Google Inc.

Thomas Richard Strohmann

Trystan G Upstill

John Blitzer

Dimitra Papachristou

Sundeep Tirumalareddy

Daniel Glasner

Hayden Shaw

Wei Fan

Sushrut Suresh Karanjkar

Pi-Chuan Chang

Suddha Kalyan Basu

Wenyuan Yin

Robert B Avery

Manas Ashok Pathak

Shubhang Verma

Kedar Dhamdhere

P Pandurang Nayak

Robert Spalek

Methods, systems, and apparatus including computer programs encoded on a computer storage medium, for retrieving image search results using embedding neural network models. In one aspect, an image search query is received. A respective pair numeric embedding for each of a plurality of image-landing page pairs is determined. Each pair numeric embedding is a numeric representation in an embedding space. An image search query embedding neural network processes features of the image search query and generates a query numeric embedding. The query numeric embedding is a numeric representation of the image search query in the same embedding space. A subset of the image-landing page pairs having pair numeric embeddings that are closest to the query numeric embedding of the image search query in the embedding space are identified as first candidate image search results.

Embedding based retrieval for image search

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for index-side synonym expansion. One method includes obtaining a token sequence for a resource and indexing a token in the token sequence. The indexing includes applying one or more stemming rules to the particular token to generate a stemmed form of the token, obtaining a variant of the stemmed form of the token, and storing data associating the resource with both the token and the variant as index terms for the resource in a search engine index.

Index-side stem-based variant generation

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for collecting query term substitution data based on one or more identified concepts. According to one implementation, a method includes receiving a query that includes at least three sequential query terms; determining that the sequential query terms represent a concept; and in response to determining that the sequential query terms represent a concept, collecting query term substitution data for one or more query terms that occur in queries that include the concept.

Using concepts as contexts for query term substitutions

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for identifying and scoring restricted-locality synonyms. In one aspect, a method includes receiving a search query including a query term and a synonym for the query term. The synonym is evaluated using one or more predetermined criteria and based on the evaluation is designated as a restricted-locality synonym. A first scoring model is selected that specifies how to score occurrences of restricted-locality synonyms in documents. A document is received that is identified as being responsive to the search query. A document score for the document is determined using the first scoring model.

Restricted-locality synonyms

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Cupertino, CA, United States of America

Thomas Richard Strohmann