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http://hdl.handle.net/1893/36264
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DC Field | Value | Language |
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dc.contributor.author | Cakebread-Andrews, Oliver | en_UK |
dc.contributor.author | Ha, Le An | en_UK |
dc.contributor.author | Frommholz, Ingo | en_UK |
dc.contributor.author | Can, Burcu | en_UK |
dc.date.accessioned | 2024-10-03T00:13:21Z | - |
dc.date.available | 2024-10-03T00:13:21Z | - |
dc.date.issued | 2024 | en_UK |
dc.identifier.uri | http://hdl.handle.net/1893/36264 | - |
dc.description.abstract | This paper summarises the differences and similarities found between humans and three natural language processing models when attempting to identify whether English online comments are sarcastic or not. Three models were used to analyse 300 comments from the FigLang 2020 Reddit Dataset, with and without context. The same 300 comments were also given to 39 non-native speakers of English and the results were compared. The aim was to find whether there were any results that could be applied to English as a Foreign Language (EFL) teaching. The results showed that there were similarities between the models and non-native speakers, in particular the logistic regression model. They also highlighted weaknesses with both non-native speakers and the models in detecting sarcasm when the comments included political topics or were phrased as questions. This has potential implications for how the EFL teaching industry could implement the results of error analysis of NLP models in teaching practices. | en_UK |
dc.language.iso | en | en_UK |
dc.publisher | ELRA Language Resource Association | en_UK |
dc.relation | Cakebread-Andrews O, Ha LA, Frommholz I & Can B (2024) Error Analysis of NLP Models and Non-Native Speakers of English Identifying Sarcasm in Reddit Comments. In: LREC-COLING 2024, Torino, 20.05.2024-25.07.2024. ELRA Language Resource Association, pp. 6247-6256. https://aclanthology.org/2024.lrec-main.552.pdf | en_UK |
dc.rights | © 2024 ELRA Language Resource Association: CC BY-NC 4.0 | en_UK |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | en_UK |
dc.subject | Sentiment Analysis | en_UK |
dc.subject | Sarcasm Detection | en_UK |
dc.subject | TEFL | en_UK |
dc.title | Error Analysis of NLP Models and Non-Native Speakers of English Identifying Sarcasm in Reddit Comments | en_UK |
dc.type | Conference Paper | en_UK |
dc.citation.spage | 6247 | en_UK |
dc.citation.epage | 6256 | en_UK |
dc.citation.publicationstatus | Published | en_UK |
dc.type.status | VoR - Version of Record | en_UK |
dc.identifier.url | https://aclanthology.org/2024.lrec-main.552.pdf | en_UK |
dc.author.email | burcu.can@stir.ac.uk | en_UK |
dc.citation.conferencedates | 2024-05-20 - 2024-07-25 | en_UK |
dc.citation.conferencelocation | Torino | en_UK |
dc.citation.conferencename | LREC-COLING 2024 | en_UK |
dc.citation.date | 01/05/2024 | en_UK |
dc.contributor.affiliation | University of Wolverhampton | en_UK |
dc.contributor.affiliation | University of Wolverhampton | en_UK |
dc.contributor.affiliation | University of Wolverhampton | en_UK |
dc.contributor.affiliation | Computing Science | en_UK |
dc.identifier.wtid | 2033473 | en_UK |
dc.date.accepted | 2024-03-01 | en_UK |
dcterms.dateAccepted | 2024-03-01 | en_UK |
dc.date.filedepositdate | 2024-07-30 | en_UK |
rioxxterms.apc | not required | en_UK |
rioxxterms.type | Conference Paper/Proceeding/Abstract | en_UK |
rioxxterms.version | VoR | en_UK |
local.rioxx.author | Cakebread-Andrews, Oliver| | en_UK |
local.rioxx.author | Ha, Le An| | en_UK |
local.rioxx.author | Frommholz, Ingo| | en_UK |
local.rioxx.author | Can, Burcu| | en_UK |
local.rioxx.project | Internal Project|University of Stirling|https://isni.org/isni/0000000122484331 | en_UK |
local.rioxx.freetoreaddate | 2024-09-26 | en_UK |
local.rioxx.licence | http://creativecommons.org/licenses/by-nc-nd/4.0/|2024-09-26| | en_UK |
local.rioxx.filename | 2024.lrec-main.552.pdf | en_UK |
local.rioxx.filecount | 1 | en_UK |
Appears in Collections: | Computing Science and Mathematics Conference Papers and Proceedings |
Files in This Item:
File | Description | Size | Format | |
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2024.lrec-main.552.pdf | Fulltext - Published Version | 506.54 kB | Adobe PDF | View/Open |
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