Statistical decision-tree models for parsing
Open accessAbstract
Syntactic natural language parsers have shown themselves to be inadequate for processing highly-ambiguous large-vocabulary text, as is evidenced by their poor performance on domains like the Wall Street Journal, and by the movement away from parsing-based approaches to text-processing in general. In this paper, I describe SPATTER, a statistical parser based on decision-tree learning techniques which constructs a complete parse for every sentence and achieves accuracy rates far better than any published result. This work is based on the following premises: (1) grammars are too complex and detailed to develop manually for most interesting domains; (2) parsing models must rely heavily on lexical and contextual information to analyze sentences accurately; and (3) existing n-gram modeling techniques are inadequate for parsing models. In experiments comparing SPATTER with IBM's computer manuals parser, SPATTER significantly outperforms the grammar-based parser. Evaluating SPATTER against the Penn Treebank Wall Street Journal corpus using the PARSEVAL measures, SPATTER achieves 86% precision, 86% recall, and 1.3 crossing brackets per sentence for sentences of 40 words or less, and 91% precision, 90% recall, and 0.5 crossing brackets for sentences between 10 and 20 words in length.
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The MMSE, ACE, and MoCA are related screenings for conditions such as MCI, dementia, and Alzheimer's disease.
“Related screenings include the mini-mental state examination (MMSE) [57], Addenbrooke’s Cognitive Examination (ACE) [58], and the Montreal Cognitive Assessment (MoCA) [59] for evaluating conditions like mild cognitive impairment (MCI), dementia, and Alzheimer’s disease (AD).”
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Classification and Regression Trees.
Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,834 · see citing works
Classification and Regression Trees.
Ryzin, J. V., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1986) · Journal of the American Statistical Association · doi:10.2307/2288003 · Cited by 21,028 · see citing works
(1992) · doi:10.5555/176313.176316 · Cited by 2,892 · see citing works
Baum, L. E. (1972) · Medical Entomology and Zoology · Cited by 1,411 · see citing works
Procedure for quantitatively comparing the syntactic coverage of English grammars
Klavans, J. L., Liberman, M., Marcus, M. P., Roukos, S., Santorini, B., Strzalkowski, T., Abney, S. P., Flickenger, S., Gdaniec, C., Grishman, C., et al. (1991) · DARPA Workshop on Speech and Natural Language (HLT) · doi:10.3115/112405.112467 · Cited by 432 · see citing works
A tree-based statistical language model for natural language speech recognition
Bahl, L. R., Brown, P. F., de Souza, P. V., et al. (1989) · IEEE Transactions on Acoustics Speech and Signal Processing · doi:10.1109/29.32278 · Cited by 259 · see citing works
Natural Language Parsing as Statistical Pattern RecognitionPreprint
Magerman, D. M. (1994) · arXiv (Cornell University) · doi:10.48550/arxiv.cmp-lg/9405009 · Cited by 222 · see citing works
Self-citationStatistically-driven computer grammars of English : the IBM/LANCASTER approach
Black, E. W., Garside, R., Leech, G., et al. (1993) · Rodopi eBooks · Cited by 72 · see citing works
A Tree-Based Statistical Language Model for Natural Language Speech Recognition
Bahl, L. R., Brown, P. F., de Souza, P. V., et al. (1990) · Elsevier eBooks · doi:10.1016/b978-0-08-051584-7.50046-2 · Cited by 35 · see citing works
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Roark, B., Mitchell, M., Hosom, J.-P., Hollingshead, K., & Kaye, J. (2011) · IEEE Transactions on Audio, Speech, and Language Processing · doi:10.1109/tasl.2011.2112351 · Cited by 355 · see citing works
Naseem, U., Razzak, I., Khan, S. K., & Prasad, M. (2021) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3434237 · Cited by 194 · see citing works
A Review of Automated Speech and Language Features for Assessment of Cognitive and Thought Disorders
Voleti, R., Liss, J. M., & Berisha, V. (2020) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2019.2952087 · Cited by 96 · see citing works
- Authors
- Magerman, D. M.
- Corresponding author
- Magerman, David M.
- Venue
- Proceedings of the 33rd annual meeting on Association for Computational Linguistics -
- Published
- 1995
- Pages
- 276-283
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W2153439141
- Cited by
- 567
- References
- 9
- Citations by year
- 2026: 1 · 2025: 6 · 2024: 6 · 2023: 9 · 2022: 10 · 2021: 13 · 2020: 16 · 2019: 18 · 2018: 6 · 2017: 12 · 2016: 13 · 2015: 17 · 2014: 16 · 2013: 9 · 2012: 21
- FWCI
- 27.62
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- OLAC - Open Language Archives Community (submittedVersion)
- Americanae (AECID Library) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Advanced Research Projects Agency
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Social Sciences › Arts and Humanities › Language and Linguistics › Syntax, Semantics, Linguistic Variation
- Keywords
- statistical parsing, decision tree induction, spatter, syntactic analysis, Wall Street Journal corpus, Penn Treebank, n-gram language models, grammar-based parsing, parsing accuracy
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