articleJan 1, 2005GOLD OA

Coarse-to-fine n -best parsing and MaxEnt discriminative reranking

Brown University

Indexed incrossref

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.

Citation impact

887
total citations
FWCI
81.37
Percentile
100%
References
21
Citations per year

Authors

2

Topics & keywords

Keywords
  • Discriminative model
  • Parsing
  • Computer science
  • Sentence
  • Artificial intelligence
  • Set (abstract data type)
  • Natural language processing
  • Generative grammar
UN Sustainable Development Goals
  • Reduced inequalities
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Funding