Improving Probabilistic Automata Learning with Additional Knowledge
Christopher Kermorvant, Colin de la Higuera and Pierre Dupont
Lecture Notes in Computer Science
, Volume 3138
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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