Our Research

Our research in computational linguistics emphasizes issues in lexical semantics, pragmatics, and the social use of language that arise when the methods of computational linguistics are applied to real-world language and real-world problems. The ultimate goal of our research is the development of better computational models of language for use in human--computer interaction and in applications such as information retrieval, text analysis, and machine translation. Two applications that have been especially important in our work are intelligent correction of real-word spelling errors and intelligent linguistic assistance to disabled users.

Several themes underlie the approaches that we take. First, we are concerned with fine-grained nuances of language, as it is really used in the world. Second, there is an emphasis on problems of representation of linguistic and semantic knowledge. Third, the approaches taken are inherently interdisciplinary; the work draws on research in psycholinguistics, philosophy, theoretical linguistics, and sociology. This orientation is particularly suited for research in such applications as machine translation and, more generally, in generation systems in which precision in language is important; in advice-giving systems; in processing long documents for conceptual retrieval; in intelligent tools for writers; and in knowledge acquisition by reading.

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Research by Gerald Penn and students:

My research concerns the study of the structure of human languages as a mathematical and computational system. Not only is natural language processing an integral component of the overall vision of artificial intelligence research, but many of the problems that have defined the rest of AI, logic programming and theoretical computer science research in general can be found inside this very rich empirical domain. More recently, the proliferation of electronically available text over the World Wide Web has created an acute demand for machine translation systems, query answering systems, and text summarisation tools for using this vast source of information. In order to approach their full potential, the next generations of these systems must be capable of providing far more precise information about meaning and the relations between the people, objects and locations described by these texts. Recent advances in wireless technology also require a natural means of interacting with ever more miniature devices, and this can only be achieved through spoken input and output. My research thus seeks to provide both a formal perspective on language to realise or improve such applications, and the algorithms to support them.

The strategy I have adopted to pursue this goal consists of several related threads of research activity in specialised logics for computational linguistics, other means of grammar specification, and their applications.

• Typed feature logic, grammar development, category theory
• Parsing and generation algorithms, indexing
• Substructural logics, parsing with Lambek categorial grammars
• Finite-state transducers, compilation methods, default reasoning
• ALE, constraint logic programming
• Pronunciation modelling, text-to-speech synthesis
• Text summarisation

Research by Suzanne Stevenson and students:

I take a highly multidisciplinary approach to computational linguistics, integrating computational theories and techniques with insights from the fields of linguistics and psycholinguistics.

Currently, a primary focus of my work is the automatic acquisition of linguistic knowledge from large text corpora, using machine learning approaches. Especially challenging is the learning of semantic information about predicates, which is only implicitly represented in text. Another main area of interest is work on cognitive models of human language acquisition and processing. In the latter, I am particularly interested in modelling how human beings so effortlessly come to the intended meaning of an utterance in spite of the high degree of ambiguity in everything we say and hear. I am also very interested in analyzing databases combining words and pictures (such as captioned images on the web), to determine how the words can help disambiguate the images, and vice versa.