Token-level semantic typology without a massively parallel corpus

Barend Beekhuizen

doi:10.18653/v1/2025.sigtyp-1.16

Token-level semantic typology without a massively parallel corpus

Correct Metadata for

Important: The Anthology treat PDFs as authoritative. Please use this form only to correct data that is out of line with the PDF. See our corrections guidelines if you need to change the PDF.

Title Adjust the title. Retain tags such as <fixed-case>.

Authors Adjust author names and order to match the PDF.

Abstract Correct abstract if needed. Retain XML formatting tags such as <tex-math>.

Verification against PDF Ensure that the new title/authors match the snapshot below. (If there is no snapshot or it is too small, consult the PDF.)

Authors concatenated from the text boxes above:

ALL author names match the snapshot above—including middle initials, hyphens, and accents.

Abstract

This paper presents a computational method for token-level lexical semantic comparative research in an original text setting, as opposed to the more common massively parallel setting. Given a set of (non-massively parallel) bitexts, the method consists of leveraging pre-trained contextual vectors in a reference language to induce, for a token in one target language, the lexical items that all other target languages would have used, thus simulating a massively parallel set-up. The method is evaluated on its extraction and induction quality, and the use of the method for lexical semantic typological research is demonstrated.

Anthology ID:: 2025.sigtyp-1.16
Volume:: Proceedings of the 7th Workshop on Research in Computational Linguistic Typology and Multilingual NLP
Month:: August
Year:: 2025
Address:: Vienna, Austria
Editors:: Michael Hahn, Priya Rani, Ritesh Kumar, Andreas Shcherbakov, Alexey Sorokin, Oleg Serikov, Ryan Cotterell, Ekaterina Vylomova
Venues:: SIGTYP | WS
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 165–176
Language:
URL:: https://aclanthology.org/2025.sigtyp-1.16/
DOI:: 10.18653/v1/2025.sigtyp-1.16
Bibkey:
Cite (ACL):: Barend Beekhuizen. 2025. Token-level semantic typology without a massively parallel corpus. In Proceedings of the 7th Workshop on Research in Computational Linguistic Typology and Multilingual NLP, pages 165–176, Vienna, Austria. Association for Computational Linguistics.
Cite (Informal):: Token-level semantic typology without a massively parallel corpus (Beekhuizen, SIGTYP 2025)
Copy Citation:
PDF:: https://aclanthology.org/2025.sigtyp-1.16.pdf

PDF Cite Search Fix data

Export citation

BibTeX
MODS XML
Endnote
Preformatted

@inproceedings{beekhuizen-2025-token,
    title = "Token-level semantic typology without a massively parallel corpus",
    author = "Beekhuizen, Barend",
    editor = "Hahn, Michael  and
      Rani, Priya  and
      Kumar, Ritesh  and
      Shcherbakov, Andreas  and
      Sorokin, Alexey  and
      Serikov, Oleg  and
      Cotterell, Ryan  and
      Vylomova, Ekaterina",
    booktitle = "Proceedings of the 7th Workshop on Research in Computational Linguistic Typology and Multilingual NLP",
    month = aug,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.sigtyp-1.16/",
    doi = "10.18653/v1/2025.sigtyp-1.16",
    pages = "165--176",
    ISBN = "979-8-89176-281-7",
    abstract = "This paper presents a computational method for token-level lexical semantic comparative research in an original text setting, as opposed to the more common massively parallel setting. Given a set of (non-massively parallel) bitexts, the method consists of leveraging pre-trained contextual vectors in a reference language to induce, for a token in one target language, the lexical items that all other target languages would have used, thus simulating a massively parallel set-up. The method is evaluated on its extraction and induction quality, and the use of the method for lexical semantic typological research is demonstrated."
}

Download as File

<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="beekhuizen-2025-token">
    <titleInfo>
        <title>Token-level semantic typology without a massively parallel corpus</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Barend</namePart>
        <namePart type="family">Beekhuizen</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2025-08</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 7th Workshop on Research in Computational Linguistic Typology and Multilingual NLP</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Michael</namePart>
            <namePart type="family">Hahn</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Priya</namePart>
            <namePart type="family">Rani</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Ritesh</namePart>
            <namePart type="family">Kumar</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Andreas</namePart>
            <namePart type="family">Shcherbakov</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Alexey</namePart>
            <namePart type="family">Sorokin</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Oleg</namePart>
            <namePart type="family">Serikov</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Ryan</namePart>
            <namePart type="family">Cotterell</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Ekaterina</namePart>
            <namePart type="family">Vylomova</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Vienna, Austria</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
        <identifier type="isbn">979-8-89176-281-7</identifier>
    </relatedItem>
    <abstract>This paper presents a computational method for token-level lexical semantic comparative research in an original text setting, as opposed to the more common massively parallel setting. Given a set of (non-massively parallel) bitexts, the method consists of leveraging pre-trained contextual vectors in a reference language to induce, for a token in one target language, the lexical items that all other target languages would have used, thus simulating a massively parallel set-up. The method is evaluated on its extraction and induction quality, and the use of the method for lexical semantic typological research is demonstrated.</abstract>
    <identifier type="citekey">beekhuizen-2025-token</identifier>
    <identifier type="doi">10.18653/v1/2025.sigtyp-1.16</identifier>
    <location>
        <url>https://aclanthology.org/2025.sigtyp-1.16/</url>
    </location>
    <part>
        <date>2025-08</date>
        <extent unit="page">
            <start>165</start>
            <end>176</end>
        </extent>
    </part>
</mods>
</modsCollection>

Download as File

%0 Conference Proceedings
%T Token-level semantic typology without a massively parallel corpus
%A Beekhuizen, Barend
%Y Hahn, Michael
%Y Rani, Priya
%Y Kumar, Ritesh
%Y Shcherbakov, Andreas
%Y Sorokin, Alexey
%Y Serikov, Oleg
%Y Cotterell, Ryan
%Y Vylomova, Ekaterina
%S Proceedings of the 7th Workshop on Research in Computational Linguistic Typology and Multilingual NLP
%D 2025
%8 August
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-281-7
%F beekhuizen-2025-token
%X This paper presents a computational method for token-level lexical semantic comparative research in an original text setting, as opposed to the more common massively parallel setting. Given a set of (non-massively parallel) bitexts, the method consists of leveraging pre-trained contextual vectors in a reference language to induce, for a token in one target language, the lexical items that all other target languages would have used, thus simulating a massively parallel set-up. The method is evaluated on its extraction and induction quality, and the use of the method for lexical semantic typological research is demonstrated.
%R 10.18653/v1/2025.sigtyp-1.16
%U https://aclanthology.org/2025.sigtyp-1.16/
%U https://doi.org/10.18653/v1/2025.sigtyp-1.16
%P 165-176

Download as File

Markdown (Informal)

[Token-level semantic typology without a massively parallel corpus](https://aclanthology.org/2025.sigtyp-1.16/) (Beekhuizen, SIGTYP 2025)

Token-level semantic typology without a massively parallel corpus (Beekhuizen, SIGTYP 2025)

ACL

Barend Beekhuizen. 2025. Token-level semantic typology without a massively parallel corpus. In Proceedings of the 7th Workshop on Research in Computational Linguistic Typology and Multilingual NLP, pages 165–176, Vienna, Austria. Association for Computational Linguistics.