In this paper, we introduce an architecture designed to achieve effective plan recognition using Bayesian Networks which encode the semantic representation of the user's utterances. The structure of the networks is determined from dialogue corpora, thus eliminating the high cost process of hand-coding domain knowledge. The conditional probability distributions are learned during a training phase in which data are obtained by the same set of dialogue acts. Furthermore, we have incorporated a module that learns semantic similarities of words from raw text corpora and uses the extracted knowledge to resolve the issue of the unknown terms, thus enhancing plan recognition accuracy, and improves the quality of the discourse. We present experimental results of an implementation of our platform for a weather information system and compare its performance against a similar, commercial one. Results depict significant improvement in the context of identifying the goals of the user. Moreover, we claim that our framework could straightforwardly be updated with new elements from the same domain or adapted to other domains as well.
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