Google Inc.

Stewart Yang

Fang Liu

Pei Cao

Dekang Lin

Hongjun Zhu

Xin Liu

Yuewei Wang

Algis P Rudys

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for processing event data. In one aspect, a method includes assigning events to event bundles based on timestamps associated with the events. Each event bundle contains events having timestamps that are within a pre-specified period of time. Event batches are created, where each event batch includes a pre-specified number of event bundles. A first event batch is provided to a first computing group and a second computing group. The first computing group is configured to perform a first processing stage, and the second computing group is configured to perform a second processing stage. A determination is made that a threshold number of the event bundles in the first event batch have been processed by the first computing group. In response to the determination, a second event batch is provided to each of the computing groups.

Deterministic data processing

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for identifying non-compositional compounds. In one aspect, a method includes the actions of receiving a collection of phrases, each phrase including two or more words; for each phrase, determining if the phrase is a non-compositional compound, a non-compositional compound being a phrase of two or more words where the words composing the phrase have different meanings in a compound than their conventional meanings individual, the determining including: identifying a similar term for a term of the phrase, substituting the similar term for the term of the phrase to generate a substitute phrase, calculating a similarity between the phrase and the substitute phrase, and identifying the phrase as a non-compositional compound when the calculated similarity is less than a specified threshold value.

Identifying non-compositional compounds

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

Methods, systems and apparatus, including computer program products, for identifying properties of an electronic document. In one aspect, a sequence of bytes representing text in a document is received. A plurality of byte-n-grams are identified from the bytes. For multiple encodings, a respective likelihood of each byte-n-gram occurring in each of the respective multiple encodings is identified. A respective encoding score for each of the multiple encodings is determined. A most likely encoding of the document is identified based on a highest encoding score among the encoding scores. In another aspect, a sequence of characters, having an encoding, are identified in a document. The sequence is segmented into features, each corresponding to two or more characters. A respective score for each of multiple languages is determined based on the features and a respective language model. A language of the document is identified based on the scores.

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Stewart Yang