@inproceedings{bakay-etal-2019-comparing,
title = "Comparing Sense Categorization Between {E}nglish {P}rop{B}ank and {E}nglish {W}ord{N}et",
author = {Bakay, {\"O}zge and
Avar, Beg{\"u}m and
Y{\i}ld{\i}z, Olcay Taner},
editor = "Vossen, Piek and
Fellbaum, Christiane",
booktitle = "Proceedings of the 10th Global Wordnet Conference",
month = jul,
year = "2019",
address = "Wroclaw, Poland",
publisher = "Global Wordnet Association",
url = "https://aclanthology.org/2019.gwc-1.39",
pages = "307--314",
abstract = "Given the fact that verbs play a crucial role in language comprehension, this paper presents a study which compares the verb senses in English PropBank with the ones in English WordNet through manual tagging. After analyzing 1554 senses in 1453 distinct verbs, we have found out that while the majority of the senses in PropBank have their one-to-one correspondents in WordNet, a substantial amount of them are differentiated. Furthermore, by analysing the differences between our manually-tagged and an automatically-tagged resource, we claim that manual tagging can help provide better results in sense annotation.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="bakay-etal-2019-comparing">
<titleInfo>
<title>Comparing Sense Categorization Between English PropBank and English WordNet</title>
</titleInfo>
<name type="personal">
<namePart type="given">Özge</namePart>
<namePart type="family">Bakay</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Begüm</namePart>
<namePart type="family">Avar</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Olcay</namePart>
<namePart type="given">Taner</namePart>
<namePart type="family">Yıldız</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2019-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 10th Global Wordnet Conference</title>
</titleInfo>
<name type="personal">
<namePart type="given">Piek</namePart>
<namePart type="family">Vossen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Christiane</namePart>
<namePart type="family">Fellbaum</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Global Wordnet Association</publisher>
<place>
<placeTerm type="text">Wroclaw, Poland</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Given the fact that verbs play a crucial role in language comprehension, this paper presents a study which compares the verb senses in English PropBank with the ones in English WordNet through manual tagging. After analyzing 1554 senses in 1453 distinct verbs, we have found out that while the majority of the senses in PropBank have their one-to-one correspondents in WordNet, a substantial amount of them are differentiated. Furthermore, by analysing the differences between our manually-tagged and an automatically-tagged resource, we claim that manual tagging can help provide better results in sense annotation.</abstract>
<identifier type="citekey">bakay-etal-2019-comparing</identifier>
<location>
<url>https://aclanthology.org/2019.gwc-1.39</url>
</location>
<part>
<date>2019-07</date>
<extent unit="page">
<start>307</start>
<end>314</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Comparing Sense Categorization Between English PropBank and English WordNet
%A Bakay, Özge
%A Avar, Begüm
%A Yıldız, Olcay Taner
%Y Vossen, Piek
%Y Fellbaum, Christiane
%S Proceedings of the 10th Global Wordnet Conference
%D 2019
%8 July
%I Global Wordnet Association
%C Wroclaw, Poland
%F bakay-etal-2019-comparing
%X Given the fact that verbs play a crucial role in language comprehension, this paper presents a study which compares the verb senses in English PropBank with the ones in English WordNet through manual tagging. After analyzing 1554 senses in 1453 distinct verbs, we have found out that while the majority of the senses in PropBank have their one-to-one correspondents in WordNet, a substantial amount of them are differentiated. Furthermore, by analysing the differences between our manually-tagged and an automatically-tagged resource, we claim that manual tagging can help provide better results in sense annotation.
%U https://aclanthology.org/2019.gwc-1.39
%P 307-314
Markdown (Informal)
[Comparing Sense Categorization Between English PropBank and English WordNet](https://aclanthology.org/2019.gwc-1.39) (Bakay et al., GWC 2019)
ACL