Work

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

Year: 2005

Type: conference-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: Annual Meeting of the Association for Computational Linguistics (ACL)

DOI: 10.3115/1219840.1219862

Authors: Eugene Charniak, Mark Johnson

Institution: Brown University

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funder: National Science Foundation

Award: Mathematical Analysis of the Compositional Structure of Images