The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
context2vec: Learning Generic Context Embedding with Bidirectional LSTM
Open accessAbstract
Context representations are central to various NLP tasks, such as word sense disambiguation, named entity recognition, coreference resolution, and many more.In this work we present a neural model for efficiently learning a generic context embedding function from large corpora, using bidirectional LSTM.With a very simple application of our context representations, we manage to surpass or nearly reach state-of-the-art results on sentence completion, lexical substitution and word sense disambiguation tasks, while substantially outperforming the popular context representation of averaged word embeddings.We release our code and pretrained models, suggesting they could be useful in a wide variety of NLP tasks.
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Cites
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Glove: Global Vectors for Word Representation
Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071
Efficient Estimation of Word Representations in Vector Space
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1301.3781 · Cited by 26,568
Distributed Representations of Words and Phrases and their Compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533
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
Neural Architectures for Named Entity Recognition
Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., & Dyer, C. (2016) · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/n16-1030 · Cited by 4,401
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Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions
Socher, R., Pennington, J., Huang, E., Ng, A. Y., & Manning, C. D. (2011) · Cited by 1,179
Improving Word Representations via Global Context and Multiple Word Prototypes
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Cited by
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325
Deep Contextualized Word Representations
Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1202 · Cited by 11,983
On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445922 · Cited by 6,988
SpanBERT: Improving Pre-training by Representing and Predicting Spans
Joshi, M., Chen, D., Liu, Y., Weld, D. S., Zettlemoyer, L., & Levy, O. (2020) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00300 · Cited by 1,827
Metadata
- Authors
- Melamud, O., Goldberger, J., & Dagan, I.
- Venue
- Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning
- Published
- 2016
- Pages
- 51-61
- OpenAlex
- W2507974895
- Cited by
- 513
- References
- 27
- FWCI
- 35.21
- Type
- conference paper
- Language
- en
- Open access
- gold
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- Melamud, O., Goldberger, J., & Dagan, I. (2016). context2vec: Learning Generic Context Embedding with Bidirectional LSTM. Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning. https://doi.org/10.18653/v1/k16-1006
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