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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)
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“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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“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
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
Harris, Z. S. (1954) · WORD · doi:10.1080/00437956.1954.11659520 · Cited by 4,807
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
Generating Sequences With Recurrent Neural Networks
Graves, A. (2013) · arXiv · doi:10.48550/arxiv.1308.0850 · Cited by 2,894
Producing high-dimensional semantic spaces from lexical co-occurrence
Lund, K., & Burgess, C. (1996) · Behavior Research Methods, Instruments, & Computers · doi:10.3758/bf03204766 · Cited by 1,875
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
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Hedderich, M. A., Lange, L., Adel, H., Strötgen, J., & Klakow, D. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.201 · Cited by 310
BERT applications in natural language processing: a review
Gardazi, N. M., Daud, A. Y., Malik, M. K., Bukhari, A. A., Alsahfi, T., & Alshemaimri, B. K. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11162-5 · Cited by 354
Data-driven materials research enabled by natural language processing and information extraction
Olivetti, E. A., Cole, J. M., Kim, E., Kononova, O., Ceder, G., Han, T. Y.-J., & Hiszpanski, A. M. (2020) · Applied Physics Reviews · doi:10.1063/5.0021106 · Cited by 302
When and Why Are Pre-Trained Word Embeddings Useful for Neural Machine Translation?
Ye, Q., Sachan, D. S., Felix, M., Padmanabhan, S. J., & Neubig, G. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-2084 · Cited by 319
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Virtanen, A., Kanerva, J., Ilo, R., Luoma, J., Luotolahti, J., Salakoski, T., Ginter, F., & Pyysalo, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.07076 · Cited by 441
Trotzek, M., Koitka, S., & Friedrich, C. M. (2018) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2018.2885515 · Cited by 312
Distributional Semantics and Linguistic Theory
Boleda, G. (2019) · Annual Review of Linguistics · doi:10.1146/annurev-linguistics-011619-030303 · Cited by 328
Salcedo-Sanz, S., Ghamisi, P., Piles, M., Werner, M., Cuadra, L., Moreno‐Martínez, Á., Izquierdo-Verdiguier, E., Muñoz-Marí, J., Mosavi, A. H., & Camps‐Valls, G. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.07.004 · Cited by 293
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Hu, D. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-29513-4_31 · Cited by 296
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Alvarez-Melis, D., & Jaakkola, T. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1214 · Cited by 287
Nguyen (2022) · doi:10.1145/3453476 · Cited by 286
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Akbik, A., Bergmann, T., & Vollgraf, R. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1078 · Cited by 292
Chandrasekharan, E., Samory, M., Jhaver, S., Charvat, H., Bruckman, A., Lampe, C., Eisenstein, J., & Gilbert, E. (2018) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3274301 · Cited by 282
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Jwa, H.-J., Oh, D., Park, K., Kang, J., & Lim, H. (2019) · Applied Sciences · doi:10.3390/app9194062 · Cited by 286
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Sun, Z., Zhang, Q., Hu, W. P., Wang, C., Chen, M., Akrami, F., & Li, C. (2020) · Proceedings of the VLDB Endowment · doi:10.14778/3407790.3407828 · Cited by 304
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Xu, K., Wang, L., Yu, M., Feng, Y., Song, Y., Wang, Z., & Yu, D. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1304 · Cited by 272
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Cerda, P., Varoquaux, G. P., & Kégl, B. (2018) · Machine Learning · doi:10.1007/s10994-018-5724-2 · Cited by 271
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Wu, Z., Ying, C., Zhao, F., Fan, Z., Dai, X., & Xia, R. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.234 · Cited by 276
Yang, X., Yumer, E., Asente, P., Kraley, M., Kifer, D., & Giles, C. L. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.462 · Cited by 275
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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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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