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Krishnanjan Bhattacharjee

Researcher at Centre for Development of Advanced Computing

Publications -  11
Citations -  24

Krishnanjan Bhattacharjee is an academic researcher from Centre for Development of Advanced Computing. The author has contributed to research in topics: Information extraction & Question answering. The author has an hindex of 3, co-authored 11 publications receiving 13 citations.

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Proceedings ArticleDOI

Named Entity Recognition: A Survey for Indian Languages

TL;DR: This study is orchestrated to provide gap analysis in available NER systems for Indian Languages, especially Hindi to develop an efficient system, in terms of accuracy, and catering to more Named Entity tags than available presently in Hindi NER tools.
Proceedings ArticleDOI

Intelligent AI Assisted Psychological Disorder Analysis Using Sentiment Inference

TL;DR: The main aim of the system is to provide a standardized platform for an assistance in diagnosis using Natural Language Processing techniques and Sentiment Inference and suggest to the psychiatrist, the psychological disorder identified and its severity from the document text.
Proceedings ArticleDOI

Survey and Gap Analysis of Word Sense Disambiguation Approaches on Unstructured Texts

TL;DR: A survey on WSD approaches in major AI-NLP methods by comparing different approaches for WSD in supervised, unsupervised, and knowledge based algorithms and based on the findings, a future hybrid approach synergizing rule-based and machine learning based methods are contemplated.
Book ChapterDOI

Sentiment Extraction from Image-Based Memes Using Natural Language Processing and Machine Learning

TL;DR: A novel approach is given (correlating the facial features along with the text in the meme itself) for the extraction of sentiment from image-based memes in a bid to understand the usage patterns of the public.
Proceedings ArticleDOI

Graph-based Question Answering System

TL;DR: This project aims at developing a graph based information extraction and retrieval system that handles relationships and connections very elegantly in graph databases.