Biomedical text mining (including biomedical natural language processing or BioNLP) refers to the methods and study of how text mining may be applied to texts and literature of the biomedical and molecular biology domains. As a field of research, biomedical text mining incorporates ideas from natural language processing, bioinformatics, medical informatics and computational linguistics. The strategies developed through studies in this field are frequently applied to the biomedical and molecular biology literature available through services such as PubMed.
Multilingual Named Entity Recognition (NER) is a computational linguistic task involving the development of models and algorithms that can identify and classify entities such as names of people, places, organizations, and more, across multiple languages. By using techniques like deep learning, transfer learning, and cross-lingual embeddings, Multilingual NER aims to create models that are capable of recognizing entities in different languages, enabling information extraction, cross-lingual knowledge linking, and language-agnostic information retrieval in diverse text sources and applications, ranging from information retrieval to machine translation and cross-lingual information synthesis.
Relation Extraction is a natural language processing task focused on identifying and categorizing the connections or associations between entities mentioned in text. Employing methods such as pattern-based approaches, dependency parsing, and neural networks, Relation Extraction seeks to automatically recognize and classify various types of relationships, such as "is-a," "works-for," or "located-in," within textual data. This technique is pivotal for knowledge graph construction, information retrieval, and understanding complex interactions in fields like biomedicine, social networks, and financial analysis.
Event Extraction is an advanced natural language processing task that involves detecting and structuring complex events or occurrences mentioned within text. Utilizing techniques like syntactic parsing, semantic role labeling, and deep learning models, Event Extraction aims to identify event triggers, participants, time frames, and relationships, facilitating the transformation of unstructured textual data into structured event representations. This process is crucial for generating event databases, tracking developments in news articles, analyzing social media content, and enhancing the understanding of dynamic processes in various domains like journalism, security analysis, and scientific research.
Data Programming and Weak Supervision are innovative approaches in natural language processing that address the challenge of obtaining labeled training data for models when traditional methods are time-consuming or expensive. Data Programming involves creating labeling functions that automatically generate noisy labels from various sources, like heuristics, rules, or distant supervision. Weak Supervision combines multiple noisy or incomplete sources of supervision to create a more accurate training signal. These techniques enable the training of models with larger and more diverse datasets, enhancing performance in applications such as text classification, image recognition, and entity linking.
Argument mining is the automatic identification and extraction of the structure of inference and reasoning expressed as arguments presented in natural language. It aims to extract claims, evidence, premises, and other elements of reasoning from textual sources. Understanding argumentative structure makes it possible to determine not only what positions people are adopting, but also why they hold the opinions they do, providing valuable insights in domains as diverse as debate analysis, discourse analysis, financial market prediction, and public relations etc.
Automatic event extraction from short stories
Multilingual Named-Entity-Recognition
Relation Extraction and Natural Language Generation
Argumentative Structure Mining with a Generative Framework
Argument Evaluation with Argumentation Framework
Complex Networks Theory and Computational Systems Biology
Assistant Professor at Visva-Bharati University, Santiniketan, West Bengal, India
Information Extraction, Knowledge Base Question Answering, Machine Translation
Senior Applied Scientist at Inception Institute of Artificial Intelligence (IIAI), Abu Dhabi, UAE
Disease Progression Modeling, Bioinformatics
Assistant Professor at IIT Jodhpur, Rajasthan, India
Fine-grained entity typing
Dr. Ashish Anand and Dr. Amit Awekar
Consultant II - Analytics And Data Science at Eli Lilly and Company
Decoding alternative splicing code in genome sequence using interpretable machine learning models.
Assistant Professor at IIIT Guwahati, Assam, India
Information Extraction & Deep Learning, Large-scale Relation Extraction
Dr. Ashish Anand and Dr. Amit Awekar
NLP Research Consultant - Advanced Analytics and Data Science at Eli Lilly and Company
Understanding biases in images and related questions in Visual Question Answering datasets
Dr. Prithwijit Guha and Dr. Ashish Anand.
Research Scientist at Samsung Research Institute, Bangalore, India
M Tech, CSE, Completed in 2021
M Tech, CSE, Completed in 2021
M Tech, CSE, Completed in 2021
M Tech, Data Science, Completed in 2021
M Tech, Data Science, Completed in 2021
B Tech, CSE, Completed in 2021
B Tech, CSE, Completed in 2021
B Tech, CSE, Completed in 2021
B Tech, CSE, Completed in 2021
M Tech, CSE, Completed in 2020
M Tech, CSE, Completed in 2020
B Tech, CSE, Completed in 2020
B Tech, CSE, Completed in 2020
B Tech, CSE, Completed in 2020
B Tech, CSE, Completed in 2020
M Tech, CSE, Completed in 2019
M Tech, CSE, Completed in 2019
M Tech, CSE, Completed in 2019
B Tech, CSE, Completed in 2019
B Tech, CSE, Completed in 2019
B Tech, CSE, Completed in 2019
B Tech, CSE, Completed in 2019
B Tech, Mathematics and Computing, Completed in 2019
Co-supervisor: Dr. Gautam Das, Mathematics
Ph D, CSE, Completed in 2018
M Tech, CSE, Completed in 2018
M Tech, CSE, Completed in 2018
M Tech, CSE, Completed in 2018
M Tech, CSE, Completed in 2018
B Tech, CSE, Completed in 2018
B Tech, CSE, Completed in 2018
B Tech, CSE, Completed in 2018
B Tech, EEE, Completed in 2018
B Tech, CSE, Completed in 2018
B Tech, Mathematics and Computing, Completed in 2018
B Tech, EEE, Completed in 2018
M Tech, CSE, Completed in 2017
M Tech, CSE, Completed in 2017
M Tech, CSE, Completed in 2017
M Tech (Joint Guidance with Dr Shirisha Nagotu), BSBE , Completed in 2017
B Tech, CSE, Completed in 2017
B Tech, CSE, Completed in 2017
B Tech, CSE, Completed in 2017
B Tech, CSE, Completed in 2017
M Tech, CSE, Completed in 2016
M Tech, CSE, Completed in 2016
B Tech, CSE, Completed in 2016
B Tech, CSE, Completed in 2016
B Tech, CSE, Completed in 2016
B Tech (Worked with Amit Sethi), CSE, Completed in 2016
M Tech, Completed in 2014
M Tech, Completed in 2014
Summer Intern, 2015 (Under Joint Sc. Academies' Summer Research Fellowship Prog.)
Affiliation: R.V. College of Engineering, Bangalore
