Computational linguistics at Maryland has two aspects. One, known as "computational psycholinguistics", uses computational models to better understand how people understand, generate, and learn language, and to characterize the human language capacity as a formal computational system. Models formalize theories to explain human data, and these theories are then tested in empirical experiments. Researchers at Maryland have particular interests in using models to investigate problems in phonetics and phonology, psycholinguistics, and language acquisition.
Computational linguistics also has a practical side, sometimes referred to as "natural language processing" or "human language technology". Here the goal is to make computers smarter about human language, improving the automated analysis and generation of text, with results that can interact effectively with other information systems.
These two strands of computational linguistics are connected by shared methods (such as Bayesian models), a shared concern with grounding theories in naturally occurring linguistic data, and a shared view of language as a fundamentally computational system for which formally explicit models and theories can be specified, designed, and tested.
Our department has close ties to the Computational Linguistics and Information Processing Laboratory (CLIP Lab) at UMD's Institute for Advanced Computer Studies, where colleagues from Linguistics, Computer Science, and the College of Information Studies (iSchool) work together to advance the state of the art in such areas as machine translation, automatic summarization, information retrieval, question answering, and computational social science.