Reference-Free Evaluation of Taxonomies

Pascal Wullschleger; Majid Zarharan; Donnacha Daly; Marc Pouly; Jennifer Foster

Reference-Free Evaluation of Taxonomies

Pascal Wullschleger, Majid Zarharan, Donnacha Daly, Marc Pouly, Jennifer Foster

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 <b>...</b> for bold, <i>...</i> for italic, and <url>...</url> for URLs.

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

We introduce two reference-free metrics for quality evaluation of taxonomies in the absence of labels. The first metric evaluates robustness by calculating the correlation between semantic and taxonomic similarity, addressing error types not considered by existing metrics. The second uses Natural Language Inference to assess logical adequacy. Both metrics are tested on five taxonomies and are shown to correlate well with F1 against ground truth taxonomies. We further demonstrate that our metrics can predict downstream performance in hierarchical classification when used with label hierarchies.

Anthology ID:: 2026.findings-acl.1273
Volume:: Findings of the Association for Computational Linguistics: ACL 2026
Month:: July
Year:: 2026
Address:: San Diego, California, United States
Editors:: Maria Liakata, Viviane P. Moreira, Jiajun Zhang, David Jurgens
Venue:: Findings
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 25489–25507
Language:
URL:: https://aclanthology.org/2026.findings-acl.1273/
DOI:
Bibkey:
Cite (ACL):: Pascal Wullschleger, Majid Zarharan, Donnacha Daly, Marc Pouly, and Jennifer Foster. 2026. Reference-Free Evaluation of Taxonomies. In Findings of the Association for Computational Linguistics: ACL 2026, pages 25489–25507, San Diego, California, United States. Association for Computational Linguistics.
Cite (Informal):: Reference-Free Evaluation of Taxonomies (Wullschleger et al., Findings 2026)
Copy Citation:
PDF:: https://aclanthology.org/2026.findings-acl.1273.pdf
Checklist:: 2026.findings-acl.1273.checklist.pdf

PDF Cite Search Checklist Fix data

Export citation

BibTeX
MODS XML
Endnote
Preformatted

@inproceedings{wullschleger-etal-2026-reference,
    title = "Reference-Free Evaluation of Taxonomies",
    author = "Wullschleger, Pascal  and
      Zarharan, Majid  and
      Daly, Donnacha  and
      Pouly, Marc  and
      Foster, Jennifer",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.findings-acl.1273/",
    pages = "25489--25507",
    ISBN = "979-8-89176-395-1",
    abstract = "We introduce two reference-free metrics for quality evaluation of taxonomies in the absence of labels. The first metric evaluates robustness by calculating the correlation between semantic and taxonomic similarity, addressing error types not considered by existing metrics. The second uses Natural Language Inference to assess logical adequacy. Both metrics are tested on five taxonomies and are shown to correlate well with F1 against ground truth taxonomies. We further demonstrate that our metrics can predict downstream performance in hierarchical classification when used with label hierarchies."
}

Download as File

<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="wullschleger-etal-2026-reference">
    <titleInfo>
        <title>Reference-Free Evaluation of Taxonomies</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Pascal</namePart>
        <namePart type="family">Wullschleger</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Majid</namePart>
        <namePart type="family">Zarharan</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Donnacha</namePart>
        <namePart type="family">Daly</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Marc</namePart>
        <namePart type="family">Pouly</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Jennifer</namePart>
        <namePart type="family">Foster</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2026-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Findings of the Association for Computational Linguistics: ACL 2026</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Maria</namePart>
            <namePart type="family">Liakata</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Viviane</namePart>
            <namePart type="given">P</namePart>
            <namePart type="family">Moreira</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Jiajun</namePart>
            <namePart type="family">Zhang</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">David</namePart>
            <namePart type="family">Jurgens</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">San Diego, California, United States</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
        <identifier type="isbn">979-8-89176-395-1</identifier>
    </relatedItem>
    <abstract>We introduce two reference-free metrics for quality evaluation of taxonomies in the absence of labels. The first metric evaluates robustness by calculating the correlation between semantic and taxonomic similarity, addressing error types not considered by existing metrics. The second uses Natural Language Inference to assess logical adequacy. Both metrics are tested on five taxonomies and are shown to correlate well with F1 against ground truth taxonomies. We further demonstrate that our metrics can predict downstream performance in hierarchical classification when used with label hierarchies.</abstract>
    <identifier type="citekey">wullschleger-etal-2026-reference</identifier>
    <location>
        <url>https://aclanthology.org/2026.findings-acl.1273/</url>
    </location>
    <part>
        <date>2026-07</date>
        <extent unit="page">
            <start>25489</start>
            <end>25507</end>
        </extent>
    </part>
</mods>
</modsCollection>

Download as File

%0 Conference Proceedings
%T Reference-Free Evaluation of Taxonomies
%A Wullschleger, Pascal
%A Zarharan, Majid
%A Daly, Donnacha
%A Pouly, Marc
%A Foster, Jennifer
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F wullschleger-etal-2026-reference
%X We introduce two reference-free metrics for quality evaluation of taxonomies in the absence of labels. The first metric evaluates robustness by calculating the correlation between semantic and taxonomic similarity, addressing error types not considered by existing metrics. The second uses Natural Language Inference to assess logical adequacy. Both metrics are tested on five taxonomies and are shown to correlate well with F1 against ground truth taxonomies. We further demonstrate that our metrics can predict downstream performance in hierarchical classification when used with label hierarchies.
%U https://aclanthology.org/2026.findings-acl.1273/
%P 25489-25507

Download as File

Markdown (Informal)

[Reference-Free Evaluation of Taxonomies](https://aclanthology.org/2026.findings-acl.1273/) (Wullschleger et al., Findings 2026)

Reference-Free Evaluation of Taxonomies (Wullschleger et al., Findings 2026)

ACL

Pascal Wullschleger, Majid Zarharan, Donnacha Daly, Marc Pouly, and Jennifer Foster. 2026. Reference-Free Evaluation of Taxonomies. In Findings of the Association for Computational Linguistics: ACL 2026, pages 25489–25507, San Diego, California, United States. Association for Computational Linguistics.