no code implementations • EACL (DravidianLangTech) 2021 • Avishek Garain, Atanu Mandal, Sudip Kumar Naskar
Offensive language identification has been an active area of research in natural language processing.
no code implementations • SEMEVAL 2020 • Avishek Garain, Sainik Mahata, Dipankar Das
This linguistic phenomenon poses a great challenge to conventional NLP domains such as Sentiment Analysis, Machine Translation, and Text Summarization, to name a few.
no code implementations • SEMEVAL 2020 • Avishek Garain
The task was further divided into three sub-tasks: offensive language identification, automatic categorization of offense types, and offense target identification.
no code implementations • 24 Jul 2020 • Avishek Garain, Sainik Kumar Mahata, Dipankar Das
This linguistic phenomenon poses a great challenge to conventional NLP domains such as Sentiment Analysis, Machine Translation, and Text Summarization, to name a few.
no code implementations • 1 Aug 2019 • Avishek Garain, Sainik Kumar Mahata
This paper describes the system submitted to "Sentiment Analysis at SEPLN (TASS)-2019" shared task.
no code implementations • WS 2019 • Sainik Kumar Mahata, Avishek Garain, Adityar Rayala, Dipankar Das, Sivaji Bandyopadhyay
In the current work, we present a description of the system submitted to WMT 2019 News Translation Shared task.
no code implementations • 31 Jul 2019 • Avishek Garain, Sainik Kumar Mahata, Subhabrata Dutta
This paper presents a method to apply Natural Language Processing for normalizing numeronyms to make them understandable by humans.
no code implementations • SEMEVAL 2019 • Avishek Garain, Arpan Basu
This system paper is a description of the system submitted to {''}SemEval-2019 Task 5{''} Task B for the English language, where we had to primarily detect hate speech and then detect aggressive behaviour and its target audience in Twitter.
no code implementations • SEMEVAL 2019 • Avishek Garain, Arpan Basu
This system paper is a description of the system submitted to {``}SemEval-2019 Task 6{''}, where we had to detect offensive language in Twitter.