A statistical parser designed by Eugene Charniak of Brown University widely used in NLP. It originates from A "Maximum-Entropy-Inspired Parser" Proceedings of NAACL-2000. It was later improved with Mark Johnson in the paper Coarse-to-Fine n-Best Parsing and MaxEnt Discriminative Reranking, Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL 2005)
The code is currently maintained at GitHub.
From the NAACL 2000 paper 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 ''standard'' sections of the Wall Street Journal tree-bank. This represents a 15% decrease in error rate over the best single-parser results on this corpus. The major technical innovation in this parser is the use of a ``maximum-entropy-inspired'' model for conditioning and smoothing that allowed 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."
Source: A Maximum-Entropy-Inspired Parser
From the ACL 2005 paper abstract:
"Discriminative reranking is one method for constructing high-performance statistical parsers (Collins, 2000). A discriminative reranker requires a source of candidate parses for each sentence. This paper describes a simple yet novel method for constructing sets of 50-best parses based on a coarse-to-fine generative parser (Charniak, 2000). This method generates 50-best lists that are of substantially higher quality than previously obtainable. We used these parses as the input to a MaxEnt reranker (Johnson et al., 1999; Riezler et al., 2002) that selects the best parse from the set of parses for each sentence, obtaining an f-score of 91.0% on sentences of length 100 or less."
Source: Coarse-to-Fine n-Best Parsing and MaxEnt Discriminative Reranking
