@article{arora-etal-2018-linear,
title = "Linear Algebraic Structure of Word Senses, with Applications to Polysemy",
author = "Arora, Sanjeev and
Li, Yuanzhi and
Liang, Yingyu and
Ma, Tengyu and
Risteski, Andrej",
editor = "Lee, Lillian and
Johnson, Mark and
Toutanova, Kristina and
Roark, Brian",
journal = "Transactions of the Association for Computational Linguistics",
volume = "6",
year = "2018",
address = "Cambridge, MA",
publisher = "MIT Press",
url = "https://aclanthology.org/Q18-1034",
doi = "10.1162/tacl_a_00034",
pages = "483--495",
abstract = "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.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="arora-etal-2018-linear">
<titleInfo>
<title>Linear Algebraic Structure of Word Senses, with Applications to Polysemy</title>
</titleInfo>
<name type="personal">
<namePart type="given">Sanjeev</namePart>
<namePart type="family">Arora</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yuanzhi</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yingyu</namePart>
<namePart type="family">Liang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tengyu</namePart>
<namePart type="family">Ma</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Andrej</namePart>
<namePart type="family">Risteski</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2018</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<genre authority="bibutilsgt">journal article</genre>
<relatedItem type="host">
<titleInfo>
<title>Transactions of the Association for Computational Linguistics</title>
</titleInfo>
<originInfo>
<issuance>continuing</issuance>
<publisher>MIT Press</publisher>
<place>
<placeTerm type="text">Cambridge, MA</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">periodical</genre>
<genre authority="bibutilsgt">academic journal</genre>
</relatedItem>
<abstract>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.</abstract>
<identifier type="citekey">arora-etal-2018-linear</identifier>
<identifier type="doi">10.1162/tacl_a_00034</identifier>
<location>
<url>https://aclanthology.org/Q18-1034</url>
</location>
<part>
<date>2018</date>
<detail type="volume"><number>6</number></detail>
<extent unit="page">
<start>483</start>
<end>495</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Journal Article
%T Linear Algebraic Structure of Word Senses, with Applications to Polysemy
%A Arora, Sanjeev
%A Li, Yuanzhi
%A Liang, Yingyu
%A Ma, Tengyu
%A Risteski, Andrej
%J Transactions of the Association for Computational Linguistics
%D 2018
%V 6
%I MIT Press
%C Cambridge, MA
%F arora-etal-2018-linear
%X 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.
%R 10.1162/tacl_a_00034
%U https://aclanthology.org/Q18-1034
%U https://doi.org/10.1162/tacl_a_00034
%P 483-495
Markdown (Informal)
[Linear Algebraic Structure of Word Senses, with Applications to Polysemy](https://aclanthology.org/Q18-1034) (Arora et al., TACL 2018)
ACL