PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Improving Probabilistic Automata Learning with Additional Knowledge
Christopher Kermorvant, Colin de la Higuera and Pierre Dupont
(2004) Lecture Notes in Computer Science , Volume 3138 . Springer , Germany . ISBN 3-540-22570-6


In this paper, we propose a way of incorporating additional knowledge in probabilistic automata inference, by using typed automata. We compare two kinds of knowledge that are introduced into the learning algorithms. A statistical clustering algorithm and a part-of-speech tagger are used to label the data according to statistical or syntactic information automatically obtained from the data. The labeled data is then used to infer correctly typed automata. The inference of typed automata with statistically labeled data provides language models competitive with state-of-the-art n-grams on the Air Travel Information System (ATIS) task.

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EPrint Type:Book
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
Theory & Algorithms
ID Code:97
Deposited By:Colin de la Higuera
Deposited On:20 May 2004