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A new statistical parser based on bigram lexical dependencies
Open accessAbstract
This paper describes a new statistical parser which is based on probabilities of dependencies between head-words in the parse tree. Standard bigram probability estimation techniques are extended to calculate probabilities of dependencies between pairs of words. Tests using Wall Street Journal data show that the method performs at least as well as SPATTER (Magerman 95; Jelinek et al. 94), which has the best published results for a statistical parser on this task. The simplicity of the approach means the model trains on 40,000 sentences in under 15 minutes. With a beam search strategy parsing speed can be improved to over 200 sentences a minute with negligible loss in accuracy.
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Building a Large Annotated Corpus of English: The Penn Treebank
Marcus, M. (1993) · doi:10.21236/ada273556 · Cited by 7,405
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Web data extraction, applications and techniques: A survey
Ferrara, E., De Meo, P., Fiumara, G., & Baumgartner, R. (2014) · Knowledge-Based Systems · doi:10.1016/j.knosys.2014.07.007 · Cited by 396
Frequency of basic English grammatical structures: A corpus analysis
Roland, D., Dick, F., & Elman, J. L. (2007) · Journal of Memory and Language · doi:10.1016/j.jml.2007.03.002 · Cited by 379
Scaling question answering to the Web
Kwok, C. C. T., Etzioni, O., & Weld, D. S. (2001) · Proceedings of the 10th international conference on World Wide Web · doi:10.1145/371920.371973 · Cited by 326
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Metadata
- Authors
- Collins, M. J.
- Venue
- Proceedings of the 34th annual meeting on Association for Computational Linguistics -
- Published
- 1996
- Pages
- 184-191
- OpenAlex
- W2110882317
- Cited by
- 614
- References
- 1
- FWCI
- 38.45
- Type
- conference paper
- Language
- en
- Open access
- bronze
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