Introduction to Natural Language Processing and Computational Linguistics Lab.

Natural Language Processing and Computational Linguistics （NLP＊CL） Lab. at KAIST seeks to provide a human oriented service for human-human and human-computer interaction by creative fusion of diverse disciplines and methodologies through deep linguistic investigations. Our research interests include in-depth analysis of linguistic phenomena with formal grammars such as CCG and its applications to practical domains such as text-to-sign language generation, early diagnosis of dementia, and information enrichment for advanced biology research.

Text-to-Sign Language Generation

We are developing methods for converting textual information into sign language expressions in order to provide deaf users with a better access to such information. In particular, we are focusing on analyzing the unique characteristics of sign language such as simultaneity and spatiality to produce more natural expressions.

Developing Tools for Early Diagnosis of Dementia

Dementia patients diagnosed earlier would benefit from better quality-of-life, thanks to medical help that slows down the irreversible progress of dementia. Taking advantage of its common symptoms, such as declining performance in memory retrieval and linguistic fluency, we are developing novel screening tools for early dementia diagnosis. Along this line, our research topics include the improvement of existing clinical tests such as category fluency tests and the estimation of the risk for dementia from puzzle game scores.

Information Enrichment for Biology Research

We are developing various techniques and tools that assist knowledge discovery in biology research, based on natural language processing and automated inference. In particular, we focus on （1） text-mining techniques that identify important information from biology research articles and that transform such information into structured formats in order to construct resources such as databases and ontologies, （2） quality-control techniques for the resources and （3） inference techniques that derive novel knowledge from the automatically accumulated information.