Using syntax for the semantic representation of sentences

Iskandar Boucharenc; Eve Sauvage; Thomas Gerald; Julien Tourille; Sabrina Campano; Cyril Grouin; Sophie Rosset

doi:10.63317/4gtinxarm3dd

Using syntax for the semantic representation of sentences

Iskandar Boucharenc, Eve Sauvage, Thomas Gerald, Julien Tourille, Sabrina Campano, Cyril Grouin, Sophie Rosset

Correct Metadata for

Use this form to create a GitHub issue with structured data describing the correction. You will need a GitHub account. Once you create that issue, the correction will be reviewed by a staff member.

⚠️ Mobile Users: Submitting this form to create a new issue will only work with github.com, not the GitHub Mobile app.

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>. You may use ... for bold, ... for italic, ... for underline, <sc>...</sc> for small-caps, <tt>...<tt> for typewriter text, <url>...</url> for URLs, <a href=...> for hyperlinks, and <par/> for paragraph breaks.

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 external publication .)

Authors concatenated from the text boxes above:

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

Abstract

Deep learning methods in natural language processing often rely on statistical methods to tokenize texts before vectorization. This segmentation produces lexical subunits offering great flexibility. However, the reuse of identical tokens across words with different meanings can favor representations based on surface form rather than on linguistic information, especially semantics. This mismatch between semantics and surface form can lead to undesirable effects in language processing. To limit the influence of form on the semantics of vector representations, we propose an intermediate representation based on syntactic parsing that is more compact and more faithful to word meaning.

Anthology ID:: 2026.slide-1.15
Volume:: Proceedings of the Workshop on Structured Linguistic Data and Evaluation (SLiDE)
Month:: May
Year:: 2026
Address:: Palma de Mallorca, Spain
Editors:: Erhard Hinrichs, Joakim Nivre, Petya Osenova, James Pustejovsky, Claus Zinn
Venues:: SLiDE | WS
SIG:
Publisher:: ELRA Language Resources Association (ELRA)
Note:
Pages:: 169–179
Language:
External URL:: https://lrec.elra.info/lrec2026-ws-slide-15
DOI:: 10.63317/4gtinxarm3dd
Bibkey:
Cite (ACL):: Iskandar Boucharenc, Eve Sauvage, Thomas Gerald, Julien Tourille, Sabrina Campano, Cyril Grouin, and Sophie Rosset. 2026. Using syntax for the semantic representation of sentences. In Proceedings of the Workshop on Structured Linguistic Data and Evaluation (SLiDE), pages 169–179, Palma de Mallorca, Spain. ELRA Language Resources Association (ELRA).
Cite (Informal):: Using syntax for the semantic representation of sentences (Boucharenc et al., SLiDE 2026)
Copy Citation:

External Cite Search Fix data

Export citation

BibTeX
MODS XML
Endnote
Preformatted

@inproceedings{boucharenc-etal-2026-using,
    title = "Using syntax for the semantic representation of sentences",
    author = "Boucharenc, Iskandar  and
      Sauvage, Eve  and
      Gerald, Thomas  and
      Tourille, Julien  and
      Campano, Sabrina  and
      Grouin, Cyril  and
      Rosset, Sophie",
    editor = "Hinrichs, Erhard  and
      Nivre, Joakim  and
      Osenova, Petya  and
      Pustejovsky, James  and
      Zinn, Claus",
    booktitle = "Proceedings of the Workshop on Structured Linguistic Data and Evaluation ({SL}i{DE})",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resources Association (ELRA)",
    url = "https://aclanthology.org/2026.slide-1.15/",
    doi = "10.63317/4gtinxarm3dd",
    pages = "169--179",
    abstract = "Deep learning methods in natural language processing often rely on statistical methods to tokenize texts before vectorization. This segmentation produces lexical subunits offering great flexibility. However, the reuse of identical tokens across words with different meanings can favor representations based on surface form rather than on linguistic information, especially semantics. This mismatch between semantics and surface form can lead to undesirable effects in language processing. To limit the influence of form on the semantics of vector representations, we propose an intermediate representation based on syntactic parsing that is more compact and more faithful to word meaning."
}

Download as File

<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="boucharenc-etal-2026-using">
    <titleInfo>
        <title>Using syntax for the semantic representation of sentences</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Iskandar</namePart>
        <namePart type="family">Boucharenc</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Eve</namePart>
        <namePart type="family">Sauvage</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Thomas</namePart>
        <namePart type="family">Gerald</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Julien</namePart>
        <namePart type="family">Tourille</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Sabrina</namePart>
        <namePart type="family">Campano</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Cyril</namePart>
        <namePart type="family">Grouin</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Sophie</namePart>
        <namePart type="family">Rosset</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2026-05</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Workshop on Structured Linguistic Data and Evaluation (SLiDE)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Erhard</namePart>
            <namePart type="family">Hinrichs</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>
        <name type="personal">
            <namePart type="given">Petya</namePart>
            <namePart type="family">Osenova</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">James</namePart>
            <namePart type="family">Pustejovsky</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Claus</namePart>
            <namePart type="family">Zinn</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>ELRA Language Resources Association (ELRA)</publisher>
            <place>
                <placeTerm type="text">Palma de Mallorca, Spain</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Deep learning methods in natural language processing often rely on statistical methods to tokenize texts before vectorization. This segmentation produces lexical subunits offering great flexibility. However, the reuse of identical tokens across words with different meanings can favor representations based on surface form rather than on linguistic information, especially semantics. This mismatch between semantics and surface form can lead to undesirable effects in language processing. To limit the influence of form on the semantics of vector representations, we propose an intermediate representation based on syntactic parsing that is more compact and more faithful to word meaning.</abstract>
    <identifier type="citekey">boucharenc-etal-2026-using</identifier>
    <identifier type="doi">10.63317/4gtinxarm3dd</identifier>
    <location>
        <url>https://aclanthology.org/2026.slide-1.15/</url>
    </location>
    <part>
        <date>2026-05</date>
        <extent unit="page">
            <start>169</start>
            <end>179</end>
        </extent>
    </part>
</mods>
</modsCollection>

Download as File

%0 Conference Proceedings
%T Using syntax for the semantic representation of sentences
%A Boucharenc, Iskandar
%A Sauvage, Eve
%A Gerald, Thomas
%A Tourille, Julien
%A Campano, Sabrina
%A Grouin, Cyril
%A Rosset, Sophie
%Y Hinrichs, Erhard
%Y Nivre, Joakim
%Y Osenova, Petya
%Y Pustejovsky, James
%Y Zinn, Claus
%S Proceedings of the Workshop on Structured Linguistic Data and Evaluation (SLiDE)
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma de Mallorca, Spain
%F boucharenc-etal-2026-using
%X Deep learning methods in natural language processing often rely on statistical methods to tokenize texts before vectorization. This segmentation produces lexical subunits offering great flexibility. However, the reuse of identical tokens across words with different meanings can favor representations based on surface form rather than on linguistic information, especially semantics. This mismatch between semantics and surface form can lead to undesirable effects in language processing. To limit the influence of form on the semantics of vector representations, we propose an intermediate representation based on syntactic parsing that is more compact and more faithful to word meaning.
%R 10.63317/4gtinxarm3dd
%U https://aclanthology.org/2026.slide-1.15/
%U https://doi.org/10.63317/4gtinxarm3dd
%P 169-179

Download as File

Markdown (Informal)

[Using syntax for the semantic representation of sentences](https://aclanthology.org/2026.slide-1.15/) (Boucharenc et al., SLiDE 2026)

Using syntax for the semantic representation of sentences (Boucharenc et al., SLiDE 2026)

ACL

Iskandar Boucharenc, Eve Sauvage, Thomas Gerald, Julien Tourille, Sabrina Campano, Cyril Grouin, and Sophie Rosset. 2026. Using syntax for the semantic representation of sentences. In Proceedings of the Workshop on Structured Linguistic Data and Evaluation (SLiDE), pages 169–179, Palma de Mallorca, Spain. ELRA Language Resources Association (ELRA).