Natural language processing (NLP) is a field of computer science, artificial intelligence and computational linguistics concerned with the interactions between computers and human (natural) languages, and, in particular, concerned with programming computers to fruitfully process large natural language corpora. In CNeRG we focus on areas of NLP like Distributed Semantics, Entity Disambiguation, Code Switching, Sanskrit Linguistics etc.
In multilingual environment code-mixing and code-switching refer to the phenomenon of effortless and natural switching between two or more languages in a single conversation, sometimes even in a single utterance. Our research group focuses on various aspects of code-switching such as function of code switching in social media, language preference for expression of sentiment and […]
Distributional semantics is a broad area of research based on Distributional hypothesis which says linguistic items with similar distributions have similar meanings. This area mostly deals with the theories and methods for quantifying and categorizing semantic similarities between linguistic items based on their distributional properties in large samples of language data. As a part of […]
Named Entity Disambiguation (NED) is a central problem of Information Extraction. The goal is to link entities in a Knowledge Graph to their mention spans in unstructured text. Ex: Michael Jordan is an American scientist, Professor at the University of California, Berkeley and a researcher in machine learning, statistics, and artificial intelligence. Where Michael Jordan is […]