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Induction of Linguistic Knowledge
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Machine Learning for Language Engineering and Linguistics
The growing availability of language corpora combined with large
computing power has opened up exciting new possibilities for
computer-aided language processing. ILK, Induction of Linguistic
Knowledge, is a research programme in which inductive learning
algorithms are developed and employed in solving natural language
problems. The research is intended to be useful in two areas:
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Language Engineering. In language engineering, building
applications is often hampered by the knowledge acquisition
bottleneck: it is hard to make linguistic knowledge explicit `by
hand', and to make it reusable for different language tasks or
languages. The application of inductive learning holds great promise
for alleviating this bottleneck. It has been shown that the
application of inductive learning results in more accurate and robust
systems than those developed by hand. Moreover, the learning methods
are language-independent. Automatically learned models can be put into
direct practice as modules in language and speech engineering
applications.
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Linguistic and Cognitive Modelling. As a new method in
linguistic analysis, inductive learning allows us to discover
linguistic knowledge in corpora, dictionaries, and sets of language
examples. They also provide a new perspective on old philosophical and
cognitive science problems concerning the representation and
acquisition of linguistic knowledge.
The research programme, directed by dr Walter Daelemans, is part of CLS (Center for Language Studies, Faculty of Arts,
Tilburg University), and is sponsored by NWO (Netherlands Organization for Scientific
Research, Foundation for Language, Speech, and Logic), SOBU (Cooperation
Association Tilburg and Eindhoven Universities), and STW (Foundation for the
Technical Sciences, The Netherlands).
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