<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="doyle-levy-2016-data">
    <titleInfo>
        <title>Data-driven learning of symbolic constraints for a log-linear model in a phonological setting</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Gabriel</namePart>
        <namePart type="family">Doyle</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Roger</namePart>
        <namePart type="family">Levy</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2016-12</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Yuji</namePart>
            <namePart type="family">Matsumoto</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Rashmi</namePart>
            <namePart type="family">Prasad</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>The COLING 2016 Organizing Committee</publisher>
            <place>
                <placeTerm type="text">Osaka, Japan</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>We propose a non-parametric Bayesian model for learning and weighting symbolically-defined constraints to populate a log-linear model. The model jointly infers a vector of binary constraint values for each candidate output and likely definitions for these constraints, combining observations of the output classes with a (potentially infinite) grammar over potential constraint definitions. We present results on a small morphophonological system, English regular plurals, as a test case. The inferred constraints, based on a grammar of articulatory features, perform as well as theoretically-defined constraints on both observed and novel forms of English regular plurals. The learned constraint values and definitions also closely resemble standard constraints defined within phonological theory.</abstract>
    <identifier type="citekey">doyle-levy-2016-data</identifier>
    <location>
        <url>https://aclanthology.org/C16-1209</url>
    </location>
    <part>
        <date>2016-12</date>
        <extent unit="page">
            <start>2217</start>
            <end>2226</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Data-driven learning of symbolic constraints for a log-linear model in a phonological setting
%A Doyle, Gabriel
%A Levy, Roger
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F doyle-levy-2016-data
%X We propose a non-parametric Bayesian model for learning and weighting symbolically-defined constraints to populate a log-linear model. The model jointly infers a vector of binary constraint values for each candidate output and likely definitions for these constraints, combining observations of the output classes with a (potentially infinite) grammar over potential constraint definitions. We present results on a small morphophonological system, English regular plurals, as a test case. The inferred constraints, based on a grammar of articulatory features, perform as well as theoretically-defined constraints on both observed and novel forms of English regular plurals. The learned constraint values and definitions also closely resemble standard constraints defined within phonological theory.
%U https://aclanthology.org/C16-1209
%P 2217-2226

Markdown (Informal)

[Data-driven learning of symbolic constraints for a log-linear model in a phonological setting](https://aclanthology.org/C16-1209) (Doyle & Levy, COLING 2016)

• Data-driven learning of symbolic constraints for a log-linear model in a phonological setting (Doyle & Levy, COLING 2016)

ACL

• Gabriel Doyle and Roger Levy. 2016. Data-driven learning of symbolic constraints for a log-linear model in a phonological setting. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2217–2226, Osaka, Japan. The COLING 2016 Organizing Committee.