<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="stratos-etal-2016-unsupervised">
    <titleInfo>
        <title>Unsupervised Part-Of-Speech Tagging with Anchor Hidden Markov Models</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Karl</namePart>
        <namePart type="family">Stratos</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Michael</namePart>
        <namePart type="family">Collins</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Daniel</namePart>
        <namePart type="family">Hsu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2016</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>We tackle unsupervised part-of-speech (POS) tagging by learning hidden Markov models (HMMs) that are particularly well-suited for the problem. These HMMs, which we call anchor HMMs, assume that each tag is associated with at least one word that can have no other tag, which is a relatively benign condition for POS tagging (e.g., “the” is a word that appears only under the determiner tag). We exploit this assumption and extend the non-negative matrix factorization framework of Arora et al. (2013) to design a consistent estimator for anchor HMMs. In experiments, our algorithm is competitive with strong baselines such as the clustering method of Brown et al. (1992) and the log-linear model of Berg-Kirkpatrick et al. (2010). Furthermore, it produces an interpretable model in which hidden states are automatically lexicalized by words.</abstract>
    <identifier type="citekey">stratos-etal-2016-unsupervised</identifier>
    <identifier type="doi">10.1162/tacl_a_00096</identifier>
    <location>
        <url>https://aclanthology.org/Q16-1018</url>
    </location>
    <part>
        <date>2016</date>
        <detail type="volume"><number>4</number></detail>
        <extent unit="page">
            <start>245</start>
            <end>257</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Journal Article
%T Unsupervised Part-Of-Speech Tagging with Anchor Hidden Markov Models
%A Stratos, Karl
%A Collins, Michael
%A Hsu, Daniel
%J Transactions of the Association for Computational Linguistics
%D 2016
%V 4
%I MIT Press
%C Cambridge, MA
%F stratos-etal-2016-unsupervised
%X We tackle unsupervised part-of-speech (POS) tagging by learning hidden Markov models (HMMs) that are particularly well-suited for the problem. These HMMs, which we call anchor HMMs, assume that each tag is associated with at least one word that can have no other tag, which is a relatively benign condition for POS tagging (e.g., “the” is a word that appears only under the determiner tag). We exploit this assumption and extend the non-negative matrix factorization framework of Arora et al. (2013) to design a consistent estimator for anchor HMMs. In experiments, our algorithm is competitive with strong baselines such as the clustering method of Brown et al. (1992) and the log-linear model of Berg-Kirkpatrick et al. (2010). Furthermore, it produces an interpretable model in which hidden states are automatically lexicalized by words.
%R 10.1162/tacl_a_00096
%U https://aclanthology.org/Q16-1018
%U https://doi.org/10.1162/tacl_a_00096
%P 245-257

Markdown (Informal)

[Unsupervised Part-Of-Speech Tagging with Anchor Hidden Markov Models](https://aclanthology.org/Q16-1018) (Stratos et al., TACL 2016)

• Unsupervised Part-Of-Speech Tagging with Anchor Hidden Markov Models (Stratos et al., TACL 2016)

ACL

• Karl Stratos, Michael Collins, and Daniel Hsu. 2016. Unsupervised Part-Of-Speech Tagging with Anchor Hidden Markov Models. Transactions of the Association for Computational Linguistics, 4:245–257.