@inproceedings{roussel-2023-lexical,
title = "Lexical Semantics with Vector Symbolic Architectures",
author = "Roussel, Adam",
editor = "Ilinykh, Nikolai and
Morger, Felix and
Dann{\'e}lls, Dana and
Dobnik, Simon and
Megyesi, Be{\'a}ta and
Nivre, Joakim",
booktitle = "Proceedings of the Second Workshop on Resources and Representations for Under-Resourced Languages and Domains (RESOURCEFUL-2023)",
month = may,
year = "2023",
address = "T{\'o}rshavn, the Faroe Islands",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.resourceful-1.8",
pages = "53--61",
abstract = "Conventional approaches to the construction of word vectors typically require very large amounts of unstructured text and powerful computing hardware, and the vectors themselves are also difficult if not impossible to inspect or interpret on their own. In this paper, we introduce a method for building word vectors using the framework of vector symbolic architectures in order to encode the semantic information in wordnets, such as the Open English WordNet or the Open Multilingual Wordnet. Such vectors perform surprisingly well on common word similarity benchmarks, and yet they are transparent, interpretable, and the information contained within them has a clear provenance.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="roussel-2023-lexical">
<titleInfo>
<title>Lexical Semantics with Vector Symbolic Architectures</title>
</titleInfo>
<name type="personal">
<namePart type="given">Adam</namePart>
<namePart type="family">Roussel</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2023-05</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Second Workshop on Resources and Representations for Under-Resourced Languages and Domains (RESOURCEFUL-2023)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Nikolai</namePart>
<namePart type="family">Ilinykh</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Felix</namePart>
<namePart type="family">Morger</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Dana</namePart>
<namePart type="family">Dannélls</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Simon</namePart>
<namePart type="family">Dobnik</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Beáta</namePart>
<namePart type="family">Megyesi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Joakim</namePart>
<namePart type="family">Nivre</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Tórshavn, the Faroe Islands</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Conventional approaches to the construction of word vectors typically require very large amounts of unstructured text and powerful computing hardware, and the vectors themselves are also difficult if not impossible to inspect or interpret on their own. In this paper, we introduce a method for building word vectors using the framework of vector symbolic architectures in order to encode the semantic information in wordnets, such as the Open English WordNet or the Open Multilingual Wordnet. Such vectors perform surprisingly well on common word similarity benchmarks, and yet they are transparent, interpretable, and the information contained within them has a clear provenance.</abstract>
<identifier type="citekey">roussel-2023-lexical</identifier>
<location>
<url>https://aclanthology.org/2023.resourceful-1.8</url>
</location>
<part>
<date>2023-05</date>
<extent unit="page">
<start>53</start>
<end>61</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Lexical Semantics with Vector Symbolic Architectures
%A Roussel, Adam
%Y Ilinykh, Nikolai
%Y Morger, Felix
%Y Dannélls, Dana
%Y Dobnik, Simon
%Y Megyesi, Beáta
%Y Nivre, Joakim
%S Proceedings of the Second Workshop on Resources and Representations for Under-Resourced Languages and Domains (RESOURCEFUL-2023)
%D 2023
%8 May
%I Association for Computational Linguistics
%C Tórshavn, the Faroe Islands
%F roussel-2023-lexical
%X Conventional approaches to the construction of word vectors typically require very large amounts of unstructured text and powerful computing hardware, and the vectors themselves are also difficult if not impossible to inspect or interpret on their own. In this paper, we introduce a method for building word vectors using the framework of vector symbolic architectures in order to encode the semantic information in wordnets, such as the Open English WordNet or the Open Multilingual Wordnet. Such vectors perform surprisingly well on common word similarity benchmarks, and yet they are transparent, interpretable, and the information contained within them has a clear provenance.
%U https://aclanthology.org/2023.resourceful-1.8
%P 53-61
Markdown (Informal)
[Lexical Semantics with Vector Symbolic Architectures](https://aclanthology.org/2023.resourceful-1.8) (Roussel, RESOURCEFUL 2023)
ACL
- Adam Roussel. 2023. Lexical Semantics with Vector Symbolic Architectures. In Proceedings of the Second Workshop on Resources and Representations for Under-Resourced Languages and Domains (RESOURCEFUL-2023), pages 53–61, Tórshavn, the Faroe Islands. Association for Computational Linguistics.