Structural ambiguity and lexical relations
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
Towards understanding text with a very large vocabulary
HLT '90 Proceedings of the workshop on Speech and Natural Language
A stochastic parts program and noun phrase parser for unrestricted text
ANLC '88 Proceedings of the second conference on Applied natural language processing
New figures of merit for best-first probabilistic chart parsing
Computational Linguistics
Review of "Statistical language learning" by Eugene Charniak. The MIT Press 1993.
Computational Linguistics
Structural disambiguation of morpho-syntactic categorial parsing for Korean
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Automatic compensation for parser figure-of-merit flaws
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Hi-index | 0.00 |
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 according to a given grammar. 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 tests performed on the Voyager direction-finding domain, Pearl has been successful at resolving part-of-speech ambiguity, determining categories for unknown words, and selecting correct parses first using a very loosely fitting covering grammar.