Speech act identification using an ontology-based partial pattern tree

This paper presents an ontology-based partial pattern tree to identify the speech act in a spoken dialogue system. This study first extracts the key words/concepts in an application domain using latent semantic analysis (LSA). A partial pattern tree is used to deal with the ill-formed sentence problem in a spoken dialogue system. Concept expansion based on domain ontology is adopted to improve system performance. For performance evaluation, a medical dialogue system with multiple services, including registration information, clinic information and FAQ information, is implemented. Four performance measures were separately used for evaluation. The speech act identification rate achieves 86.2%. A Task Success Rate of 77% is obtained. The contextual appropriateness of the system response is 78.5%. Finally, the correct rate for FAQ retrieval is 82% with an improvement of 15% in comparison with the keyword-based vector space model. The results show the proposed ontology-based partial pattern tree is effective for dialogue management.