Method for determining candidate input, input prompting method and electronic device

    公开(公告)号:US11050685B2

    公开(公告)日:2021-06-29

    申请号:US16011216

    申请日:2018-06-18

    Abstract: A method for determining a candidate input is provided. Text that precedes and/or follows a current text input location in an input interface is acquired. A candidate sentence is acquired based on the text which is contained in a current sentence that corresponds to the current text input location. A preset number of possible conjunctions for the candidate sentence are determined based on occurrence probabilities of the possible conjunctions that are determined for the candidate sentence. The preset number of possible conjunctions are connected to the candidate sentence to obtain predicted candidate sentences. Occurrence probabilities of the predicted candidate sentences are calculated according to the text that precedes and/or follows the current text input location. Further, a preset number of the predicted candidate sentences are provided based on the calculated occurrence probabilities of the predicted candidate sentences as candidate inputs.

    Semantic analysis method and apparatus, and storage medium

    公开(公告)号:US11366970B2

    公开(公告)日:2022-06-21

    申请号:US16672121

    申请日:2019-11-01

    Abstract: A semantic analysis method includes: inputting a word vector of each word in each sample sentence in a dialog flow into an encoder model, to obtain a sentence vector representing semantics of the sample sentence; inputting the sentence vector into a first decoder model corresponding to each context sentence of the sample sentence and a second decoder model corresponding to each word of the sample sentence, to obtain a first identifier corresponding to the context sentence and a second identifier corresponding to the word; obtaining a probability of the first identifier and a probability of the second identifier, and determining a value of a target function; performing parameter training on the encoder model according to the value of the target function; and inputting a word vector of each word in a test sentence into the trained encoder model, to obtain a sentence vector representing semantics of the test sentence.

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