy-inspired model tree-bank style parse tree dard section partial result new parser average precision recall error rate single-parser result major technical innova tion

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