Different from the traditional document-level feedback, passage-level feedback restricts the context of selecting relevant terms to a passage in a document, rather than to the entire document. It can thus avoid the selection of non-relevant terms from non-relevant parts in a document. The most recent work of passage-level feedback has been investigated from the viewpoint of the fixed-window type of passage. However, the fixed-window type of passage has limitation in optimizing the passage-level feedback, since it includes a query-independent portion. To minimize the query-independence of the passage, this paper proposes a new type of passage, called completely-arbitrary passage. Based on this, we devise a novel two-stage passage feedback - which consists of passage-retrieval and passage-extension as sub-steps, unlike previous single-stage passage feedback relying only on passage retrieval. Experimental results show that the proposed two-stage passage-level feedback much significantly improves the document-level feedback than the single-stage passage feedback that uses the fixed-window type of passage. © 2008 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Na, S. H., Kang, I. S., Lee, Y. H., & Lee, J. H. (2008). Applying completely-arbitrary passage for pseudo-relevance feedback in language modeling approach. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4993 LNCS, pp. 626–631). https://doi.org/10.1007/978-3-540-68636-1_74
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