APPLYING COMPLETELY-ARBITRARY PASSAGE FOR PSEUDO-RELEVANCE FEEDBACK IN LANGUAGE MODELING APPROACH

Abstract

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-arbitraty 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.