The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Learning accurate, compact, and interpretable tree annotation
Open accessAbstract
We present an automatic approach to tree annotation in which basic nonterminal symbols are alternately split and merged to maximize the likelihood of a training treebank. Starting with a simple X-bar grammar, we learn a new grammar whose nonterminals are subsymbols of the original nonterminals. In contrast with previous work, we are able to split various terminals to different degrees, as appropriate to the actual complexity in the data. Our grammars automatically learn the kinds of linguistic distinctions exhibited in previous work on manual tree annotation. On the other hand, our grammars are much more compact and substantially more accurate than previous work on automatic annotation. Despite its simplicity, our best grammar achieves an F1 of 90.2% on the Penn Treebank, higher than fully lexicalized systems.
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“In contrast to RNN sequence-to-sequence models [37], the Transformer outperforms the BerkeleyParser [29] even when training only on the WSJ training set of 40K sentences.”
From Attention Is All You Need (2017) · cites this paper as [29]
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Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
“In contrast to RNN sequence-to-sequence models [37], the Transformer outperforms the BerkeleyParser [29] even when training only on the WSJ training set of 40K sentences.”
MethodRecent Trends in Deep Learning Based Natural Language Processing [Review Article]
Young, T., Hazarika, D., Poria, S., & Cambria, E. (2018) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2018.2840738 · Cited by 2,921
Recurrent Convolutional Neural Networks for Text Classification
Lai, S., Xu, L., Liu, K., & Zhao, J. (2015) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v29i1.9513 · Cited by 2,384
(2011) · Cited by 1,192
Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions
Socher, R., Pennington, J., Huang, E., Ng, A. Y., & Manning, C. D. (2011) · Cited by 1,181
Metadata
- Authors
- Petrov, S., Barrett, L., Thibaux, R., & Klein, D.
- Affiliations
- University of California, Berkeley (US)
- Venue
- Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the ACL - ACL '06
- Published
- 2006
- Pages
- 433-440
- ISSN
- 1525-2477 (ISSN-L)
- OpenAlex
- W2139621418
- Cited by
- 793
- References
- 18
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 4 · 2023: 9 · 2022: 7 · 2021: 16 · 2020: 22 · 2019: 24 · 2018: 33 · 2017: 36 · 2016: 72 · 2015: 80 · 2014: 87 · 2013: 91 · 2012: 103
- FWCI
- 18.21
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Conference on Computational Linguistics (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Social Sciences › Arts and Humanities › Language and Linguistics › Syntax, Semantics, Linguistic Variation
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Keywords
- tree annotation, grammar learning, Penn Treebank, lexicalization
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