Automatic question-answering method based on deep learning
A technology of automatic question answering and deep learning, applied in neural learning methods, natural language data processing, special data processing applications, etc., can solve problems such as inability to truly understand text semantics, semantic gaps, etc., to reduce system development costs and improve relevance , the effect of narrowing the range
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[0053] When a user asks the question "Can ginger cure hair loss", the processing flow of the system is described as follows:
[0054] 1) Use the Elasticsearch full-text search service to select 500 similar questions from the system database, and the 500 questions obtained are all questions that contain common words with the user's questions.
[0055] 2) Use the Jieba word segmentation tool for Chinese word segmentation, set a user-defined dictionary in the Jieba tool, enable stop word removal, and the question after word segmentation is "ginger | treatment | hair loss".
[0056] 3) Calculate the TFIDF of each word in the user question and similar questions.
[0057] 4) Use the BOW vector, TFIDF and Word2Vec model in the construction stage to construct a 200-dimensional text representation vector for user questions and similar questions, where the word vector obtained according to the Word2Vec model is as follows: figure 2 As shown, each word is converted into a 200-dimension...
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