The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Probabilistic Top-Down Parsing and Language Modeling
Open accessAbstract
This paper describes the functioning of a broad-coverage probabilistic top-down parser, and its application to the problem of language modeling for speech recognition. The paper first introduces key notions in language modeling and probabilistic parsing, and briefly reviews some previous approaches to using syntactic structure for language modeling. A lexicalized probabilistic top-down parser is then presented, which performs very well, in terms of both the accuracy of returned parses and the efficiency with which they are found, relative to the best broad-coverage statistical parsers. A new language model that utilizes probabilistic top-down parsing is then outlined, and empirical results show that it improves upon previous work in test corpus perplexity. Interpolation with a trigram model yields an exceptional improvement relative to the improvement observed by other models, demonstrating the degree to which the information captured by our parsing model is orthogonal to that captured by a trigram model. A small recognition experiment also demonstrates the utility of the model.
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Cites
Elements of Information Theory
Cover, T. M., & Thomas, J. A. (2001) · Wiley eBooks · doi:10.1002/0471200611 · Cited by 36,808
Introduction to automata theory, languages and computation
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Foundations of statistical natural language processing
Manning, C. D., & Schütze, H. (1999) · Cited by 9,816
Compilers: Principles, Techniques, and Tools
Aho, A. V., Sethi, R., & Ullman, J. D. (1986) · Cited by 8,082
Building a Large Annotated Corpus of English: The Penn Treebank
Marcus, M. (1993) · doi:10.21236/ada273556 · Cited by 7,405
Statistical Methods for Speech Recognition
Sun, D. X., & Jelinek, F. (1999) · Journal of the American Statistical Association · doi:10.2307/2670189 · Cited by 1,946
Head-Driven Statistical Models for Natural Language Parsing
Collins, M. (2003) · Computational Linguistics · doi:10.1162/089120103322753356 · Cited by 1,826
De Roeck, A. (1995) · Handbook of Pragmatics · doi:10.1075/hop.m.comm2 · Cited by 1,715
An efficient context-free parsing algorithm
Earley, J. (1983) · Communications of the ACM · doi:10.1145/357980.358005 · Cited by 1,606
(1999) · Cited by 1,521
Cited by
Composition in Distributional Models of Semantics
Mitchell, J., & Lapata, M. (2010) · Cognitive Science · doi:10.1111/j.1551-6709.2010.01106.x · Cited by 960
Coarse-to-fine n-best parsing and MaxEnt discriminative reranking
Charniak, E., & Johnson, M. (2005) · Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics - ACL '05 · doi:10.3115/1219840.1219862 · Cited by 872
Recurrent Neural Network Grammars
Dyer, C., Kuncoro, A., Ballesteros, M., & Smith, N. A. (2016) · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/n16-1024 · Cited by 517
Speakers optimize information density through syntactic reduction
Levy, R., & Jaeger, T. F. (2007) · Advances in Neural Information Processing Systems 19 · doi:10.7551/mitpress/7503.003.0111 · Cited by 503
The ERP response to the amount of information conveyed by words in sentences
Frank, S. L., Otten, L. J., Galli, G., & Vigliocco, G. (2015) · Brain and Language · doi:10.1016/j.bandl.2014.10.006 · Cited by 381
Metadata
- Authors
- Roark, B.
- Venue
- Computational Linguistics
- Publisher
- MIT Press - Journals
- Published
- 2001-06
- Volume
- 27
- Issue
- 2
- Pages
- 249-276
- ISSN
- 0891-2017 (ISSN-L)
- 1530-9312
- OpenAlex
- W2123893795
- Cited by
- 329
- References
- 38
- FWCI
- 15.77
- Type
- journal article
- Language
- en
- Open access
- bronze
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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