The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Contrastive estimation: training log-linear models on unlabeled data
Open accessAbstract
Conditional random fields (Lafferty et al., 2001) are quite effective at sequence labeling tasks like shallow parsing (Sha and Pereira, 2003) and named-entity extraction (McCallum and Li, 2003). CRFs are log-linear, allowing the incorporation of arbitrary features into the model. To train on unlabeled data, we require unsupervised estimation methods for log-linear models; few exist. We describe a novel approach, contrastive estimation. We show that the new technique can be intuitively understood as exploiting implicit negative evidence and is computationally efficient. Applied to a sequence labeling problem---POS tagging given a tagging dictionary and unlabeled text---contrastive estimation outperforms EM (with the same feature set), is more robust to degradations of the dictionary, and can largely recover by modeling additional features.
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Cites
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
Lafferty, J., McCallum, A., & Pereira, F. C. N. (2001) · ScholarlyCommons (University of Pennsylvania) · Cited by 12,935
On the limited memory BFGS method for large scale optimization
Liu, D. C., & Nocedal, J. (1989) · Mathematical Programming · doi:10.1007/bf01589116 · Cited by 8,560
Training Products of Experts by Minimizing Contrastive Divergence
Hinton, G. E. (2002) · Neural Computation · doi:10.1162/089976602760128018 · Cited by 4,879 · cited 1×
“Not to be confused withcontrastive divergence minimization (Hinton, 2003), a technique for training products of experts.”
BackgroundMaximum likelihood estimation from incomplete data via the EM algorithm
Dempster, A. P. (1977) · Journal of the Royal Statistical Society Series A (Statistics in Society) · Cited by 4,543
Unsupervised word sense disambiguation rivaling supervised methods
Yarowsky, D. (1995) · Proceedings of the 33rd annual meeting on Association for Computational Linguistics - · doi:10.3115/981658.981684 · Cited by 2,385
On the algorithmic implementation of multiclass kernel-based vector machines
Crammer, K., & Singer, Y. (2002) · Cited by 1,887
Shallow parsing with conditional random fields
Sha, F., & Pereira, F. (2003) · Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - NAACL '03 · doi:10.3115/1073445.1073473 · Cited by 1,246
McCallum, A., & Li, W. (2003) · Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - · doi:10.3115/1119176.1119206 · Cited by 1,160
On the algorithmic implementation of multiclass kernel-based vector machines
CrammerKoby, & SingerYoram (2002) · Journal of Machine Learning Research · doi:10.5555/944790.944813 · Cited by 874
Eugene Charniak, D. (1994) · Medical Entomology and Zoology · Cited by 811
Cited by
Natural Language Processing (almost) from Scratch
Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., & Kuksa, P. P. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1103.0398 · Cited by 4,962
ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
Clark, K., Luong, M.-T., Le, Q. V., & Manning, C. D. (2020) · arXiv · doi:10.48550/arxiv.2003.10555 · Cited by 1,844
A Universal Part-of-Speech Tagset
Petrov, S., Das, D., & McDonald, R. (2012) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/3pjyez8kmkhz · Cited by 722
Unsupervised Neural Machine Translation
Artetxe, M., Labaka, G., Agirre, E., & Cho, K. (2017) · arXiv · doi:10.48550/arxiv.1710.11041 · Cited by 632
GeDi: Generative Discriminator Guided Sequence Generation
Krause, B., Gotmare, A. D., McCann, B., Keskar, N. S., Joty, S., Socher, R., & Rajani, N. F. (2021) · Findings of the Association for Computational Linguistics: EMNLP 2021 · doi:10.18653/v1/2021.findings-emnlp.424 · Cited by 220
Metadata
- Authors
- Smith, N. A., & Eisner, J.
- Venue
- Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics - ACL '05
- Published
- 2005
- Pages
- 354-362
- OpenAlex
- W2137807925
- Cited by
- 305
- References
- 24
- FWCI
- 10.02
- Type
- conference paper
- Language
- en
- Open access
- bronze
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