TY - GEN
T1 - MUCIC@LT-EDI-ACL2022
T2 - 2nd Workshop on Language Technology for Equality, Diversity and Inclusion, LTEDI 2022
AU - Anusha, M. D.
AU - Balouchzahi, F.
AU - Shashirekha, H. L.
AU - Sidorov, G.
N1 - Publisher Copyright:
© 2022 Association for Computational Linguistics.
PY - 2022
Y1 - 2022
N2 - Spreading positive vibes or hope content on social media may help many people to get motivated in their life. To address Hope Speech detection in YouTube comments, this paper presents the description of the models submitted by our team - MUCIC, to the Hope Speech Detection for Equality, Diversity, and Inclusion (HopeEDI) shared task at Association for Computational Linguistics (ACL) 2022. This shared task consists of texts in five languages, namely: English, Spanish (in Latin scripts), and Tamil, Malayalam, and Kannada (in code-mixed native and Roman scripts) with the aim of classifying the YouTube comment into "Hope", "Not-Hope" or "Not-Intended" categories. The proposed methodology uses the re-sampling technique to deal with imbalanced data in the corpus and obtained 1st rank for English language with a macro-averaged F1-score of 0.550 and weighted-averaged F1-score of 0.860. The code to reproduce this work is available in GitHub.
AB - Spreading positive vibes or hope content on social media may help many people to get motivated in their life. To address Hope Speech detection in YouTube comments, this paper presents the description of the models submitted by our team - MUCIC, to the Hope Speech Detection for Equality, Diversity, and Inclusion (HopeEDI) shared task at Association for Computational Linguistics (ACL) 2022. This shared task consists of texts in five languages, namely: English, Spanish (in Latin scripts), and Tamil, Malayalam, and Kannada (in code-mixed native and Roman scripts) with the aim of classifying the YouTube comment into "Hope", "Not-Hope" or "Not-Intended" categories. The proposed methodology uses the re-sampling technique to deal with imbalanced data in the corpus and obtained 1st rank for English language with a macro-averaged F1-score of 0.550 and weighted-averaged F1-score of 0.860. The code to reproduce this work is available in GitHub.
UR - http://www.scopus.com/inward/record.url?scp=85133708178&partnerID=8YFLogxK
M3 - Contribución a la conferencia
AN - SCOPUS:85133708178
T3 - LTEDI 2022 - 2nd Workshop on Language Technology for Equality, Diversity and Inclusion, Proceedings of the Workshop
SP - 161
EP - 166
BT - LTEDI 2022 - 2nd Workshop on Language Technology for Equality, Diversity and Inclusion, Proceedings of the Workshop
A2 - Chakravarthi, Bharathi Raja
A2 - Bharathi, B
A2 - McCrae, John P
A2 - Zarrouk, Manel
A2 - Bali, Kalika
A2 - Buitelaar, Paul
PB - Association for Computational Linguistics (ACL)
Y2 - 27 May 2022
ER -