Applying Completely-Arbitrary Passage for Pseudo-Relevance Feedback in Language Modeling Approach (original) (raw)

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 nonrelevant terms from non-relevant parts in a document. The most recent work of passage-level feedback has been investigated from the viewpoint of the fixedwindow type of passage. However, the fixed-window type of passage has limitation in optimizing the passage-level feedback, since it includes a queryindependent 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 singlestage 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.