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Foundations of statistical natural language processing
Abstract
Statistical approaches to processing natural language text have become dominant in recent years. This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear. The book contains all the theory and algorithms needed for building NLP tools. It provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations. The book covers collocation finding, word sense disambiguation, probabilistic parsing, information retrieval, and other applications.
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Cited by
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Du, M., Li, F., Zheng, G., & Srikumar, V. (2017) · Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3133956.3134015 · Cited by 1,835
Hotho, A., Nürnberger, A., & Paaß, G. (2005) · Journal for Language Technology and Computational Linguistics · doi:10.21248/jlcl.20.2005.68 · Cited by 893
Good debt or bad debt: Detecting semantic orientations in economic texts
Malo, P., Sinha, A., Korhonen, P., Wallenius, J., & Takala, P. (2014) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.23062 · Cited by 513
Extracting relations with integrated information using kernel methods
Zhao, S., & Grishman, R. (2005) · Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics - ACL '05 · doi:10.3115/1219840.1219892 · Cited by 313
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Metadata
- Authors
- Weikum, G.
- Venue
- ACM SIGMOD Record
- Published
- 2002-09
- Volume
- 31
- Issue
- 3
- Pages
- 37-38
- ISSN
- 0163-5808 (ISSN-L)
- OpenAlex
- W2045812729
- Cited by
- 615
- References
- 0
- FWCI
- 16.39
- Type
- journal article
- Language
- en
- Open access
- closed
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- Weikum, G. (2002). Foundations of statistical natural language processing. ACM SIGMOD Record. https://doi.org/10.1145/601858.601867
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
