Projects

The ability to extract causal relationships from textual data can have far-reaching impacts in fields such as healthcare, finance, and environmental science, where understanding cause-and-effect dynamics is critical for decision-making and problem-solving.

Multimodal NLP research explores the integration of information from multiple sources, such as text, images, and audio, fostering a more comprehensive understanding of language and communication. By combining modalities, this field aims to enhance natural language processing applications, enabling machines to interpret and generate content in a more contextually aware and human-like manner.

Named Entity Recognition (NER) plays a crucial role in information extraction by identifying and classifying entities such as people, organizations, and locations in unstructured text, providing a structured framework for understanding and organizing valuable information.

Multilingual and low-resource NLP research addresses the challenges of working with languages that have limited linguistic resources, expanding the reach of natural language processing technologies to diverse linguistic contexts.

Lexical Complexity Prediction (LCP) is a burgeoning field in NLP that aims to quantify and assess the complexity of vocabulary and language structure in texts. Researchers in LCP are developing models and methodologies to automatically predict the lexical complexity of written content, providing valuable insights into language difficulty and aiding in applications such as language education and content accessibility assessment.