NDLI: Query revision during cluster based search on large unstructured corpora

Content Provider	IEEE Xplore Digital Library
Author	Deolalikar, V.
Copyright Year	2014
Description	Author affiliation: Hewlett-Packard Res., Sunnyvale, CA, USA (Deolalikar, V.)
Abstract	We investigate a frequently occurring issue in search (retrieval) in the age of big unstructured data. Searches conducted on large unstructured corpora result in long results lists. Such results lists are often clustered and reranked for ease of navigation. Should a query be revised during time-critical examinations of such long cluster based reranked lists? This question arises naturally during early stages of commercially important applications of IR such as eDiscovery, but has not yet been given any research attention. Four factors compound the difficulty of this question in the setting of eDiscovery: (a) the query sources (the technical experts) are different from the legal staff that are actually executing the query and using the retrieval system, (b) the retrieved lists for each query tend to be very long, and (c) the user might be accessing these retrieved results through a clustering interface, and (c) all decisions must be transparent and easy to explain due to the litigious nature of the application. Analogous difficulties arise in other applications involving search over large unstructured corpora. We introduce a framework to help users make the decision of “whether to revise.” Our framework consists of two components. First, we introduce a “limited view” which is a summary of a long cluster-based reranked list. This is the first input to the user. This provides the user a summary of the long cluster-based list. Second, we construct query predictors for this limited view, and provide their prediction as a second input to the user. This prediction is used to corroborate the inspection of the summary limited view. The proposed combination of a limited view and query performance prediction can assist search staff in determining whether to pursue an expensive query revision or not, as well as save precious time by precluding inspections of lists with very few relevant documents during the early stages of commercially important applications such as eDiscovery.
Starting Page	845
Ending Page	853
File Size	849761
Page Count	9
File Format	PDF
ISBN	9781479956661
DOI	10.1109/BigData.2014.7004314
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-10-27
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Measurement Law Clustering algorithms Search problems Vectors Standards
Content Type	Text
Resource Type	Article

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