Google Inc.

Other

Technion Research & Development Foundation Limited

Shaul Markovitch

Lev Finkelstein

Tomer Shmiel

Yossi Matias

Evgeniy Gabrilovich

Eran Ofek

Yosi Markovich

Isaac Elias

Dan Guez

Giora Unger

Artiom Myaskouvskey

Itzhak Barokas

Dror Eiger

Technology described herein enhances a user's search experience by providing refined search results that are relevant to a displayed document. Contextual search results are obtained which identify a list of documents responsive to a formulated query that is based on the user's search query, as well as one or more supplemental terms that are based on content in the displayed document during user entry of the search query. The contextual search results are then 'refined' by re-ranking the documents in the list, based on the similarity between the user's original search query and terms in these documents. This re-ranking enables contextual search results to be provided that are also highly relevant to the user's informational need.

Query formulation and search in the context of a displayed document

The present invention relates to the identification of alternative suggestions which potentially improve on a given query suggestion, without being perceived by a user as being offensively different from the user's query. The alternative suggestions may for example be different query formulations that relate to the same topic as that of the given query suggestion. The technology disclosed uses similarity screening of the given query suggestion against unique queries which do not include the given query suggestion as a prefix, in conjunction with query utility scores representing prior user response to the unique queries.

Modifying a user query to improve the results

A method of providing weighted concepts related to a sequence of one or more words, including: providing on a computer an encyclopedia with concepts and a document explaining each concept, forming a vector, which contains the frequency of the word for each concept, for each word in the encyclopedia, arranging the vector according to the frequency of appearance of the word for each concept, selecting the concepts with the highest frequencies for each word from the vector, truncating the rest of the vector, inducing a feature generator using the truncated vectors; wherein the feature generator is adapted to receive as input one or more words and provide a list of weighted concepts, which are most related to the one or more words provided as input.

Text categorization using external knowledge

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Haifa, Israel

Shaul Markovitch