dc.identifier.uri |
http://dx.doi.org/10.15488/16881 |
|
dc.identifier.uri |
https://www.repo.uni-hannover.de/handle/123456789/17008 |
|
dc.contributor.author |
Hakimov, Sherzod
|
|
dc.contributor.author |
Ewerth, Ralph
|
|
dc.contributor.editor |
Mehta, Parth
|
|
dc.contributor.editor |
Mandl, Thomas
|
|
dc.contributor.editor |
Majumder, Prasenjit
|
|
dc.contributor.editor |
Mitra, Mandar
|
|
dc.date.accessioned |
2024-04-04T08:54:05Z |
|
dc.date.available |
2024-04-04T08:54:05Z |
|
dc.date.issued |
2021 |
|
dc.identifier.citation |
Hakimov, S.; Ewerth, R.: Combining Textual Features for the Detection of Hateful and Offensive Language. In: Mehta, Parth; Mandl, Thomas; Majumder, Prasenjit; Mitra, Mandar (Eds.): FIRE-WN 2021: FIRE 2021 working notes : working notes of FIRE 2021 - Forum for Information Retrieval Evaluation. Aachen, Germany : RWTH Aachen, 2021 (CEUR Workshop Proceedings ; 3159), S. 412-418. |
|
dc.description.abstract |
The detection of offensive, hateful and profane language has become a critical challenge since many users in social networks are exposed to cyberbullying activities on a daily basis. In this paper, we present an analysis of combining different textual features for the detection of hateful or offensive posts on Twitter. We provide a detailed experimental evaluation to understand the impact of each building block in a neural network architecture. The proposed architecture is evaluated on the English Subtask 1A: Identifying Hate, offensive and profane content from the post datasets of HASOC-2021 dataset under the team name TIB-VA. We compared different variants of the contextual word embeddings combined with the character level embeddings and the encoding of collected hate terms. |
eng |
dc.language.iso |
eng |
|
dc.publisher |
Aachen, Germany : RWTH Aachen |
|
dc.relation.ispartof |
FIRE-WN 2021: FIRE 2021 working notes : working notes of FIRE 2021 - Forum for Information Retrieval Evaluation |
|
dc.relation.ispartofseries |
CEUR Workshop Proceedings ; 3159 |
|
dc.relation.uri |
https://ceur-ws.org/Vol-3159/T1-40.pdf |
|
dc.rights |
CC BY 4.0 Unported |
|
dc.rights.uri |
https://creativecommons.org/licenses/by/4.0/ |
|
dc.subject |
abusive language detection |
eng |
dc.subject |
hate speech detection |
eng |
dc.subject |
offensive language detection |
eng |
dc.subject |
social media mining |
eng |
dc.subject.classification |
Konferenzschrift |
ger |
dc.subject.ddc |
004 | Informatik
|
|
dc.subject.ddc |
020 | Bibliotheks- und Informationswissenschaft
|
|
dc.title |
Combining Textual Features for the Detection of Hateful and Offensive Language |
eng |
dc.type |
BookPart |
|
dc.type |
Text |
|
dc.relation.essn |
1613-0073 |
|
dc.bibliographicCitation.volume |
3159 |
|
dc.bibliographicCitation.firstPage |
412 |
|
dc.bibliographicCitation.lastPage |
418 |
|
dc.description.version |
publishedVersion |
|
tib.accessRights |
frei zug�nglich |
|