The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Improving Distributional Similarity with Lessons Learned from Word Embeddings
Open accessAbstract
Recent trends suggest that neural-network-inspired word embedding models outperform traditional count-based distributional models on word similarity and analogy detection tasks. We reveal that much of the performance gains of word embeddings are due to certain system design choices and hyperparameter optimizations, rather than the embedding algorithms themselves. Furthermore, we show that these modifications can be transferred to traditional distributional models, yielding similar gains. In contrast to prior reports, we observe mostly local or insignificant performance differences between the methods, with no global advantage to any single approach over the others.
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Cites
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
Indexing by latent semantic analysis
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A unified architecture for natural language processing: deep neural networks with multitask learning
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The Approximation of One Matrix by Another of Lower Rank
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Word association norms, mutual information, and lexicography
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Linguistic Regularities in Continuous Space Word Representations
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Cited by
Bag of Tricks for Efficient Text Classification
Joulin, A., Grave, E., Bojanowski, P., & Mikolov, T. (2017) · Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers · doi:10.18653/v1/e17-2068 · Cited by 4,545
ConceptNet 5.5: An Open Multilingual Graph of General Knowledge
Speer, R., Chin, J., & Havasi, C. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11164 · Cited by 2,312
A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications
Cai, H., Zheng, V. W., & Chang, K. C.-C. (2018) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2018.2807452 · Cited by 2,034
Comparing different supervised machine learning algorithms for disease prediction
Uddin, S., Khan, A., Hossain, M. E., & Moni, M. A. (2019) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-019-1004-8 · Cited by 1,627
Rasmy, L., Xiang, Y., Xie, Z., Tao, C., & Zhi, D. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00455-y · Cited by 886
Metadata
- Authors
- Levy, O., Goldberg, Y., & Dagan, I.
- Venue
- Transactions of the Association for Computational Linguistics
- Publisher
- MIT Press - Journals
- Published
- 2015-12
- Volume
- 3
- Pages
- 211-225
- ISSN
- 2307-387X (ISSN-L)
- OpenAlex
- W1615991656
- Cited by
- 1,308
- References
- 27
- FWCI
- 164.23
- Type
- journal article
- Language
- en
- Open access
- diamond
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- Levy, O., Goldberg, Y., & Dagan, I. (2015). Improving Distributional Similarity with Lessons Learned from Word Embeddings. Transactions of the Association for Computational Linguistics. https://doi.org/10.1162/tacl_a_00134
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