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Three new probabilistic models for dependency parsing: an exploration

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Eisner, Jason M.·1996·Proceedings of the 16th conference on Computational linguistics -

Abstract

After presenting a novel O(n3) parsing algorithm for dependency grammar, we develop three contrasting ways to stochasticize it. We propose (a) a lexical affinity model where words struggle to modify each other, (b) a sense tagging model where words fluctuate randomly in their selectional preferences, and (c) a generative model where the speaker fleshes out each word's syntactic and conceptual structure without regard to the implications for the hearer. We also give preliminary empirical results from evaluating the three models' parsing performance on annotated Wall Street Journal training text (derived from the Penn Treebank). In these results, the generative model performs significantly better than the others, and does about equally well at assigning part-of-speech tags.

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Metadata

Authors
Eisner, J. M.
Venue
Proceedings of the 16th conference on Computational linguistics -
Published
1996
Volume
1
Pages
340
ISSN
1525-2477 (ISSN-L)
OpenAlex
W2052449326
Cited by
609
References
0
FWCI
6.18
Type
conference paper
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Three new probabilistic models for dependency parsing: an exploration. https://app.cochat.ai/papers/10.3115/992628.992688
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Eisner, J. M. (1996). Three new probabilistic models for dependency parsing: an exploration. Proceedings of the 16th conference on Computational linguistics -. https://doi.org/10.3115/992628.992688

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