Google Inc.

Dekang Lin

John Blitzer

Rahul Gupta

Jakob David Uszkoreit

Jie Zhou

Jun Wu

Engin Cinar Sahin

Fernando Pereira

Zhe Qian

Fang Liu

Ciprian Ioan Chelba

Hongjun Zhu

Stewart Yang

Evgeniy Gabrilovich

Alexandru Marius Pasca

Shaohua Sun

Methods and systems are provided for a question answering. In some implementations, a data element to be updated is identified in a knowledge graph and a query is generated based at least in part on the data element. The query is provided to a query processing engine. Information is received from the query processing engine in response to the query. The knowledge graph is updated based at least in part on the received information.

Question answering to populate knowledge base

A language processing system collects similar queries and respective responses and aggregated by responses. Incorrect responses are determined and filtered by the aggregation. The remaining responses are then used to query a high precision system for attributes of entities specified by the queries. The attribute type is determined from the responses of the high precision system, and corresponding parse rules are generated. The parse rules are then associated with an operation that yields a response that specifies an attribute of the attribute type.

Inducing command inputs from high precision and high recall data

Systems and methods to generate modified/refined user inputs based on the original user input, such as a search query, are disclosed. The method may be implemented for Roman-based and/or non-Roman based language such as Chinese. The method may generally include receiving an original user input and identifying core terms therein, determining potential alternative inputs by replacing core term(s) in the original input with another term according to a similarity matrix and/or substituting a word sequence in the original input with another word sequence according to an expansion/contraction table where one word sequence is a substring of the other, computing likelihood of each potential alternative input, and selecting most likely alternative inputs according to a predetermined criteria, e.g., likelihood of the alternative input being at least that of the original input. A cache containing pre-computed original user inputs and corresponding alternative inputs may be provided.

Suggesting and refining user input based on original user input

A method identifies pairs of first and second command inputs from respective user device sessions for which the first and second operation data are indicative of a first operation failure and a second operation success. The first operation data indicate a first operation performed on data from a first resource property in response to the first command input, and the second operation data indicate a second operation performed on data from a second resource property in response to the second command input. They system determines, from the identified pairs of first and second command inputs, command inputs for which a parsing rule that is associated with the second operation is to be generated.

Inducing command inputs from property sequences

Methods, systems, and apparatus, including computer program products, for extracting information from unstructured text. Fact pairs are used to extract basic patterns from a body of text. Patterns are generalized by replacing words with classes of similar words. Generalized patterns are used to extract further fact pairs from the body of text. The process can begin with fact pairs, basic patterns, or generalized patterns.

Extracting information from unstructured text using generalized extraction patterns

Aspects directed to phrase generation are provided. A method is provided that includes identifying a plurality of phrase candidates from a plurality of text string entries in a corpus. For each phrase candidate: identifying a plurality of left contexts and a plurality of right contexts for the phrase candidate, each left context of the plurality of left contexts being a nearest unique feature to the right of the phrase candidate in a text string entry and each right context of the plurality of right contexts being the nearest unique feature to the right of the phrase candidate, and calculating a left context vector including a score for each left context feature and a right context vector including a score for each right context feature of the phrase candidate. A similarity is determined between pairs of phrase candidates using the respective left and right context vectors for each phrase candidate.

Generating phrase candidates from text string entries

Phrase generation

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Cupertino, CA, United States of America

Dekang Lin