Blog Archive: 2010

Slides from CIKM 2010 Reverted Indexing talk

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Here are the slides from our talk at CIKM 2010 last week. More details on reverted indexing can be found in an earlier post and on the FXPAL site, the full paper is available here, and the previous post describes why the technique works. The contribution of the paper can be summarized as follows:

We treat query result sets as unstructured text “documents” — and index them.

Reverted Indexing

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Traditional interactive information retrieval systems function by creating inverted lists, or term indexes. For every term in the vocabulary, a list is created that contains the documents in which that term occurs and its relative frequency within each document. Retrieval algorithms then use these term frequencies alongside other collection statistics to identify the matching documents for a query.

In a paper to be published at CIKM 2010, Jeremy Pickens, Matt Cooper and  I describe a way of using the inverted index to associate document ids with the queries that retrieve them. Our approach combines the inverted index with the notion of retrievability to create an efficient query expansion algorithm that is useful for a number of applications, including relevance feedback. We call this kind of index a reverted index because rather than mapping terms onto documents, it maps document ids onto queries that retrieved the associated documents.

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Query suggestion vs. term suggestion

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Diane Kelly presented an interesting (and much tweeted-about) paper at SIGIR this week. The paper, “A Comparsion of Query and Term Suggestion Features for Interactive Searching,” co-written with Karl Gyllstrom and Earl Bailey, looks at the effects that query and term suggestions have on users’ performance and preferences. These are important topics for interactive information seeking, both for known-item and exploratory search.

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Expanding query expansion

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Looks like I missed a good paper at JCDL 2009: A Polyrepresentational Approach to Interactive Query Expansion by Diriye, Blandford and Tombros. As with many good ideas, this paper describes an approach that is obviously useful once described, but one I had not come across before.

Manual query expansion can be useful when relevance feedback fails because it doesn’t know why a person found a document relevant, but people are often reluctant to use the suggestions offered by information seeking systems. This paper offers a new twist on these recommended terms: When suggesting query terms for expanding a user’s queries, they show terms with some representation of the context in which they occur. Evaluation showed that this contextual information allowed users to understand query terms better, and that it improved their ability to make relevance judgments with respect to documents that contained the suggested terms.

In Cerchiamo, we offered users term suggestions based on relevance judgments made by search partners. While the suggested terms were useful for identifying other relevant documents, they weren’t always used. It’s likely that term recommendation in collaborative search situations would benefit from these techniques even more than in the standalone search because in the collaborative search case term recommendations may be based on documents that a searcher has never seen.