JOINT RANKING MODEL FOR MULTILINGUAL WEB SEARCH
First Claim
1. In a computing environment, a method comprising, determining similarity between a first document of first language and a second document of a second, different language, and using the similarity in ranking relevance of the second document with respect to a query submitted in the first language.
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Abstract
Described is a technology in which a classifier is built to rank documents of different languages found in a query based at least in part on similarity to other documents and the relevance of those other documents to the query. A joint ranking model, e.g., based upon a Boltzmann machine, is used to represent the content similarity among documents, and to help determine joint relevance probability for a set of documents. The relevant documents of one language are thus leveraged to improve the relevance estimation for documents of different languages. In one aspect, a hidden layer of units (neurons) represents clusters (corresponding to relevant topics) among the retrieved documents, with an output layer representing the relevant documents and their features, and edges representing a relationship between clusters and documents.
36 Citations
20 Claims
- 1. In a computing environment, a method comprising, determining similarity between a first document of first language and a second document of a second, different language, and using the similarity in ranking relevance of the second document with respect to a query submitted in the first language.
- 10. In a computing environment, a system comprising, a classifier that ranks multilingual documents relative to one another, including means for relating features of documents to topic-based clusters of documents, the classifier ranking documents of different languages relative to a query based upon similarity data between documents according to features of those documents.
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16. One or more computer-readable media having computer-executable instructions, which when executed perform steps, comprising:
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featurizing a set of documents of different languages into multilingual features; clustering the set of documents into topic-based clustering data; constructing a Boltzmann machine based on the documents, the features of the documents, and the clustering data; and using the Boltzmann machine to rank documents of different languages returned in a search. - View Dependent Claims (17, 18, 19, 20)
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Specification