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HLT '90 Proceedings of the workshop on Speech and Natural Language
Statistical parsing of messages
HLT '90 Proceedings of the workshop on Speech and Natural Language
Deducing linguistic structure from the statistics of large corpora
HLT '90 Proceedings of the workshop on Speech and Natural Language
Poor estimates of context are worse than none
HLT '90 Proceedings of the workshop on Speech and Natural Language
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HLT '90 Proceedings of the workshop on Speech and Natural Language
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ANLC '88 Proceedings of the second conference on Applied natural language processing
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Computational Linguistics
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EACL '93 Proceedings of the sixth conference on European chapter of the Association for Computational Linguistics
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ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
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ACL '92 Proceedings of the 30th annual meeting on Association for Computational Linguistics
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ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
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HLT '91 Proceedings of the workshop on Speech and Natural Language
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HLT '94 Proceedings of the workshop on Human Language Technology
History-Based Inside-Outside Algorithm
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
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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.