Three models namely, CoHope-ML, CoHope-NN, and CoHope-TL based on Ensemble of classifiers, Keras Neural Network (NN) and BiLSTM with Conv1d model respectively are proposed for the shared task.
This paper describes the models submitted by the team MUCS for Offensive Language Identification in Dravidian Languages-EACL 2021 shared task that aims at identifying and classifying code-mixed texts of three language pairs namely, Kannada-English (Kn-En), Malayalam-English (Ma-En), and Tamil-English (Ta-En) into six predefined categories (5 categories in Ma-En language pair).
Sentiments/opinions/reviews’ of users posted on social media are the valuable information that have motivated researchers to analyze them to get better insight and feedbacks about any product such as a video in Instagram, a movie in Netflix, or even new brand car introduced by BMW.
These approaches obtained the highest performance in the shared task for Meitei, Bangla, and Multilingual texts with instance-F1 scores of 0. 350, 0. 412, and 0. 380 respectively using Pre-aggregation of labels.
To address the automatic detection of abusive languages in online platforms, this paper describes the models submitted by our team - MUCIC to the shared task on “Abusive Comment Detection in Tamil-ACL 2022”.
Hope is an inherent part of human life and essential for improving the quality of life.
Spreading positive vibes or hope content on social media may help many people to get motivated in their life.
The increase in domain specific text processing applications are demanding tools and techniques for domain specific Text Classification (TC) which may be helpful in many downstream applications like Machine Translation, Summarization, Question Answering etc.
This strict annotation process resulted in promising performance for simple machine learning classifiers with only bi-grams; however, binary and multiclass hope speech detection results reveal that contextual embedding models have higher performance in this dataset.