International Business Machines Corporation

Bar Weiner

Haggai Roitman

Shai Erera

Doron Cohen

Guy Feigenblat

Yosi Mass

Oren Sar-Shalom

Roee Shraga

A computer-implemented method, computerized apparatus and computer program product for query performance prediction, the method comprising: obtaining a result list comprising a listing of documents retrieved from a collection in response to a query; obtaining for each of the listed documents in the result list a score indicating a measure of the document's relevance to the query; sampling the result list to obtain a plurality of sub-lists each of which comprising a listing of documents subsumed by the result list; for each of the plurality of sub-lists, analyzing scores of the documents listed therein to obtain a sample performance estimator; and estimating performance of the result list based on the sample performance estimator of each of the plurality of sub-lists.

Method and system for query performance prediction

In a computerized information retrieval system: executing a search based on a query, to retrieve a set of tables ranked according to their relevancy to the query, wherein each of the tables includes one or more columns; selecting, from the retrieved tables, a predefined number of highest-ranking tables; scoring each column in the highest-ranking tables using a link analysis algorithm, and selecting, from the scored columns, a predefined number of highest-scoring columns; scoring terms contained within each of the highest-scoring columns, and selecting, from the scored terms, a predefined number of highest-scoring terms; re-ranking the highest-ranking tables by using the highest-scoring terms as pseudo relevance feedback that expands the query; and providing, as a response to the query, at least one of: the re-ranked tables, ordered according to the re-ranking, and data contained in at least one of the re-ranked tables, wherein the data are ordered according to the re-ranking.

Column-based query expansion for table retrieval

An exemplary method includes: determining a pool of documents, wherein each document is within at least one of a plurality of lists, each of the lists results from executing a query on a corpus, and the corpus comprises at least the pool of documents; determining a first ranking of documents within the pool of documents based at least in part on first scores computed for respective documents within the pool; estimating relevance to the specified query at least of respective documents within the first ranking, wherein the relevance is estimated without user feedback regarding the relevance; and determining a second ranking of documents within the pool based at least in part on second scores computed at least for respective documents within the first ranking, wherein the second score for a given document is computed based at least in part on the estimated relevance of at least the given document.

Utilizing pseudo-relevance feedback in fusion-based retrieval

Techniques are disclosed for query performance prediction (QPP) in the fusion-based retrieval setting. Symmetric list similarity measures used in traditional QPP techniques do not properly account for relevance-dependent aspects of the relationship between a given (base) reference list generated using an information retrieval technique and a final fused list generated using a fusion technique, as such a relationship is actually asymmetric. Embodiments more properly model the asymmetric relationship of reference and fused lists using an asymmetric co-relevance model that estimates, assuming a reference list contains relevant information, the odds that the fused list will be observed. In particular, the asymmetric co-relevance between a reference list and a fused list may be determined by adjusting a symmetric co-relevance of the reference list and the fused list using an odds ratio between the symmetric co-relevance of the reference list and the fused list to the reference list's own relevance.

Enhanced query performance prediction for information retrieval systems

Embodiments of the present systems and methods may provide techniques that provide improved information retrieval. For example, a method may comprise receiving, at the computer system, a query to retrieve a document from a corpus of documents, retrieving, at the computer system, a plurality of documents from the corpus of documents using a plurality of retrieval methods, each retrieval method generating a ranked list of retrieved documents and a score for each document, fusing, at the computer system, the generated ranked list of retrieved documents to form an aggregated ranked list of retrieved documents by re-scoring, at the computer system, the plurality of documents according to its passage scores, with respect to the query and associating, at the computer system, a given document and its maximal passage using relevance information induced from the plurality of ranked lists.

Utilizing passages in fusion-based document retrieval

A method for automated selection of a search result ranker comprising: providing a set of queries; for each of said queries, receiving, from a search engine, a plurality of relevancy score sets, wherein each relevancy score set is associated with search results found in a corpus of electronic documents using each of a plurality of computerized search result rankers; calculating a difficulty score for each of said queries relative to all other queries in the set, based on said plurality of relevancy score sets associated with said query; calculating a quality score for each of said search result rankers based on said plurality of relevancy score sets associated with said search result ranker, wherein each of said plurality of relevancy score sets is weighed according to the difficulty score of its associated query; and selecting one of said search rankers based on said quality score.

Automated selection of search ranker

Methods and systems for generating and evaluating fused query lists. A query on a corpus of documents is evaluated using a plurality of retrieval methods and a ranked list for each of the plurality of retrieval methods is obtained. A plurality of fused ranked lists is sampled, each fusing said ranked lists for said plurality of retrieval methods, and the sampled fused ranked lists are sorted. In an unsupervised manner, an objective comprising a likelihood that a fused ranked list, fusing said ranked lists for each of said plurality of retrieval methods, is relevant to a query and a relevance event, is optimized to optimize the sampling, until convergence is achieved. Documents of the fused ranked list are determined based on the optimization.

Unsupervised learning to fuse for information retrieval

A computer implemented method for estimating quality of document retrieval comprising: retrieving from a corpus of documents stored on at least one storage a plurality of digital documents which comply with a document retrieval query according to a retrieval model; computing a plurality of retrieval scores each calculated for one of the plurality of digital documents using a relevance function scoring a relevance of one of the retrieved plurality of digital documents to the query; computing a calibrated weighted product model (WPM) estimator by calculating a combination of the plurality of retrieval scores weighted according to a plurality of retrieval features of the corpus and/or the query and/or a document, wherein the plurality of retrieval features are weighted according to a relative importance; and using the calibrated WPM estimator to score the plurality of digital documents' relevance to the query.

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Bar Weiner