Bi-LSTM label recommendation method based on attention mechanism

A recommendation method and attention technology, applied in the label recommendation field of Bi-LSTM, can solve problems such as the expansion of software information site label collection

Active Publication Date: 2019-12-13
CHONGQING UNIV
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Problems solved by technology

And because there are differences in expression habits among different users, for the same problem, they may give labels with similar meanings but different descriptions, for example,

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  • Bi-LSTM label recommendation method based on attention mechanism
  • Bi-LSTM label recommendation method based on attention mechanism
  • Bi-LSTM label recommendation method based on attention mechanism

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Embodiment Construction

[0118] In order to further illustrate the various embodiments, the present invention provides accompanying drawings, which are part of the disclosure of the present invention, and are mainly used to illustrate the embodiments, and can be used to explain the operating principles of the embodiments in conjunction with the relevant descriptions in the specification, for reference Those of ordinary skill in the art should be able to understand other possible implementations and advantages of the present invention. The components in the figures are not drawn to scale, and similar component symbols are generally used to represent similar components.

[0119] According to an embodiment of the present invention, a Bi-LSTM label recommendation method based on attention mechanism is provided.

[0120] Now in conjunction with accompanying drawing and specific embodiment the present invention is further described, as Figure 1-6 As shown, according to the label recommendation method of th...

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Abstract

The invention discloses a Bi-LSTM label recommendation method based on an attention mechanism. The Bi-LSTM label recommendation method based on the attention mechanism comprises the following steps: collecting an experimental data set; analyzing specific text data from the experimental data set; preprocessing the text data; extracting semantic features from the preprocessed question text description; constructing a multi-label classification model; recommending a proper label for the new problem through the constructed multi-label classification model; and evaluating and analyzing a label recommendation result. The method has the beneficial effects that the label recommendation task is mainly converted into the multi-label classification problem through the Bi-LSTM model based on the attention mechanism, the labels are automatically recommended according to the text description content of the problem, and the label recommendation accuracy is improved.

Description

technical field [0001] The present invention relates to the technical field of tag recommendation, in particular to a Bi-LSTM tag recommendation method based on attention mechanism. Background technique [0002] Today, the rapid development of human science and technology has not only significantly improved people's living standards, but also brought great convenience to people's daily life. The ever-changing Internet technology is an important part of science and technology since the 21st century. It has gradually become a necessity of people's life from scratch. Compared with traditional ways of obtaining information such as telephones, radios, televisions, and broadcasts, the transmission of information through the Internet has the advantages of large amount of information transmission, diversified information, and fast information transmission speed. It is precisely because of the diversity, convenience and timeliness of information delivered by Internet technology that...

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Application Information

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IPC IPC(8): G06F16/35G06F16/335G06F17/27G06N3/04G06N3/08
CPCG06F16/35G06F16/335G06N3/08G06N3/044G06N3/045Y02D10/00
Inventor 徐玲李灿何健军杨梦宁张小洪杨丹葛永新洪明坚王洪星黄晟陈飞宇
Owner CHONGQING UNIV
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