Hybrid vector space model for flexible voice search
Cheongjae Lee, Tatsuya Kawahara · 2012
Abstract—This paper addresses incorporation of semantic analysis into information retrieval (IR) based on the vector space model (VSM) for flexible matching of spontaneous queries in a voice search system. Information of semantic slots or concepts that correspond to database fields is expected to help enhancing IR, but the semantic analyzer often fails or needs a large amount of training data. We propose a hybrid model which combines dedicated VSMs for concept slots with a general VSM as a backoff. The model has been evaluated in a book search task and shown to be effective and robust against ASR and SLU errors. Index Terms: spoken language understanding, voice search, vector space model