<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="er-etal-2024-lightweight">
    <titleInfo>
        <title>Lightweight Connective Detection Using Gradient Boosting</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Mustafa</namePart>
        <namePart type="given">Erolcan</namePart>
        <namePart type="family">Er</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Murathan</namePart>
        <namePart type="family">Kurfalı</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Deniz</namePart>
        <namePart type="family">Zeyrek</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2024-05</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 20th Joint ACL - ISO Workshop on Interoperable Semantic Annotation @ LREC-COLING 2024</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Harry</namePart>
            <namePart type="family">Bunt</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Nancy</namePart>
            <namePart type="family">Ide</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Kiyong</namePart>
            <namePart type="family">Lee</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Volha</namePart>
            <namePart type="family">Petukhova</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">Laurent</namePart>
            <namePart type="family">Romary</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>ELRA and ICCL</publisher>
            <place>
                <placeTerm type="text">Torino, Italia</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>In this work, we introduce a lightweight discourse connective detection system. Employing gradient boosting trained on straightforward, low-complexity features, this proposed approach sidesteps the computational demands of the current approaches that rely on deep neural networks. Considering its simplicity, our approach achieves competitive results while offering significant gains in terms of time even on CPU. Furthermore, the stable performance across two unrelated languages suggests the robustness of our system in the multilingual scenario. The model is designed to support the annotation of discourse relations, particularly in scenarios with limited resources, while minimizing performance loss.</abstract>
    <identifier type="citekey">er-etal-2024-lightweight</identifier>
    <location>
        <url>https://aclanthology.org/2024.isa-1.7</url>
    </location>
    <part>
        <date>2024-05</date>
        <extent unit="page">
            <start>53</start>
            <end>59</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Lightweight Connective Detection Using Gradient Boosting
%A Er, Mustafa Erolcan
%A Kurfalı, Murathan
%A Zeyrek, Deniz
%Y Bunt, Harry
%Y Ide, Nancy
%Y Lee, Kiyong
%Y Petukhova, Volha
%Y Pustejovsky, James
%Y Romary, Laurent
%S Proceedings of the 20th Joint ACL - ISO Workshop on Interoperable Semantic Annotation @ LREC-COLING 2024
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F er-etal-2024-lightweight
%X In this work, we introduce a lightweight discourse connective detection system. Employing gradient boosting trained on straightforward, low-complexity features, this proposed approach sidesteps the computational demands of the current approaches that rely on deep neural networks. Considering its simplicity, our approach achieves competitive results while offering significant gains in terms of time even on CPU. Furthermore, the stable performance across two unrelated languages suggests the robustness of our system in the multilingual scenario. The model is designed to support the annotation of discourse relations, particularly in scenarios with limited resources, while minimizing performance loss.
%U https://aclanthology.org/2024.isa-1.7
%P 53-59

Markdown (Informal)

[Lightweight Connective Detection Using Gradient Boosting](https://aclanthology.org/2024.isa-1.7) (Er et al., ISA-WS 2024)

• Lightweight Connective Detection Using Gradient Boosting (Er et al., ISA-WS 2024)

ACL

• Mustafa Erolcan Er, Murathan Kurfalı, and Deniz Zeyrek. 2024. Lightweight Connective Detection Using Gradient Boosting. In Proceedings of the 20th Joint ACL - ISO Workshop on Interoperable Semantic Annotation @ LREC-COLING 2024, pages 53–59, Torino, Italia. ELRA and ICCL.