Abstract
The experiments presented in this paper were aimed at the selection of documents to be used in the blind or pseudo relevance feedback in spoken document retrieval. The previous experiments with the automatic selection of the relevant documents for the blind relevance feedback method have shown the possibilities of the dynamical selection of the relevant documents for each query depending on the content of the retrieved documents instead of just blindly defining the number of the relevant documents to be used in advance. The score normalization techniques commonly used in the speaker identification task are used for the dynamical selection of the relevant documents. In the previous experiments, the language modeling information retrieval method was used. In the experiments presented in this paper, we have derived the score normalization technique also for the vector space information retrieval method. The results of our experiments show, that these normalization techniques are not method-dependent and can be successfully used in several information retrieval system settings.
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Acknowledgments
The work was supported by the Ministry of Education, Youth and Sports of the Czech Republic project No. LM2015071 and by the grant of the University of West Bohemia, project No. SGS-2016-039.
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Skorkovská, L. (2016). Relevant Documents Selection for Blind Relevance Feedback in Speech Information Retrieval. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds) Text, Speech, and Dialogue. TSD 2016. Lecture Notes in Computer Science(), vol 9924. Springer, Cham. https://doi.org/10.1007/978-3-319-45510-5_48
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DOI: https://doi.org/10.1007/978-3-319-45510-5_48
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