Effective pseudo-relevance feedback for spoken document retrieval

With the exponential proliferation of multimedia associated with spoken documents, research on spoken document retrieval (SDR) has emerged and attracted much attention in the past two decades. Apart from much effort devoted to developing robust indexing and modeling techniques for representing spoken documents, a recent line of thought targets at the improvement of query modeling for better reflecting the user's information need. Pseudo-relevance feedback is by far the most commonly-used paradigm for query reformulation, which assumes that a small amount of top-ranked feedback documents obtained from the initial round of retrieval are relevant and can be utilized for this purpose. Nevertheless, simply taking all of the top-ranked feedback documents obtained from the initial retrieval for query modeling (reformulation) does not always work well, especially when the top-ranked documents contain much redundant or non-relevant information. In the view of this, we explore in this paper an interesting problem of how to effectively glean useful cues from the top-ranked documents so as to achieve more accurate query modeling. To do this, different kinds of information cues are considered and integrated into the process of feedback document selection so as to improve query effectiveness. Experiments conducted on the TDT (Topic Detection and Tracking) task show the advantages of our retrieval methods for SDR.