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Intelligent question answering method based on depth learning

An intelligent question answering and deep learning technology, applied in neural learning methods, special data processing applications, instruments, etc., can solve problems such as large construction complexity, reduce complexity and avoid error transmission.

Inactive Publication Date: 2018-12-21
百卓网络科技有限公司
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AI Technical Summary

Problems solved by technology

For example, chat dialogue tasks, if the traditional method is used, lengthy processes such as word segmentation, keyword extraction, keyword matching, and similarity calculation are required. Errors generated in each step may have an impact on other steps, which makes the original traditional method The construction complexity is very large

Method used

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  • Intelligent question answering method based on depth learning
  • Intelligent question answering method based on depth learning
  • Intelligent question answering method based on depth learning

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Embodiment Construction

[0034] The present invention will be further described below in conjunction with accompanying drawing and exemplary embodiment:

[0035] Such as figure 1 As shown, the flow of the exemplary embodiment includes the following steps:

[0036] Step 1, collect the original dialogue data, and use the Internet crawler technology to grab the comment data of a social network platform with a level of one million, including question data and comment data, which are recorded as source_data and target_data respectively;

[0037] Step 2, data preprocessing, first segment the original dialogue data (including question data and comment data). Chinese word segmentation technology is an independent research field, and the technology is complicated. This article will not repeat the specific technical details, but directly use the ready-made The word segmentation tool (Jieba) also converts text into numbers that the model can understand by using One-Hot Encoding encoding. One-hot encod...

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Abstract

The invention discloses an intelligent question answering method based on depth learning, comprising the following steps: step 1, collecting original conversation data; Step 2, data preprocess, 3, construct a Seq2seq model, establishing an encode layer (Encoder) and a decoding lay (Decoder), and connecting that Encoder layer and the Decoder layer to obtain the Seq2seq model; 4, model prediction, wherein that model prediction is a Seq2seq model construct according to the above, and aft the Seq2seq model is trained with the original conversation data, the problem data is taken as input, and themodel automatically generates comment data. Through deep learning, the problem is directly mapped to the answer, so as to optimize the whole question answering method, avoid the problem of error transmission, and greatly reduce the complexity of the system.

Description

technical field [0001] The invention relates to the field of natural language processing, in particular to an intelligent question answering method based on deep learning. Background technique [0002] As the age of artificial intelligence brings, the fields at the forefront are conversational interactions (personal assistants or chatbots) and computer vision and autonomous driving – thanks to advances in hardware and big data, and machines that are revolutionizing Learn the technology (huge progress on a scale in just a few years). Advances in artificial intelligence have made it possible to solve problems that were previously considered beyond the reach of machines, and this technological product has become a commodity within our daily lives. [0003] The essence of personal assistants or chatbots is mostly open-field intelligent question answering. In the current question answering system, the idea based on retrieval matching combined with traditional machine learning is...

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Application Information

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IPC IPC(8): G06F17/30G06N3/04G06N3/08
CPCG06N3/08G06N3/044
Inventor 钟力夏宇房鹏展
Owner 百卓网络科技有限公司
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