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PERFORMING SEMANTIC ANALYSES OF USER-GENERATED TEXTUAL AND VOICE CONTENT
PERFORMING SEMANTIC ANALYSES OF USER-GENERATED TEXTUAL AND VOICE CONTENT
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机译:对用户生成的文本和语音内容进行语义分析
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摘要
Performing semantic analysis on a user-generated text string includes training a neural network model with a plurality of known text strings to obtain a first distributed vector representation of the known text strings and a second distributed vector representation of a plurality of words in the known text strings, computing a relevance matrix of the first and second distributed representations based on a cosine distance between each of the plurality of words and the plurality of known text strings, and performing a latent dirichlet allocation (LDA) operation using the relevance matrix as an input to obtain a distribution of topics associated with the plurality of known text strings.
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