The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Don't count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors
Open accessAbstract
Context-predicting models (more commonly known as embeddings or neural language models) are the new kids on the distributional semantics block.Despite the buzz surrounding these models, the literature is still lacking a systematic comparison of the predictive models with classic, count-vector-based distributional semantic approaches.In this paper, we perform such an extensive evaluation, on a wide range of lexical semantics tasks and across many parameter settings.The results, to our own surprise, show that the buzz is fully justified, as the context-predicting models obtain a thorough and resounding victory against their count-based counterparts.
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Cites
Golub, G. (2012) · doi:10.56021/9781421407944 · Cited by 34,505
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
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
Topics in semantic representation
Griffiths, T. L., Steyvers, M., & Tenenbaum, J. B. (2007) · Psychological Review · doi:10.1037/0033-295x.114.2.211 · Cited by 1,122
Cited by
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
Language Models as Knowledge Bases?
Petroni, F., Rocktäschel, T., Riedel, S., Lewis, P., Bakhtin, A., Wu, Y., & Miller, A. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1250 · Cited by 1,834
Document Modeling with Gated Recurrent Neural Network for Sentiment Classification
Tang, D., Qin, B., & Liu, T. (2015) · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d15-1167 · Cited by 1,519
Improving Distributional Similarity with Lessons Learned from Word Embeddings
Levy, O., Goldberg, Y., & Dagan, I. (2015) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00134 · Cited by 1,308
Learning Distributed Representations of Sentences from Unlabelled Data
Hill, F., Cho, K., & Korhonen, 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/n16-1162 · Cited by 566
Metadata
- Authors
- Baroni, M., Dinu, G., & Kruszewski, G.
- Venue
- Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2014
- Pages
- 238-247
- OpenAlex
- W2251803266
- Cited by
- 1,364
- References
- 5
- FWCI
- 96.45
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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- Baroni, M., Dinu, G., & Kruszewski, G. (2014). Don't count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). https://doi.org/10.3115/v1/p14-1023
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