Topic
Chart parser
About: Chart parser is a research topic. Over the lifetime, 154 publications have been published within this topic receiving 3551 citations.
Papers published on a yearly basis
Papers
TL;DR: In this article, a parsing algorithm which seems to be the most efficient general context-free algorithm known is described, which is similar to both Knuth's LR(k) algorithm and the familiar top-down algorithm.
Abstract: A parsing algorithm which seems to be the most efficient general context-free algorithm known is described. It is similar to both Knuth's LR(k) algorithm and the familiar top-down algorithm. It has a time bound proportional to n3 (where n is the length of the string being parsed) in general; it has an n2 bound for unambiguous grammars; and it runs in linear time on a large class of grammars, which seems to include most practical context-free programming language grammars. In an empirical comparison it appears to be superior to the top-down and bottom-up algorithms studied by Griffiths and Petrick.
1,762 citations
Proceedings Article•
1 Oct 2001TL;DR: In this article, the authors present a view of parsing as directed hypergraph analysis, which naturally covers both symbolic and probabilistic parsing, and illustrate the approach by showing how a dynamic extension of Dijkstra's algorithm can be used to construct a Probabilistic chart parser with an O(n3) time bound for arbitrary PCFGs.
Abstract: While symbolic parsers can be viewed as deduction systems, this view is less natural for probabilistic parsers. We present a view of parsing as directed hypergraph analysis, which naturally covers both symbolic and probabilistic parsing. We illustrate the approach by showing how a dynamic extension of Dijkstra's algorithm can be used to construct a probabilistic chart parser with an O(n3) time bound for arbitrary PCFGs, while preserving as much of the flexibility of symbolic chart parsers as is allowed by the inherent ordering of probabilistic dependencies.
148 citations
9 Apr 1991
TL;DR: Pear Pearl as discussed by the authors is a time-asynchronous bottom-up chart parser with Earley-type top-down prediction which pursues the highest scoring theory in the chart, where the score of a theory represents the extent to which the context of the sentence predicts that interpretation.
Abstract: This paper describes a natural language parsing algorithm for unrestricted text which uses a probability-based scoring function to select the "best" parse of a sentence. The parser, Pearl, is a time-asynchronous bottom-up chart parser with Earley-type top-down prediction which pursues the highest-scoring theory in the chart, where the score of a theory represents the extent to which the context of the sentence predicts that interpretation. This parser differs from previous attempts at stochastic parsers in that it uses a richer form of conditional probabilities based on context to predict likelihood. Pearl also provides a framework for incorporating the results of previous work in part-of-speech assignment, unknown word models, and other probabilistic models of linguistic features into one parsing tool, interleaving these techniques instead of using the traditional pipeline architecture. In preliminary tests, Pearl has been successful at resolving part-of-speech and word (in speech processing) ambiguity, determining categories for unknown words, and selecting correct parses first using a very loosely fitting covering grammar.
96 citations
13 Feb 1991
TL;DR: A natural language parsing algorithm for unrestricted text which uses a probability-based scoring function to select the "best" parse of a sentence and provides a framework for incorporating the results of previous work in part-of-speech assignment, unknown word models, and other probabilistic models of linguistic features into one parsing tool, interleaving these techniques instead of using the traditional pipeline architecture.
Abstract: This paper describes a natural language parsing algorithm for unrestricted text which uses a probability-based scoring function to select the “best” parse of a sentence. The parser, Pearl, is a time-asynchronous bottom-up chart parser with Earley-type top-down prediction which pursues the highest-scoring theory in the chart, where the score of a theory represents the extent to which the context of the sentence predicts that interpretation. This parser differs from previous attempts at stochastic parsers in that it uses a richer form of conditional probabilities based on context to predict likelihood. Pearl also provides a framework for incorporating the results of previous work in part-of-speech assignment, unknown word models, and other probabilistic models of linguistic features into one parsing tool, interleaving these techniques instead of using the traditional pipeline architecture. In preliminary tests, Pearl has been successful at resolving part-of-speech and word (in speech processing) ambiguity, determining categories for unknown words, and selecting correct parses first using a very loosely fitting covering grammar.
87 citations
TL;DR: This paper proposes a sequential coupling of a hidden Markov model (HMM) recognizer for offline handwritten English sentences with a probabilistic bottom-up chart parser using stochastic context-free grammars extracted from a text corpus and concludes that syntax analysis helps to improve recognition rates significantly.
Abstract: This paper proposes a sequential coupling of a hidden Markov model (HMM) recognizer for offline handwritten English sentences with a probabilistic bottom-up chart parser using stochastic context-free grammars (SCFG) extracted from a text corpus. Based on extensive experiments, we conclude that syntax analysis helps to improve recognition rates significantly.
73 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2021 | 1 |
| 2019 | 1 |
| 2018 | 3 |
| 2017 | 3 |
| 2016 | 1 |
| 2015 | 5 |