Search Results for author: Sudip Kumar Naskar

Found 37 papers, 5 papers with code

A Rule Based Lightweight Bengali Stemmer

no code implementations ICON 2020 Souvick Das, Rajat Pandit, Sudip Kumar Naskar

In this paper, we study and review existing works on stemming in Bengali and other Indian languages.

Information Retrieval Retrieval

Convolutional Neural Networks can achieve binary bail judgement classification

no code implementations25 Jan 2024 Amit Barman, Devangan Roy, Debapriya Paul, Indranil Dutta, Shouvik Kumar Guha, Samir Karmakar, Sudip Kumar Naskar

There is an evident lack of implementation of Machine Learning (ML) in the legal domain in India, and any research that does take place in this domain is usually based on data from the higher courts of law and works with English data.

Attentive Fusion: A Transformer-based Approach to Multimodal Hate Speech Detection

2 code implementations19 Jan 2024 Atanu Mandal, Gargi Roy, Amit Barman, Indranil Dutta, Sudip Kumar Naskar

With the recent surge and exponential growth of social media usage, scrutinizing social media content for the presence of any hateful content is of utmost importance.

Hate Speech Detection

Enhancing AI Research Paper Analysis: Methodology Component Extraction using Factored Transformer-based Sequence Modeling Approach

no code implementations5 Nov 2023 Madhusudan Ghosh, Debasis Ganguly, Partha Basuchowdhuri, Sudip Kumar Naskar

Research in scientific disciplines evolves, often rapidly, over time with the emergence of novel methodologies and their associated terminologies.

Evaluating Impact of Social Media Posts by Executives on Stock Prices

1 code implementation1 Nov 2022 Anubhav Sarkar, Swagata Chakraborty, Sohom Ghosh, Sudip Kumar Naskar

This paper investigates the impact of social media posts on close price prediction of stocks using Twitter and Reddit posts.

Time Series Time Series Analysis

FiNCAT: Financial Numeral Claim Analysis Tool

1 code implementation26 Jan 2022 Sohom Ghosh, Sudip Kumar Naskar

It extracts context embeddings of the numerals using one of the transformer based pre-trained language model called BERT.

Language Modelling regression

A Deep Learning Approach to Integrate Human-Level Understanding in a Chatbot

no code implementations31 Dec 2021 Afia Fairoose Abedin, Amirul Islam Al Mamun, Rownak Jahan Nowrin, Amitabha Chakrabarty, Moin Mostakim, Sudip Kumar Naskar

Extracting the client reviews from conversations by using chatbots, organizations can reduce the major gap of understanding between the users and the chatbot and improve their quality of products and services. Thus, in our research we incorporated all the key elements that are necessary for a chatbot to analyse and understand an input text precisely and accurately.

Chatbot intent-classification +6

Is Attention always needed? A Case Study on Language Identification from Speech

no code implementations5 Oct 2021 Atanu Mandal, Santanu Pal, Indranil Dutta, Mahidas Bhattacharya, Sudip Kumar Naskar

Language Identification (LID) is a crucial preliminary process in the field of Automatic Speech Recognition (ASR) that involves the identification of a spoken language from audio samples.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +4

Spyder: Aggression Detection on Multilingual Tweets

no code implementations LREC 2020 Anisha Datta, Shukrity Si, Urbi Chakraborty, Sudip Kumar Naskar

In the last few years, hate speech and aggressive comments have covered almost all the social media platforms like facebook, twitter etc.

Improving CAT Tools in the Translation Workflow: New Approaches and Evaluation

no code implementations WS 2019 Mihaela Vela, Santanu Pal, Marcos Zampieri, Sudip Kumar Naskar, Josef van Genabith

User feedback revealed that the users preferred using CATaLog Online over existing CAT tools in some respects, especially by selecting the output of the MT system and taking advantage of the color scheme for TM suggestions.

Automatic Post-Editing Management +1

The Transference Architecture for Automatic Post-Editing

no code implementations COLING 2020 Santanu Pal, Hongfei Xu, Nico Herbig, Sudip Kumar Naskar, Antonio Krueger, Josef van Genabith

In automatic post-editing (APE) it makes sense to condition post-editing (pe) decisions on both the source (src) and the machine translated text (mt) as input.

Automatic Post-Editing NMT

JU-Saarland Submission to the WMT2019 English--Gujarati Translation Shared Task

no code implementations WS 2019 Riktim Mondal, Shankha Raj Nayek, Aditya Chowdhury, Santanu Pal, Sudip Kumar Naskar, Josef van Genabith

In this paper we describe our joint submission (JU-Saarland) from Jadavpur University and Saarland University in the WMT 2019 news translation shared task for English{--}Gujarati language pair within the translation task sub-track.

Machine Translation NMT +1

JU\_ETCE\_17\_21 at SemEval-2019 Task 6: Efficient Machine Learning and Neural Network Approaches for Identifying and Categorizing Offensive Language in Tweets

1 code implementation SEMEVAL 2019 Preeti Mukherjee, Mainak Pal, Somnath Banerjee, Sudip Kumar Naskar

This paper describes our system submissions as part of our participation (team name: JU{\_}ETCE{\_}17{\_}21) in the SemEval 2019 shared task 6: {``}OffensEval: Identifying and Catego- rizing Offensive Language in Social Media{''}.

Language Identification Word Embeddings

Keep It or Not: Word Level Quality Estimation for Post-Editing

no code implementations WS 2018 Prasenjit Basu, Santanu Pal, Sudip Kumar Naskar

The paper presents our participation in the WMT 2018 shared task on word level quality estimation (QE) of machine translated (MT) text, i. e., to predict whether a word in MT output for a given source context is correctly translated and hence should be retained in the post-edited translation (PE), or not.

Language Modelling Machine Translation +1

Neural Automatic Post-Editing Using Prior Alignment and Reranking

no code implementations EACL 2017 Santanu Pal, Sudip Kumar Naskar, Mihaela Vela, Qun Liu, Josef van Genabith

APE translations produced by our system show statistically significant improvements over the first-stage MT, phrase-based APE and the best reported score on the WMT 2016 APE dataset by a previous neural APE system.

Automatic Post-Editing NMT +2

Multi-Engine and Multi-Alignment Based Automatic Post-Editing and its Impact on Translation Productivity

no code implementations COLING 2016 Santanu Pal, Sudip Kumar Naskar, Josef van Genabith

In the paper we show that parallel system combination in the APE stage of a sequential MT-APE combination yields substantial translation improvements both measured in terms of automatic evaluation metrics as well as in terms of productivity improvements measured in a post-editing experiment.

Automatic Post-Editing Translation

CATaLog Online: Porting a Post-editing Tool to the Web

no code implementations LREC 2016 Santanu Pal, Marcos Zampieri, Sudip Kumar Naskar, Tapas Nayak, Mihaela Vela, Josef van Genabith

The tool features a number of editing and log functions similar to the desktop version of CATaLog enhanced with several new features that we describe in detail in this paper.

Machine Translation Management +1

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