The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Improving Lexical Embeddings with Semantic Knowledge
Open accessAbstract
Word embeddings learned on unlabeled data are a popular tool in semantics, but may not capture the desired semantics.We propose a new learning objective that incorporates both a neural language model objective (Mikolov et al., 2013) and prior knowledge from semantic resources to learn improved lexical semantic embeddings.We demonstrate that our embeddings improve over those learned solely on raw text in three settings: language modeling, measuring semantic similarity, and predicting human judgements.
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Cites
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
A unified architecture for natural language processing: deep neural networks with multitask learning
Collobert, R., & Weston, J. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390177 · Cited by 5,058
Greedy layer-wise training of deep networks
Bengio, Y., Lamblin, P., Popovici, D., & Larochelle, H. (2007) · Advances in Neural Information Processing Systems 19 · doi:10.7551/mitpress/7503.003.0024 · Cited by 4,841
Cited by
Retrofitting Word Vectors to Semantic Lexicons
Faruqui, M., Dodge, J., Jauhar, S. K., Dyer, C., Hovy, E., & Smith, N. A. (2015) · Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.3115/v1/n15-1184 · Cited by 721
From Word To Sense Embeddings: A Survey on Vector Representations of Meaning
Camacho-Collados, J., & Pilehvar, M. T. (2018) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.11259 · Cited by 363
Progress in Neural NLP: Modeling, Learning, and Reasoning
Zhou, M., Duan, N., Liu, S., & Shum, H.-Y. (2020) · Engineering · doi:10.1016/j.eng.2019.12.014 · Cited by 281
From Paraphrase Database to Compositional Paraphrase Model and Back
Wieting, J., Bansal, M., Gimpel, K., & Livescu, K. (2015) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00143 · Cited by 268
Pavlick, E., Rastogi, P., Ganitkevitch, J., Van Durme, B., & Callison-Burch, C. (2015) · Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) · doi:10.3115/v1/p15-2070 · Cited by 256
Metadata
- Authors
- Yu, M., & Dredze, M.
- Venue
- Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Published
- 2014
- Pages
- 545-550
- OpenAlex
- W2250930514
- Cited by
- 276
- References
- 3
- FWCI
- 22.93
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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- Yu, M., & Dredze, M. (2014). Improving Lexical Embeddings with Semantic Knowledge. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). https://doi.org/10.3115/v1/p14-2089
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