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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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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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