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Target-Based Offensive Language Identification. / Zampieri, Marcos; Morgan, Skye ; North, Kai et al.
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics. Stroudsberg, Pa.: Association for Computational Linguistics (ACL Anthology), 2023. p. 762-770.
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Harvard
Zampieri, M, Morgan, S, North, K, Simmons, A, Khandelwal, P
, Ranasinghe, T, Rosenthal, S & Nakov, P 2023,
Target-Based Offensive Language Identification. in
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics (ACL Anthology), Stroudsberg, Pa., pp. 762-770.
https://doi.org/10.18653/v1/2023.acl-short.66APA
Zampieri, M., Morgan, S., North, K., Simmons, A., Khandelwal, P.
, Ranasinghe, T., Rosenthal, S., & Nakov, P. (2023).
Target-Based Offensive Language Identification. In
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (pp. 762-770). Association for Computational Linguistics (ACL Anthology).
https://doi.org/10.18653/v1/2023.acl-short.66Vancouver
Zampieri M, Morgan S, North K, Simmons A, Khandelwal P
, Ranasinghe T et al.
Target-Based Offensive Language Identification. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics. Stroudsberg, Pa.: Association for Computational Linguistics (ACL Anthology). 2023. p. 762-770 doi: 10.18653/v1/2023.acl-short.66
Author
Zampieri, Marcos ; Morgan, Skye ; North, Kai et al. /
Target-Based Offensive Language Identification. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics. Stroudsberg, Pa. : Association for Computational Linguistics (ACL Anthology), 2023. pp. 762-770
Bibtex
@inproceedings{cd4f0d0e44d346aea8b58206f7831631,
title = "Target-Based Offensive Language Identification",
abstract = "We present TBO, a new dataset for Target-based Offensive language identification. TBO contains post-level annotations regarding the harmfulness of an offensive post and token-level annotations comprising of the target and the offensive argument expression. Popular offensive language identification datasets for social media focus on annotation taxonomies only at the post level and more recently, some datasets have been released that feature only token-level annotations. TBO is an important resource that bridges the gap between post-level and token-level annotation datasets by introducing a single comprehensive unified annotation taxonomy. We use the TBO taxonomy to annotate post-level and token-level offensive language on English Twitter posts. We release an initial dataset of over 4,500 instances collected from Twitter and we carry out multiple experiments to compare the performance of different models trained and tested on TBO.",
author = "Marcos Zampieri and Skye Morgan and Kai North and Austin Simmons and Paridhi Khandelwal and Tharindu Ranasinghe and Sara Rosenthal and Preslav Nakov",
year = "2023",
month = jul,
day = "14",
doi = "10.18653/v1/2023.acl-short.66",
language = "English",
pages = "762--770",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics",
publisher = "Association for Computational Linguistics (ACL Anthology)",
}
RIS
TY - GEN
T1 - Target-Based Offensive Language Identification
AU - Zampieri, Marcos
AU - Morgan, Skye
AU - North, Kai
AU - Simmons, Austin
AU - Khandelwal, Paridhi
AU - Ranasinghe, Tharindu
AU - Rosenthal, Sara
AU - Nakov, Preslav
PY - 2023/7/14
Y1 - 2023/7/14
N2 - We present TBO, a new dataset for Target-based Offensive language identification. TBO contains post-level annotations regarding the harmfulness of an offensive post and token-level annotations comprising of the target and the offensive argument expression. Popular offensive language identification datasets for social media focus on annotation taxonomies only at the post level and more recently, some datasets have been released that feature only token-level annotations. TBO is an important resource that bridges the gap between post-level and token-level annotation datasets by introducing a single comprehensive unified annotation taxonomy. We use the TBO taxonomy to annotate post-level and token-level offensive language on English Twitter posts. We release an initial dataset of over 4,500 instances collected from Twitter and we carry out multiple experiments to compare the performance of different models trained and tested on TBO.
AB - We present TBO, a new dataset for Target-based Offensive language identification. TBO contains post-level annotations regarding the harmfulness of an offensive post and token-level annotations comprising of the target and the offensive argument expression. Popular offensive language identification datasets for social media focus on annotation taxonomies only at the post level and more recently, some datasets have been released that feature only token-level annotations. TBO is an important resource that bridges the gap between post-level and token-level annotation datasets by introducing a single comprehensive unified annotation taxonomy. We use the TBO taxonomy to annotate post-level and token-level offensive language on English Twitter posts. We release an initial dataset of over 4,500 instances collected from Twitter and we carry out multiple experiments to compare the performance of different models trained and tested on TBO.
U2 - 10.18653/v1/2023.acl-short.66
DO - 10.18653/v1/2023.acl-short.66
M3 - Conference contribution/Paper
SP - 762
EP - 770
BT - Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics
PB - Association for Computational Linguistics (ACL Anthology)
CY - Stroudsberg, Pa.
ER -
