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Linear Algebraic Structure of Word Senses, with Applications to Polysemy
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Word embeddings are ubiquitous in NLP and information retrieval, but it is unclear what they represent when the word is polysemous. Here it is shown that multiple word senses reside in linear superposition within the word embedding and simple sparse coding can recover vectors that approximately capture the senses. The success of our approach, which applies to several embedding methods, is mathematically explained using a variant of the random walk on discourses model (Arora et al., 2016). A novel aspect of our technique is that each extracted word sense is accompanied by one of about 2000 “discourse atoms” that gives a succinct description of which other words co-occur with that word sense. Discourse atoms can be of independent interest, and make the method potentially more useful. Empirical tests are used to verify and support the theory.
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“Those papers conceive of documents being a "random walk" in a discourse space, where words are more likely to follow other words if they are closer to them in an embedding space.”
From Embedding Regression: Models for Context-Specific Description and Inference (2023) · cites this paper as Arora et al. 2018
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“Crucially for ALC, Arora et al. (2018) also proves that under this model, a particular relationship will follow for the embedding of a word and the embeddings of the words that appear in the contexts around it.”
From Embedding Regression: Models for Context-Specific Description and Inference (2023) · cites this paper as Arora et al. 2018
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“That A serves to downweight the contributions of very common (but uninformative) words when averaging.”
From Embedding Regression: Models for Context-Specific Description and Inference (2023) · cites this paper as Arora et al. 2018
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“The main result from Arora et al. (2018) shows the following: if the random walk model holds, the researcher can obtain an embedding for word w (e.g., "bill") by taking the average of the embeddings of the words around w (u w Þ and multiplying them by a particular square matrix A .”
From Embedding Regression: Models for Context-Specific Description and Inference (2023) · cites this paper as Arora et al. 2018
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“For example, embedding representations can be used to solve analogy reasoning tasks, implying the vectors do indeed capture relational meaning between words (e.g., Arora et al. 2018;Mikolov et al. 2013).”
From Embedding Regression: Models for Context-Specific Description and Inference (2023) · cites this paper as Arora et al. 2018
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Jakubowski, A., Gašić, M., & Zibrowius, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.09413 · Cited by 6
Beyond the Benchmarks: Toward Human-Like Lexical Representations
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Metadata
- Authors
- Arora, S., Li, Y., Liang, Y., Ma, T., & Risteski, A.
- Affiliations
- Princeton University (US)
- Venue
- Transactions of the Association for Computational Linguistics
- Publisher
- MIT Press
- Published
- 2018-12
- Volume
- 6
- Pages
- 483-495
- ISSN
- 2307-387X (ISSN-L)
- OpenAlex
- W2295508206
- Cited by
- 186
- References
- 31
- Citations by year
- 2026: 6 · 2025: 14 · 2024: 9 · 2023: 13 · 2022: 14 · 2021: 26 · 2020: 32 · 2019: 30 · 2018: 23
- FWCI
- 15.53
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- Transactions of the Association for Computational Linguistics (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1527371, 1317308)
- Office of Naval Research (ONR-N00014, N00014)
- 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 › Sentiment Analysis and Opinion Mining
- Keywords
- word embeddings, polysemy, word senses, linear superposition, sparse coding, co-occurrence
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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