<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="czarnowska-etal-2019-words">
    <titleInfo>
        <title>Words are Vectors, Dependencies are Matrices: Learning Word Embeddings from Dependency Graphs</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Paula</namePart>
        <namePart type="family">Czarnowska</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Guy</namePart>
        <namePart type="family">Emerson</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Ann</namePart>
        <namePart type="family">Copestake</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2019-may</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 13th International Conference on Computational Semantics - Long Papers</title>
        </titleInfo>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Gothenburg, Sweden</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Distributional Semantic Models (DSMs) construct vector representations of word meanings based on their contexts. Typically, the contexts of a word are defined as its closest neighbours, but they can also be retrieved from its syntactic dependency relations. In this work, we propose a new dependency-based DSM. The novelty of our model lies in associating an independent meaning representation, a matrix, with each dependency-label. This allows it to capture specifics of the relations between words and contexts, leading to good performance on both intrinsic and extrinsic evaluation tasks. In addition to that, our model has an inherent ability to represent dependency chains as products of matrices which provides a straightforward way of handling further contexts of a word.</abstract>
    <identifier type="citekey">czarnowska-etal-2019-words</identifier>
    <identifier type="doi">10.18653/v1/W19-0408</identifier>
    <location>
        <url>https://aclanthology.org/W19-0408</url>
    </location>
    <part>
        <date>2019-may</date>
        <extent unit="page">
            <start>91</start>
            <end>102</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Words are Vectors, Dependencies are Matrices: Learning Word Embeddings from Dependency Graphs
%A Czarnowska, Paula
%A Emerson, Guy
%A Copestake, Ann
%S Proceedings of the 13th International Conference on Computational Semantics - Long Papers
%D 2019
%8 may
%I Association for Computational Linguistics
%C Gothenburg, Sweden
%F czarnowska-etal-2019-words
%X Distributional Semantic Models (DSMs) construct vector representations of word meanings based on their contexts. Typically, the contexts of a word are defined as its closest neighbours, but they can also be retrieved from its syntactic dependency relations. In this work, we propose a new dependency-based DSM. The novelty of our model lies in associating an independent meaning representation, a matrix, with each dependency-label. This allows it to capture specifics of the relations between words and contexts, leading to good performance on both intrinsic and extrinsic evaluation tasks. In addition to that, our model has an inherent ability to represent dependency chains as products of matrices which provides a straightforward way of handling further contexts of a word.
%R 10.18653/v1/W19-0408
%U https://aclanthology.org/W19-0408
%U https://doi.org/10.18653/v1/W19-0408
%P 91-102

Markdown (Informal)

[Words are Vectors, Dependencies are Matrices: Learning Word Embeddings from Dependency Graphs](https://aclanthology.org/W19-0408) (Czarnowska et al., 2019)

• Words are Vectors, Dependencies are Matrices: Learning Word Embeddings from Dependency Graphs (Czarnowska et al., 2019)

ACL

• Paula Czarnowska, Guy Emerson, and Ann Copestake. 2019. Words are Vectors, Dependencies are Matrices: Learning Word Embeddings from Dependency Graphs. In Proceedings of the 13th International Conference on Computational Semantics - Long Papers, pages 91–102, Gothenburg, Sweden. Association for Computational Linguistics.