The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
The Penn Chinese TreeBank: Phrase structure annotation of a large corpus
Abstract
With growing interest in Chinese Language Processing, numerous NLP tools (e.g., word segmenters, part-of-speech taggers, and parsers) for Chinese have been developed all over the world. However, since no large-scale bracketed corpora are available to the public, these tools are trained on corpora with different segmentation criteria, part-of-speech tagsets and bracketing guidelines, and therefore, comparisons are difficult. As a first step towards addressing this issue, we have been preparing a large bracketed corpus since late 1998. The first two installments of the corpus, 250 thousand words of data, fully segmented, POS-tagged and syntactically bracketed, have been released to the public via LDC (www.ldc.upenn.edu). In this paper, we discuss several Chinese linguistic issues and their implications for our treebanking efforts and how we address these issues when developing our annotation guidelines. We also describe our engineering strategies to improve speed while ensuring annotation quality.
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Lectures on Government and Binding: The Pisa Lectures
Chomsky, N. (1993) · Mouton eBooks · doi:10.1515/9783110884166 · Cited by 8,541
Building a Large Annotated Corpus of English: The Penn Treebank
Marcus, M. (1993) · doi:10.21236/ada273556 · Cited by 7,405
The Proposition Bank: An Annotated Corpus of Semantic Roles
Palmer, M., Gildea, D., & Kingsbury, P. (2005) · Computational Linguistics · doi:10.1162/0891201053630264 · Cited by 2,273
Self-citationSteedman, M. (2000) · The MIT Press eBooks · doi:10.7551/mitpress/6591.001.0001 · Cited by 1,585
(1999) · Cited by 1,521
A Maximum Entropy Model for Part-Of-Speech Tagging
Ratnaparkhi, A. (1996) · Cited by 1,317
Sciullo, A.-M. D., & Williams, E. (1987) · Cited by 826
Three generative, lexicalised models for statistical parsing
Collins, M. (1997) · Proceedings of the 35th annual meeting on Association for Computational Linguistics - · doi:10.3115/976909.979620 · Cited by 743
The Penn Treebank: annotating predicate argument structure
Marcus, M., Kim, G., Marcinkiewicz, M. A., MacIntyre, R., Bies, A., Ferguson, M., Katz, K., & Schasberger, B. (1994) · Proceedings of the workshop on Human Language Technology - HLT '94 · doi:10.3115/1075812.1075835 · Cited by 742
Discriminative Reranking for Natural Language Parsing
Collins, M., & Koo, T. (2005) · Computational Linguistics · doi:10.1162/0891201053630273 · Cited by 710
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A Survey of the Usages of Deep Learning for Natural Language Processing
Otter, D. W., Medina, J. R., & Kalita, J. K. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2979670 · Cited by 1,836
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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
Dependency Distance as a Metric of Language Comprehension Difficulty
Liu, H. (2008) · Journal of Cognitive Science · doi:10.17791/jcs.2008.9.2.159 · Cited by 466
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Metadata
- Authors
- XUE, N., XIA, F., CHIOU, F.-D., & PALMER, M.
- Venue
- Natural Language Engineering
- Publisher
- Cambridge University Press (CUP)
- Published
- 2005-05-19
- Volume
- 11
- Issue
- 2
- Pages
- 207-238
- ISSN
- 1351-3249 (ISSN-L)
- 1469-8110
- OpenAlex
- W2096204319
- Cited by
- 666
- References
- 19
- FWCI
- 17.86
- Type
- journal article
- Language
- en
- Open access
- closed
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