@inproceedings{hassan-etal-2018-umduluth,
title = "{UMD}uluth-{CS}8761 at {S}em{E}val-2018 Task9: Hypernym Discovery using Hearst Patterns, Co-occurrence frequencies and Word Embeddings",
author = "Hassan, Arshia Zernab and
Vallabhajosyula, Manikya Swathi and
Pedersen, Ted",
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/S18-1149",
doi = "10.18653/v1/S18-1149",
pages = "914--918",
abstract = "Hypernym Discovery is the task of identifying potential hypernyms for a given term. A hypernym is a more generalized word that is super-ordinate to more specific words. This paper explores several approaches that rely on co-occurrence frequencies of word pairs, Hearst Patterns based on regular expressions, and word embeddings created from the UMBC corpus. Our system Babbage participated in Subtask 1A for English and placed 6th of 19 systems when identifying concept hypernyms, and 12th of 18 systems for entity hypernyms.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="hassan-etal-2018-umduluth">
<titleInfo>
<title>UMDuluth-CS8761 at SemEval-2018 Task9: Hypernym Discovery using Hearst Patterns, Co-occurrence frequencies and Word Embeddings</title>
</titleInfo>
<name type="personal">
<namePart type="given">Arshia</namePart>
<namePart type="given">Zernab</namePart>
<namePart type="family">Hassan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Manikya</namePart>
<namePart type="given">Swathi</namePart>
<namePart type="family">Vallabhajosyula</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ted</namePart>
<namePart type="family">Pedersen</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>Hypernym Discovery is the task of identifying potential hypernyms for a given term. A hypernym is a more generalized word that is super-ordinate to more specific words. This paper explores several approaches that rely on co-occurrence frequencies of word pairs, Hearst Patterns based on regular expressions, and word embeddings created from the UMBC corpus. Our system Babbage participated in Subtask 1A for English and placed 6th of 19 systems when identifying concept hypernyms, and 12th of 18 systems for entity hypernyms.</abstract>
<identifier type="citekey">hassan-etal-2018-umduluth</identifier>
<identifier type="doi">10.18653/v1/S18-1149</identifier>
<location>
<url>https://aclanthology.org/S18-1149</url>
</location>
<part>
<date>2018-06</date>
<extent unit="page">
<start>914</start>
<end>918</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T UMDuluth-CS8761 at SemEval-2018 Task9: Hypernym Discovery using Hearst Patterns, Co-occurrence frequencies and Word Embeddings
%A Hassan, Arshia Zernab
%A Vallabhajosyula, Manikya Swathi
%A Pedersen, Ted
%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 hassan-etal-2018-umduluth
%X Hypernym Discovery is the task of identifying potential hypernyms for a given term. A hypernym is a more generalized word that is super-ordinate to more specific words. This paper explores several approaches that rely on co-occurrence frequencies of word pairs, Hearst Patterns based on regular expressions, and word embeddings created from the UMBC corpus. Our system Babbage participated in Subtask 1A for English and placed 6th of 19 systems when identifying concept hypernyms, and 12th of 18 systems for entity hypernyms.
%R 10.18653/v1/S18-1149
%U https://aclanthology.org/S18-1149
%U https://doi.org/10.18653/v1/S18-1149
%P 914-918
Markdown (Informal)
[UMDuluth-CS8761 at SemEval-2018 Task9: Hypernym Discovery using Hearst Patterns, Co-occurrence frequencies and Word Embeddings](https://aclanthology.org/S18-1149) (Hassan et al., SemEval 2018)
ACL