WORD CLASSIFICATION BASED ON PHONETIC FEATURES

    公开(公告)号:US20200327281A1

    公开(公告)日:2020-10-15

    申请号:US16915298

    申请日:2020-06-29

    Applicant: Google LLC

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for obtaining a textual term; determining, by one or more computers, a vector representing a phonetic feature of the textual term; comparing the vector representing the phonetic feature of the textual term with a reference vector representing a phonetic feature of a reference textual term; and classifying the textual term based on the comparing the vector with the reference vector.

    Determining word senses using neural networks

    公开(公告)号:US10460229B1

    公开(公告)日:2019-10-29

    申请号:US15464053

    申请日:2017-03-20

    Applicant: Google LLC

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for disambiguating word sense. One of the methods includes maintaining a respective word sense numeric representation of each of a plurality of word senses of a particular word; receiving a request to determine the word sense of the particular word when included in a particular text sequence, the particular text sequence comprising one or more context words and the particular word; determining a context numeric representation of the context words in the particular text sequence; and selecting a word sense of the plurality of word senses having a word sense numeric representation that is closest to the context numeric representation as the word sense of the particular word when included in the particular text sequence.

    DYNAMIC SUMMARY GENERATOR
    3.
    发明申请

    公开(公告)号:US20180276210A1

    公开(公告)日:2018-09-27

    申请号:US15968034

    申请日:2018-05-01

    Applicant: Google LLC

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving a request for information about a term from a user; obtaining, at one or more processors, one or more characteristics of the user; obtaining, at the one or more processors, a template including a plurality of attributes associated with the term; generating, at the one or more processors and based on the one or more characteristics of the user, a response, the response including a respective description for each attribute of the plurality of attributes in the template; and providing for output, data representing the response.

    Dynamic summary generator
    4.
    发明授权

    公开(公告)号:US09965474B2

    公开(公告)日:2018-05-08

    申请号:US14505107

    申请日:2014-10-02

    Applicant: Google LLC

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving a request for information about a term from a user; obtaining, at one or more processors, one or more characteristics of the user; obtaining, at the one or more processors, a template including a plurality of attributes associated with the term; generating, at the one or more processors and based on the one or more characteristics of the user, a response, the response including a respective description for each attribute of the plurality of attributes in the template; and providing for output, data representing the response.

    Generating feature embeddings from a co-occurrence matrix

    公开(公告)号:US10685012B2

    公开(公告)日:2020-06-16

    申请号:US15424671

    申请日:2017-02-03

    Applicant: Google LLC

    Abstract: Methods, and systems, including computer programs encoded on computer storage media for generating compressed representations from a co-occurrence matrix. A method includes obtaining a set of sub matrices of a co-occurrence matrix, where each row of the co-occurrence matrix corresponds to a feature from a first feature vocabulary and each column of the co-occurrence matrix corresponds to a feature from a second feature vocabulary; selecting a sub matrix, wherein the sub matrix is associated with a particular row block and column block of the co-occurrence matrix; assigning respective d-dimensional initial row and column embedding vectors to each row and column from the particular row and column blocks, respectively; and determining a final row embedding vector and a final column embedding vector by iteratively adjusting the initial row embedding vectors and the initial column embedding vectors using the co-occurrence matrix.

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