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<p><it>Abstract</it>—This article describes a new method for building a natural language understanding (NLU) system, in which the system’s rules are learnt automatically from training data. The method has been applied to design of a speech understanding (SU) system. Designers of such systems rely increasingly on robust matchers to perform the task of extracting meaning from one or several word sequence hypotheses generated by a speech recognizer; a robust matcher processes semantically important islands of words and constituents rather than attempting to parse the entire word sequence. We describe a new data structure, the Semantic Classification Tree (SCT), that learns semantic rules from training data and can be a building block for robust matchers for NLU tasks. By reducing the need for handcoding and debugging a large number of rules, this approach facilitates rapid construction of an NLU system. In the case of an SU system, the rules learned by an SCT are highly resistant to errors by the speaker or by the speech recognizer because they depend on a small number of words in each utterance. Our work shows that semantic rules can be learned automatically from training data, yielding successful NLU for a realistic application.</p>
Speech understanding, semantic classification tree, SCT, machine learning, natural language, decision tree.

R. Kuhn and R. De Mori, "The Application of Semantic Classification Trees to Natural Language Understanding," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 17, no. , pp. 449-460, 1995.
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