Spoken Document Retrieval Using Extended Query Model and Web Documents.
Kiichi Hasegawa, Masanori Takehara, Satoshi Tamura, Satoru Hayamizu · NTCIR · 2013
This paper proposes a novel approach for spoken document retrieval. In our method, a query model which is one of the probabilistic language models is adopted, in order to computes a probability to generate a given query from each document. We employ not only a “static” document collection consisting of targeted documents but also a “dynamic” document collection including web documents related with queries. We expand the query model so as to incorporate probabilities obtained from “static” and “dynamic” language models using the Dirichlet smoothing. Furthermore, in order to improve retrieved results, we develop a weighting method for web documents. Experiments using NTCIR-9 SpokenDoc Dry-run and NTCIR-10 SpokenDoc-2 Formal-run were conducted, and it is found our proposed scheme has enough performance compared with conventional methods.