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Found 9 papers, 0 papers with code
JUNLP@DravidianLangTech-EACL2021: Offensive Language Identification in Dravidian Langauges
Offensive language identification has been an active area of research in natural language processing.
JUNLP at SemEval-2020 Task 9: Sentiment Analysis of Hindi-English Code Mixed Data Using Grid Search Cross Validation
This linguistic phenomenon poses a great challenge to conventional NLP domains such as Sentiment Analysis, Machine Translation, and Text Summarization, to name a few.
Garain at SemEval-2020 Task 12: Sequence based Deep Learning for Categorizing Offensive Language in Social Media
The task was further divided into three sub-tasks: offensive language identification, automatic categorization of offense types, and offense target identification.
JUNLP@SemEval-2020 Task 9:Sentiment Analysis of Hindi-English code mixed data using Grid Search Cross Validation
This linguistic phenomenon poses a great challenge to conventional NLP domains such as Sentiment Analysis, Machine Translation, and Text Summarization, to name a few.
Sentiment Analysis at SEPLN (TASS)-2019: Sentiment Analysis at Tweet level using Deep Learning
This paper describes the system submitted to "Sentiment Analysis at SEPLN (TASS)-2019" shared task.
JUMT at WMT2019 News Translation Task: A Hybrid approach to Machine Translation for Lithuanian to English
In the current work, we present a description of the system submitted to WMT 2019 News Translation Shared task.
Normalyzing Numeronyms -- A NLP approach
This paper presents a method to apply Natural Language Processing for normalizing numeronyms to make them understandable by humans.
The Titans at SemEval-2019 Task 6: Offensive Language Identification, Categorization and Target Identification
This system paper is a description of the system submitted to {``}SemEval-2019 Task 6{''}, where we had to detect offensive language in Twitter.
The Titans at SemEval-2019 Task 5: Detection of hate speech against immigrants and women in Twitter
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.
