<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="varatharaj-todd-2024-just">
    <titleInfo>
        <title>More than Just Statistical Recurrence: Human and Machine Unsupervised Learning of Māori Word Segmentation across Morphological Processes</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Ashvini</namePart>
        <namePart type="family">Varatharaj</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Simon</namePart>
        <namePart type="family">Todd</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2024-06</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 21st SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Garrett</namePart>
            <namePart type="family">Nicolai</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Eleanor</namePart>
            <namePart type="family">Chodroff</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Frederic</namePart>
            <namePart type="family">Mailhot</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Çağrı</namePart>
            <namePart type="family">Çöltekin</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Mexico City, Mexico</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Non-Māori-speaking New Zealanders (NMS) are able to segment Māori words in a highly similar way to fluent speakers (Panther et al., 2024). This ability is assumed to derive through the identification and extraction of statistically recurrent forms. We examine this assumption by asking how NMS segmentations compare to those produced by Morfessor, an unsupervised machine learning model that operates based on statistical recurrence, across words formed by a variety of morphological processes. Both NMS and Morfessor succeed in segmenting words formed by concatenative processes (compounding and affixation without allomorphy), but NMS also succeed for words that invoke templates (reduplication and allomorphy) and other cues to morphological structure, implying that their learning process is sensitive to more than just statistical recurrence.</abstract>
    <identifier type="citekey">varatharaj-todd-2024-just</identifier>
    <identifier type="doi">10.18653/v1/2024.sigmorphon-1.3</identifier>
    <location>
        <url>https://aclanthology.org/2024.sigmorphon-1.3</url>
    </location>
    <part>
        <date>2024-06</date>
        <extent unit="page">
            <start>20</start>
            <end>31</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T More than Just Statistical Recurrence: Human and Machine Unsupervised Learning of Māori Word Segmentation across Morphological Processes
%A Varatharaj, Ashvini
%A Todd, Simon
%Y Nicolai, Garrett
%Y Chodroff, Eleanor
%Y Mailhot, Frederic
%Y Çöltekin, Çağrı
%S Proceedings of the 21st SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F varatharaj-todd-2024-just
%X Non-Māori-speaking New Zealanders (NMS) are able to segment Māori words in a highly similar way to fluent speakers (Panther et al., 2024). This ability is assumed to derive through the identification and extraction of statistically recurrent forms. We examine this assumption by asking how NMS segmentations compare to those produced by Morfessor, an unsupervised machine learning model that operates based on statistical recurrence, across words formed by a variety of morphological processes. Both NMS and Morfessor succeed in segmenting words formed by concatenative processes (compounding and affixation without allomorphy), but NMS also succeed for words that invoke templates (reduplication and allomorphy) and other cues to morphological structure, implying that their learning process is sensitive to more than just statistical recurrence.
%R 10.18653/v1/2024.sigmorphon-1.3
%U https://aclanthology.org/2024.sigmorphon-1.3
%U https://doi.org/10.18653/v1/2024.sigmorphon-1.3
%P 20-31

Markdown (Informal)

[More than Just Statistical Recurrence: Human and Machine Unsupervised Learning of Māori Word Segmentation across Morphological Processes](https://aclanthology.org/2024.sigmorphon-1.3) (Varatharaj & Todd, SIGMORPHON 2024)

• More than Just Statistical Recurrence: Human and Machine Unsupervised Learning of Māori Word Segmentation across Morphological Processes (Varatharaj & Todd, SIGMORPHON 2024)

ACL

• Ashvini Varatharaj and Simon Todd. 2024. More than Just Statistical Recurrence: Human and Machine Unsupervised Learning of Māori Word Segmentation across Morphological Processes. In Proceedings of the 21st SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology, pages 20–31, Mexico City, Mexico. Association for Computational Linguistics.