<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="turney-2013-distributional">
    <titleInfo>
        <title>Distributional Semantics Beyond Words: Supervised Learning of Analogy and Paraphrase</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Peter</namePart>
        <namePart type="given">D</namePart>
        <namePart type="family">Turney</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>There have been several efforts to extend distributional semantics beyond individual words, to measure the similarity of word pairs, phrases, and sentences (briefly, tuples; ordered sets of words, contiguous or noncontiguous). One way to extend beyond words is to compare two tuples using a function that combines pairwise similarities between the component words in the tuples. A strength of this approach is that it works with both relational similarity (analogy) and compositional similarity (paraphrase). However, past work required hand-coding the combination function for different tasks. The main contribution of this paper is that combination functions are generated by supervised learning. We achieve state-of-the-art results in measuring relational similarity between word pairs (SAT analogies and SemEval 2012 Task 2) and measuring compositional similarity between noun-modifier phrases and unigrams (multiple-choice paraphrase questions).</abstract>
    <identifier type="citekey">turney-2013-distributional</identifier>
    <identifier type="doi">10.1162/tacl_a_00233</identifier>
    <location>
        <url>https://aclanthology.org/Q13-1029</url>
    </location>
    <part>
        <date>2013</date>
        <detail type="volume"><number>1</number></detail>
        <extent unit="page">
            <start>353</start>
            <end>366</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Journal Article
%T Distributional Semantics Beyond Words: Supervised Learning of Analogy and Paraphrase
%A Turney, Peter D.
%J Transactions of the Association for Computational Linguistics
%D 2013
%V 1
%I MIT Press
%C Cambridge, MA
%F turney-2013-distributional
%X There have been several efforts to extend distributional semantics beyond individual words, to measure the similarity of word pairs, phrases, and sentences (briefly, tuples; ordered sets of words, contiguous or noncontiguous). One way to extend beyond words is to compare two tuples using a function that combines pairwise similarities between the component words in the tuples. A strength of this approach is that it works with both relational similarity (analogy) and compositional similarity (paraphrase). However, past work required hand-coding the combination function for different tasks. The main contribution of this paper is that combination functions are generated by supervised learning. We achieve state-of-the-art results in measuring relational similarity between word pairs (SAT analogies and SemEval 2012 Task 2) and measuring compositional similarity between noun-modifier phrases and unigrams (multiple-choice paraphrase questions).
%R 10.1162/tacl_a_00233
%U https://aclanthology.org/Q13-1029
%U https://doi.org/10.1162/tacl_a_00233
%P 353-366

Markdown (Informal)

[Distributional Semantics Beyond Words: Supervised Learning of Analogy and Paraphrase](https://aclanthology.org/Q13-1029) (Turney, TACL 2013)

• Distributional Semantics Beyond Words: Supervised Learning of Analogy and Paraphrase (Turney, TACL 2013)

ACL

• Peter D. Turney. 2013. Distributional Semantics Beyond Words: Supervised Learning of Analogy and Paraphrase. Transactions of the Association for Computational Linguistics, 1:353–366.