Text abstract and sentiment classification combined training method

A technology for sentiment classification and training methods, applied in text database clustering/classification, neural learning methods, text database query, etc.

Active Publication Date: 2020-03-27
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In the previous research work, the text summarization and sentiment classification tasks were trained separately through the model, so that the joint expression of the two tasks could not be well learned between the two models.

Method used

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  • Text abstract and sentiment classification combined training method
  • Text abstract and sentiment classification combined training method
  • Text abstract and sentiment classification combined training method

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Embodiment Construction

[0014] All features disclosed in the present invention, or steps in all methods or processes disclosed, can be combined in any way, except for mutually exclusive features or steps. Any feature disclosed in this specification (including any appended claims, abstract and drawings), unless expressly stated otherwise, may be replaced by alternative features which are equivalent or serve a similar purpose. That is, unless expressly stated otherwise, each feature is one example only of a series of equivalent or similar features.

[0015] In the present invention, a hierarchical end-to-end model is designed to jointly train sentiment classification and text summary tasks. The hierarchical end-to-end model includes a text summary layer and a sentiment classification layer. The text summary layer combines the source text Compressed into short sentences to generate text summaries; and the sentiment classification layer further summarizes the generated text summaries into an emotional ca...

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Abstract

The invention provides a text abstract and sentiment classification combined training method, which is realized by adopting a text abstract and sentiment classification combined model, and specifically comprises the following steps of: preprocessing a text, and constructing a training set vocabulary; constructing a text abstract model, and carrying out text abstract task pre-training; adding an emotion classification layer on the basis of a text abstract model, constructing a layered end-to-end model, and jointly training emotion classification and text abstract tasks. The invention provides atext abstract and sentiment classification combined training method. Through combined training of the two types of tasks, the content consistency between the generated abstract and the input text canbe improved, the generated abstract can better contain emotion information of the input text, key information of the input text is extracted through the abstract task, and emotion prediction is moreaccurate.

Description

technical field [0001] The invention relates to a text summarization and sentiment classification method in the field of natural language processing, in particular to a joint training method based on text summarization and sentiment classification. Background technique [0002] With the explosive growth of text information in recent years, people are exposed to massive amounts of text information every day, such as news, microblogs, blogs, reports, papers, etc. Text summarization has a wide range of application scenarios. Intuitively, it can be used to generate news titles, paper keywords, abstracts, etc.; broadly speaking, text summarization technology can also be applied to the result optimization of search engines such as Google and Baidu. The task of extracting key information and forming a refined expression can be solved by automatic text summarization technology. The mainstream methods of text summarization are divided into two categories: extractive (Extractive) and...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/35G06F16/34G06F16/33G06F40/289G06N3/04G06N3/08
CPCG06F16/35G06F16/3344G06F16/345G06N3/049G06N3/084G06N3/045
Inventor 高建彬潘慧
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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