The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index.
Enriching Word Vectors with Subword Information
Open accessAbstract
Continuous word representations, trained on large unlabeled corpora are useful for many natural language processing tasks. Popular models that learn such representations ignore the morphology of words, by assigning a distinct vector to each word. This is a limitation, especially for languages with large vocabularies and many rare words. In this paper, we propose a new approach based on the skipgram model, where each word is represented as a bag of character n-grams. A vector representation is associated to each character n-gram; words being represented as the sum of these representations. Our method is fast, allowing to train models on large corpora quickly and allows us to compute word representations for words that did not appear in the training data. We evaluate our word representations on nine different languages, both on word similarity and analogy tasks. By comparing to recently proposed morphological word representations, we show that our vectors achieve state-of-the-art performance on these tasks.
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“Contrary to string similarity-based approaches, continuous word representations capture not only lexical but also semantic properties of tokens.”
From A Method for the Calculation of Parallel Passages for Buddhist Chinese Sources Based on Million-scale Nearest Neighbor Search (2020) · cites this paper as Bojanowski et al. (2017)
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“We screened 4 different word embeddings following a similar procedure reported in our previous study [46] and found that the Common Crawl embeddings—released by Facebook and trained using the fastText on the Common Crawl data set [47]—achieved better performance compared to other embeddings on a validation data set.”
From Extracting Family History of Patients From Clinical Narratives: Exploring an End-to-End Solution With Deep Learning Models (2020) · cites this paper as [47]
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In the citing paper
“I use Fasttext (Bojanowski et al. 2017) as a word representation model because of its capacity to collect sub-word information, which can be helpful in the case of compound words.”
From A Method for the Calculation of Parallel Passages for Buddhist Chinese Sources Based on Million-scale Nearest Neighbor Search (2020) · cites this paper as Bojanowski et al. 2017
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“This technique involves learning large-scale word embeddings (Bojanowski et al. 2017; Joulin et al. 2016), representing an advancement from Word2Vec.”
From Large language models (LLMs): survey, technical frameworks, and future challenges (2024) · cites this paper as Bojanowski et al. 2017
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Cites
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
Self-citationDistributed 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
Self-citationThe proof and measurement of association between two things
Spearman, C. (1904) · The American Journal of Psychology · doi:10.2307/1412159 · Cited by 8,039
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Character-level Convolutional Networks for Text ClassificationPreprint
Zhang, X., Zhao, J., & LeCun, Y. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1509.01626 · Cited by 4,481
From Frequency to Meaning: Vector Space Models of Semantics
Turney, P. D., & Pantel, P. (2010) · Journal of Artificial Intelligence Research · doi:10.1613/jair.2934 · Cited by 2,949
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Graves, A. (2013) · arXiv · doi:10.48550/arxiv.1308.0850 · Cited by 2,894
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Character-Aware Neural Language Models
Kim, Y., Jernite, Y., Sontag, D. A., & Rush, A. M. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10362 · Cited by 1,590
Research works printed but not linked: 1
- Cui Qing, 2015, ACM Transactions on Information Systems, 34, 1
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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
Recent Trends in Deep Learning Based Natural Language Processing [Review Article]
Young, T., Hazarika, D., Poria, S., & Cambria, E. (2018) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2018.2840738 · Cited by 2,921
Parameter-efficient transfer learning for NLP
Houlsby, N., Giurgiu, A., Jastrzebski, S., Morrone, B., de Laroussilhe, Q., Gesmundo, A., Attariyan, M., & Gelly, S. (2019) · arXiv · doi:10.48550/arxiv.1902.00751 · Cited by 2,270
Self-supervised Learning: Generative or Contrastive
Liu, X., Zhang, F., Hou, Z., Mian, L., Wang, Z., Zhang, J., & Tang, J. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3090866 · Cited by 2,008
Alsentzer, E., Murphy, J., Boag, W., Weng, W.-H., Jindi, D., Naumann, T., & McDermott, M. (2019) · Proceedings of the 2nd Clinical Natural Language Processing Workshop · doi:10.18653/v1/w19-1909 · Cited by 1,736
Metadata
- Authors
- Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T.
- Affiliations
- Meta (United States) (US)
- Facebook AI Research,
- Corresponding author
- Bojanowski, Piotr
- Joulin, Armand
- ORCID
- Bojanowski, Piotr
- Venue
- Transactions of the Association for Computational Linguistics
- Publisher
- MIT Press
- Published
- 2017-12
- Volume
- 5
- Pages
- 135-146
- ISSN
- 2307-387X (ISSN-L)
- OpenAlex
- W2493916176
- Cited by
- 9,645
- References
- 47
- Citations by year
- 2026: 389 · 2025: 677 · 2024: 790 · 2023: 1188 · 2022: 1253 · 2021: 1691 · 2020: 1561 · 2019: 1288 · 2018: 648 · 2017: 142
- FWCI
- 593.75
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- Transactions of the Association for Computational Linguistics (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Keywords
- word embeddings, character n-grams, skip-gram model, out-of-vocabulary words, word similarity, word analogy, multilingual word representation
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