UNBNLP at SemEval-2018 Task 10: Evaluating unsupervised approaches to capturing discriminative attributes

Milton King; Ali Hakimi Parizi; Paul Cook

doi:10.18653/v1/S18-1168

UNBNLP at SemEval-2018 Task 10: Evaluating unsupervised approaches to capturing discriminative attributes

Milton King, Ali Hakimi Parizi, Paul Cook

Correct Metadata for

Important: The Anthology treat PDFs as authoritative. Please use this form only to correct data that is out of line with the PDF. See our corrections guidelines if you need to change the PDF.

Title Adjust the title. Retain tags such as <fixed-case>.

Authors Adjust author names and order to match the PDF.

Abstract Correct abstract if needed. Retain XML formatting tags such as <tex-math>.

Verification against PDF Ensure that the new title/authors match the snapshot below. (If there is no snapshot or it is too small, consult the PDF.)

Authors concatenated from the text boxes above:

ALL author names match the snapshot above—including middle initials, hyphens, and accents.

Abstract

In this paper we present three unsupervised models for capturing discriminative attributes based on information from word embeddings, WordNet, and sentence-level word co-occurrence frequency. We show that, of these approaches, the simple approach based on word co-occurrence performs best. We further consider supervised and unsupervised approaches to combining information from these models, but these approaches do not improve on the word co-occurrence model.

Anthology ID:: S18-1168
Volume:: Proceedings of the 12th International Workshop on Semantic Evaluation
Month:: June
Year:: 2018
Address:: New Orleans, Louisiana
Editors:: Marianna Apidianaki, Saif M. Mohammad, Jonathan May, Ekaterina Shutova, Steven Bethard, Marine Carpuat
Venue:: SemEval
SIG:: SIGLEX
Publisher:: Association for Computational Linguistics
Note:
Pages:: 1013–1016
Language:
URL:: https://aclanthology.org/S18-1168/
DOI:: 10.18653/v1/S18-1168
Bibkey:
Cite (ACL):: Milton King, Ali Hakimi Parizi, and Paul Cook. 2018. UNBNLP at SemEval-2018 Task 10: Evaluating unsupervised approaches to capturing discriminative attributes. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 1013–1016, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):: UNBNLP at SemEval-2018 Task 10: Evaluating unsupervised approaches to capturing discriminative attributes (King et al., SemEval 2018)
Copy Citation:
PDF:: https://aclanthology.org/S18-1168.pdf

PDF Cite Search Fix data

Export citation

BibTeX
MODS XML
Endnote
Preformatted

@inproceedings{king-etal-2018-unbnlp,
    title = "{UNBNLP} at {S}em{E}val-2018 Task 10: Evaluating unsupervised approaches to capturing discriminative attributes",
    author = "King, Milton  and
      Hakimi Parizi, Ali  and
      Cook, Paul",
    editor = "Apidianaki, Marianna  and
      Mohammad, Saif M.  and
      May, Jonathan  and
      Shutova, Ekaterina  and
      Bethard, Steven  and
      Carpuat, Marine",
    booktitle = "Proceedings of the 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/S18-1168/",
    doi = "10.18653/v1/S18-1168",
    pages = "1013--1016",
    abstract = "In this paper we present three unsupervised models for capturing discriminative attributes based on information from word embeddings, WordNet, and sentence-level word co-occurrence frequency. We show that, of these approaches, the simple approach based on word co-occurrence performs best. We further consider supervised and unsupervised approaches to combining information from these models, but these approaches do not improve on the word co-occurrence model."
}

Download as File

<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="king-etal-2018-unbnlp">
    <titleInfo>
        <title>UNBNLP at SemEval-2018 Task 10: Evaluating unsupervised approaches to capturing discriminative attributes</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Milton</namePart>
        <namePart type="family">King</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Ali</namePart>
        <namePart type="family">Hakimi Parizi</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Paul</namePart>
        <namePart type="family">Cook</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2018-06</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 12th International Workshop on Semantic Evaluation</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Marianna</namePart>
            <namePart type="family">Apidianaki</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Saif</namePart>
            <namePart type="given">M</namePart>
            <namePart type="family">Mohammad</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Jonathan</namePart>
            <namePart type="family">May</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Ekaterina</namePart>
            <namePart type="family">Shutova</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Steven</namePart>
            <namePart type="family">Bethard</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Marine</namePart>
            <namePart type="family">Carpuat</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">New Orleans, Louisiana</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>In this paper we present three unsupervised models for capturing discriminative attributes based on information from word embeddings, WordNet, and sentence-level word co-occurrence frequency. We show that, of these approaches, the simple approach based on word co-occurrence performs best. We further consider supervised and unsupervised approaches to combining information from these models, but these approaches do not improve on the word co-occurrence model.</abstract>
    <identifier type="citekey">king-etal-2018-unbnlp</identifier>
    <identifier type="doi">10.18653/v1/S18-1168</identifier>
    <location>
        <url>https://aclanthology.org/S18-1168/</url>
    </location>
    <part>
        <date>2018-06</date>
        <extent unit="page">
            <start>1013</start>
            <end>1016</end>
        </extent>
    </part>
</mods>
</modsCollection>

Download as File

%0 Conference Proceedings
%T UNBNLP at SemEval-2018 Task 10: Evaluating unsupervised approaches to capturing discriminative attributes
%A King, Milton
%A Hakimi Parizi, Ali
%A Cook, Paul
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Bethard, Steven
%Y Carpuat, Marine
%S Proceedings of the 12th International Workshop on Semantic Evaluation
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F king-etal-2018-unbnlp
%X In this paper we present three unsupervised models for capturing discriminative attributes based on information from word embeddings, WordNet, and sentence-level word co-occurrence frequency. We show that, of these approaches, the simple approach based on word co-occurrence performs best. We further consider supervised and unsupervised approaches to combining information from these models, but these approaches do not improve on the word co-occurrence model.
%R 10.18653/v1/S18-1168
%U https://aclanthology.org/S18-1168/
%U https://doi.org/10.18653/v1/S18-1168
%P 1013-1016

Download as File

Markdown (Informal)

[UNBNLP at SemEval-2018 Task 10: Evaluating unsupervised approaches to capturing discriminative attributes](https://aclanthology.org/S18-1168/) (King et al., SemEval 2018)

UNBNLP at SemEval-2018 Task 10: Evaluating unsupervised approaches to capturing discriminative attributes (King et al., SemEval 2018)

ACL

Milton King, Ali Hakimi Parizi, and Paul Cook. 2018. UNBNLP at SemEval-2018 Task 10: Evaluating unsupervised approaches to capturing discriminative attributes. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 1013–1016, New Orleans, Louisiana. Association for Computational Linguistics.