Abstract

. In this paper we show that clustering alphabet symbols before PDFA inference is performed reduces perplexity on new data. This result is especially important in real tasks, such as spoken language interfaces, in which data sparseness is a significant issue. We describe the application of the ALERGIA algorithm combined with an independent clustering technique to the Air Travel Information System (ATIS) task. A 25 % reduction in perplexity was obtained. This result outperforms a trigram model under the same simple smoothing scheme. 1 Introduction Inference of deterministic finite automaton (DFA) from positive and negative data can be solved by the RPNI algorithm, proposed independently by Trakhtenbrot et al. [16] and by Oncina et al. [13]. This algorithm was used by Lang in his extensive experimental study of learning random deterministic automata from sparse samples [10]. An adapted version of this algorithm proved to be successful in the recent Abbadingo competition [9]. However th...

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