<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="sirts-goldwater-2013-minimally">
    <titleInfo>
        <title>Minimally-Supervised Morphological Segmentation using Adaptor Grammars</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Kairit</namePart>
        <namePart type="family">Sirts</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Sharon</namePart>
        <namePart type="family">Goldwater</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2013</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <genre authority="bibutilsgt">journal article</genre>
    <relatedItem type="host">
        <titleInfo>
            <title>Transactions of the Association for Computational Linguistics</title>
        </titleInfo>
        <originInfo>
            <issuance>continuing</issuance>
            <publisher>MIT Press</publisher>
            <place>
                <placeTerm type="text">Cambridge, MA</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">periodical</genre>
        <genre authority="bibutilsgt">academic journal</genre>
    </relatedItem>
    <abstract>This paper explores the use of Adaptor Grammars, a nonparametric Bayesian modelling framework, for minimally supervised morphological segmentation. We compare three training methods: unsupervised training, semi-supervised training, and a novel model selection method. In the model selection method, we train unsupervised Adaptor Grammars using an over-articulated metagrammar, then use a small labelled data set to select which potential morph boundaries identified by the metagrammar should be returned in the final output. We evaluate on five languages and show that semi-supervised training provides a boost over unsupervised training, while the model selection method yields the best average results over all languages and is competitive with state-of-the-art semi-supervised systems. Moreover, this method provides the potential to tune performance according to different evaluation metrics or downstream tasks.</abstract>
    <identifier type="citekey">sirts-goldwater-2013-minimally</identifier>
    <identifier type="doi">10.1162/tacl_a_00225</identifier>
    <location>
        <url>https://aclanthology.org/Q13-1021</url>
    </location>
    <part>
        <date>2013</date>
        <detail type="volume"><number>1</number></detail>
        <extent unit="page">
            <start>255</start>
            <end>266</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Journal Article
%T Minimally-Supervised Morphological Segmentation using Adaptor Grammars
%A Sirts, Kairit
%A Goldwater, Sharon
%J Transactions of the Association for Computational Linguistics
%D 2013
%V 1
%I MIT Press
%C Cambridge, MA
%F sirts-goldwater-2013-minimally
%X This paper explores the use of Adaptor Grammars, a nonparametric Bayesian modelling framework, for minimally supervised morphological segmentation. We compare three training methods: unsupervised training, semi-supervised training, and a novel model selection method. In the model selection method, we train unsupervised Adaptor Grammars using an over-articulated metagrammar, then use a small labelled data set to select which potential morph boundaries identified by the metagrammar should be returned in the final output. We evaluate on five languages and show that semi-supervised training provides a boost over unsupervised training, while the model selection method yields the best average results over all languages and is competitive with state-of-the-art semi-supervised systems. Moreover, this method provides the potential to tune performance according to different evaluation metrics or downstream tasks.
%R 10.1162/tacl_a_00225
%U https://aclanthology.org/Q13-1021
%U https://doi.org/10.1162/tacl_a_00225
%P 255-266

Markdown (Informal)

[Minimally-Supervised Morphological Segmentation using Adaptor Grammars](https://aclanthology.org/Q13-1021) (Sirts & Goldwater, TACL 2013)

• Minimally-Supervised Morphological Segmentation using Adaptor Grammars (Sirts & Goldwater, TACL 2013)

ACL

• Kairit Sirts and Sharon Goldwater. 2013. Minimally-Supervised Morphological Segmentation using Adaptor Grammars. Transactions of the Association for Computational Linguistics, 1:255–266.