Search Results for author: Fazlourrahman Balouchzahi

Found 10 papers, 1 papers with code

MUCS@LT-EDI-EACL2021:CoHope-Hope Speech Detection for Equality, Diversity, and Inclusion in Code-Mixed Texts

no code implementations EACL (LTEDI) 2021 Fazlourrahman Balouchzahi, Aparna B K, H L Shashirekha

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.

Hope Speech Detection Language Modelling

MUCS@DravidianLangTech-EACL2021:COOLI-Code-Mixing Offensive Language Identification

no code implementations EACL (DravidianLangTech) 2021 Fazlourrahman Balouchzahi, Aparna B K, H L Shashirekha

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).

Language Identification

LA-SACo: A Study of Learning Approaches for Sentiments Analysis inCode-Mixing Texts

no code implementations EACL (DravidianLangTech) 2021 Fazlourrahman Balouchzahi, H L Shashirekha

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.

Transfer Learning

MUCIC@TamilNLP-ACL2022: Abusive Comment Detection in Tamil Language using 1D Conv-LSTM

1 code implementation DravidianLangTech (ACL) 2022 Fazlourrahman Balouchzahi, Anusha Gowda, Hosahalli Shashirekha, Grigori Sidorov

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”.

Abusive Language

MUCS@TechDOfication using FineTuned Vectors and n-grams

no code implementations ICON 2020 Fazlourrahman Balouchzahi, M D Anusha, H L Shashirekha

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.

Machine Translation Question Answering +2

PolyHope: Two-Level Hope Speech Detection from Tweets

no code implementations25 Oct 2022 Fazlourrahman Balouchzahi, Grigori Sidorov, Alexander Gelbukh

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.

Hope Speech Detection

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