Query Reformulation Using Post-Execution Results Analysis
First Claim
1. A computer-implemented method for search query reformulation, comprising:
- generating a query reformulation candidate for an original query;
receiving a first set of documents in response to a search based on the original query;
receiving a second of documents in response to a search based on the query reformulation candidate;
extracting one or more features that indicate a relevance of the first set of documents to the second set of documents; and
providing the one or more features to a classifier, wherein the classifier determines whether the query reformulation candidate will generate more relevant search results than the original query.
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Abstract
Systems, methods, devices, and media are described to facilitate the training and employing of a three-class classifier for post-execution search query reformulation. In some embodiments, the classification is trained through a supervised learning process, based on a training set of queries mined from a query log. Query reformulation candidates are determined for each query in the training set, and searches are performed using each reformulation candidate and the un-reformulated training query. The resulting documents lists are analyzed to determine ranking and topic drift features, and to calculate a quality classification. The features and classification for each reformulation candidate are used to train the classifier in an offline mode. In some embodiments, the classifier is employed in an online mode to dynamically perform query reformulation on user-submitted queries.
109 Citations
20 Claims
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1. A computer-implemented method for search query reformulation, comprising:
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generating a query reformulation candidate for an original query; receiving a first set of documents in response to a search based on the original query; receiving a second of documents in response to a search based on the query reformulation candidate; extracting one or more features that indicate a relevance of the first set of documents to the second set of documents; and providing the one or more features to a classifier, wherein the classifier determines whether the query reformulation candidate will generate more relevant search results than the original query. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A server device, comprising:
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at least one processor; and a query processing component, executable by the at least one processor and configured to perform operations including; generating a query reformulation candidate for an original query submitted to a search engine; employing the search engine to execute a search based on the original query; receiving a first set of web documents in response to the search based on the original query; employing the search engine to execute a search based on the query reformulation candidate; receiving a second set of documents in response to the search based on the query reformulation candidate; extracting one or more features that indicate a relevance of the first set of web documents to the second set of web documents; and providing the one or more features as input to a multi-class classifier model, wherein the multi-class classifier model determines whether the query reformulation candidate will generate improved search results compared to the original query. - View Dependent Claims (10, 11, 12, 13, 14, 15)
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16. A computer-implemented method for search query reformulation, comprising:
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generating at least one query reformulation candidate for a training query; retrieving one or more candidate search result documents in response to a search based on the at least one query reformulation candidate; retrieving one or more original search result documents in response to a search based on the training query; extracting one or more quality features based on the one or more candidate search result documents and on the one or more original search result documents; computing a quality score for each of the at least one query reformulation candidate, wherein the quality score indicates a relative quality of the at least one query reformulation candidate compared to the training query; based on the computed quality score, classifying each of the at least one query reformulation candidate into one of a set of categories that includes a positive category, a negative category, and a neutral category; employing the classified at least one query reformulation candidate to train a classifier, using a supervised learning method; and employing the classifier to dynamically reformulate one or more online queries received at a search engine. - View Dependent Claims (17, 18, 19, 20)
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Specification