Autores
Shahiki Tash Moein
Armenta Segura Jesús Jorge
Ahani Zahra
Kolesnikova Olga
Sidorov Grigori
Gelbukh Alexander
Título LIDOMA@DravidianLangTech: Convolutional Neural Networks for Studying Correlation Between Lexical Features and Sentiment Polarity in Tamil and Tulu Languages
Tipo Congreso
Sub-tipo Memoria
Descripción 3rd Workshop on Speech and Language Technologies for Dravidian Languages, DravidianLangTech 2023
Resumen With the prevalence of code-mixing among speakers of Dravidian languages, DravidianLangTech proposed the shared task on Sentiment Analysis in Tamil and Tulu at RANLP 2023. This paper presents the submission of LIDOMA, which proposes a methodology that combines lexical features and Convolutional Neural Networks (CNNs) to address the challenge. A fine-tuned 6-layered CNN model is employed, achieving macro F1 scores of 0.542 and 0.199 for Tulu and Tamil, respectively. © DravidianLangTech 2023 - 3rd Workshop on Speech and Language Technologies for Dravidian Languages, associated with 14th International Conference on Recent Advances in Natural Language Processing, RANLP 2023 - Proceedings.
Observaciones DOI 10.26615/978-954-452-085-4_025
Lugar Varna
País Bulgaria
No. de páginas 180-185
Vol. / Cap.
Inicio 2023-09-07
Fin
ISBN/ISSN 9789544520854