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Found 11 papers, 1 papers with code
JUNLP@Dravidian-CodeMix-FIRE2020: Sentiment Classification of Code-Mixed Tweets using Bi-Directional RNN and Language Tags
Sentiment analysis has been an active area of research in the past two decades and recently, with the advent of social media, there has been an increasing demand for sentiment analysis on social media texts.
Development of POS tagger for English-Bengali Code-Mixed data
The proposed system is a modular approach that starts by tagging individual tokens with their respective languages and then passes them to different POS taggers, designed for different languages (English and Bengali, in our case).
Preparation of Sentiment tagged Parallel Corpus and Testing its effect on Machine Translation
The preparation of raw parallel corpus, sentiment analysis of the sentences and the training of a Character Based Neural Machine Translation model using the same has been discussed extensively in this paper.
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.
Code-Mixed to Monolingual Translation Framework
The use of multilingualism in the new generation is widespread in the form of code-mixed data on social media, and therefore a robust translation system is required for catering to the monolingual users, as well as for easier comprehension by language processing models.
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.
JUCBNMT at WMT2018 News Translation Task: Character Based Neural Machine Translation of Finnish to English
In the current work, we present a description of the system submitted to WMT 2018 News Translation 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.
SMT vs NMT: A Comparison over Hindi & Bengali Simple Sentences
All of the systems use English-Hindi and English-Bengali language pairs containing simple sentences as well as sentences of other complexity.
Preparing Bengali-English Code-Mixed Corpus for Sentiment Analysis of Indian Languages
To gather attention and encourage researchers to work on this crisis, we prepared gold standard Bengali-English code-mixed data with language and polarity tag for sentiment analysis purposes.
