<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="alkiek-etal-2026-neurobiber">
    <titleInfo>
        <title>Neurobiber: Fast and Interpretable Linguistic Feature Extraction</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Kenan</namePart>
        <namePart type="family">Alkiek</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Anna</namePart>
        <namePart type="family">Wegmann</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Jian</namePart>
        <namePart type="family">Zhu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">David</namePart>
        <namePart type="family">Jurgens</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2026-10</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Sweta</namePart>
            <namePart type="family">Agrawal</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Alberto</namePart>
            <namePart type="family">Abad</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Budapest, Hungary</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
        <identifier type="isbn">979-8-89176-461-3</identifier>
    </relatedItem>
    <abstract>Linguistic style is central to how texts convey meaning and to tasks from register analysis to authorship verification, yet extracting detailed stylistic features at scale is hard: rule-based taggers are interpretable but slow, while neural style embeddings are fast but opaque. We present Neurobiber, a RoBERTa tagger that predicts 96 interpretable features from Biber’s Multidimensional Analysis in a single forward pass, bringing this analysis to corpus scale: a one-billion-token corpus tags on a single GPU in about 9.5 hours, over an order of magnitude faster than single-thread taggers. Neurobiber is trained on BiberPlus, a new open-source rule tagger that computes the same 96 features deterministically from explicit rules and ships built-in analytics such as PCA and factor analysis; users tag at scale with Neurobiber and fall back to BiberPlus for exact, auditable counts. Neurobiber reproduces BiberPlus at 0.97 macro-F1; its features recover Biber’s primary dimension of variation, and on PAN 2020 authorship verification they contribute signal complementary to a strong neural encoder. Both tools are released openly, with a live demo.</abstract>
    <identifier type="citekey">alkiek-etal-2026-neurobiber</identifier>
    <location>
        <url>https://aclanthology.org/2026.emnlp-demos.61/</url>
    </location>
    <part>
        <date>2026-10</date>
        <extent unit="page">
            <start>639</start>
            <end>650</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Neurobiber: Fast and Interpretable Linguistic Feature Extraction
%A Alkiek, Kenan
%A Wegmann, Anna
%A Zhu, Jian
%A Jurgens, David
%Y Agrawal, Sweta
%Y Abad, Alberto
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-461-3
%F alkiek-etal-2026-neurobiber
%X Linguistic style is central to how texts convey meaning and to tasks from register analysis to authorship verification, yet extracting detailed stylistic features at scale is hard: rule-based taggers are interpretable but slow, while neural style embeddings are fast but opaque. We present Neurobiber, a RoBERTa tagger that predicts 96 interpretable features from Biber’s Multidimensional Analysis in a single forward pass, bringing this analysis to corpus scale: a one-billion-token corpus tags on a single GPU in about 9.5 hours, over an order of magnitude faster than single-thread taggers. Neurobiber is trained on BiberPlus, a new open-source rule tagger that computes the same 96 features deterministically from explicit rules and ships built-in analytics such as PCA and factor analysis; users tag at scale with Neurobiber and fall back to BiberPlus for exact, auditable counts. Neurobiber reproduces BiberPlus at 0.97 macro-F1; its features recover Biber’s primary dimension of variation, and on PAN 2020 authorship verification they contribute signal complementary to a strong neural encoder. Both tools are released openly, with a live demo.
%U https://aclanthology.org/2026.emnlp-demos.61/
%P 639-650

Markdown (Informal)

[Neurobiber: Fast and Interpretable Linguistic Feature Extraction](https://aclanthology.org/2026.emnlp-demos.61/) (Alkiek et al., EMNLP 2026)

• Neurobiber: Fast and Interpretable Linguistic Feature Extraction (Alkiek et al., EMNLP 2026)

ACL

• Kenan Alkiek, Anna Wegmann, Jian Zhu, and David Jurgens. 2026. Neurobiber: Fast and Interpretable Linguistic Feature Extraction. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 639–650, Budapest, Hungary. Association for Computational Linguistics.