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An end-to-end automatic analysis method for English discourse structure based on pipeline mode

An automatic analysis and discourse technology, applied in semantic analysis, character and pattern recognition, natural language data processing, etc., to achieve the effect of improving accuracy

Active Publication Date: 2020-06-05
BEIJING INSTITUTE OF TECHNOLOGYGY
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  • Claims
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Problems solved by technology

At the same time, aiming at the problem that linguistic features cannot dig out deeper semantics, by carefully analyzing the characteristics of non-explicit discourse relationship recognition and using the advantages of word pair features, a non-explicit discourse relationship recognition model based on deep learning is proposed.

Method used

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  • An end-to-end automatic analysis method for English discourse structure based on pipeline mode
  • An end-to-end automatic analysis method for English discourse structure based on pipeline mode
  • An end-to-end automatic analysis method for English discourse structure based on pipeline mode

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Embodiment Construction

[0051] The present invention will be described in detail below with reference to the accompanying drawings and examples. The experimental methods used in the following examples are conventional methods unless otherwise specified.

[0052] The first is the training step, such as figure 1 As shown, the process is as follows:

[0053] 1. Prepare the training corpus, the implementation steps are as follows:

[0054] (1) Using section 02-21 in the Pennsylvania Discourse Treebank (PDTB) version 2.0 as the training corpus, for explicit discourse relations, extract the corresponding connectives, the range of arguments (Arg1, Arg2), and discourse relations category and the corresponding original text, and obtain the corresponding part-of-speech tagging and syntactic analysis; for the non-explicit discourse relationship, extract the corresponding argument range, discourse relationship category, and the corresponding part-of-speech tagging and syntactic analysis results of the original...

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Abstract

The present invention involves an automatic analysis method based on the end -to -end English chapter structure based on a pipeline mode, which belongs to the field of natural language processing application technology; first of all, the present invention first uses the traditional method to identify the traditional method.For the recognition of informal chapter relationships, we cannot dig deeper semantics and the problem of data sparseness and semantic gaps brought about by the characteristics of linguistic characteristics. By carefully analyzing the characteristics of the recognition of the informal chapter relationship, the use of the advantages of the characteristics of the characteristics of the characteristicsThe informal chapter relationship identification model based on deep learning was proposed.Compared with existing technology, the invention improves the accuracy of the entire end -to -end system.

Description

technical field [0001] The present invention relates to an end-to-end English discourse structure automatic analysis method based on pipeline mode, in particular to an explicit discourse relationship analysis method based on the combination of hybrid convolution tree kernel and polynomial kernel and a non-explicit discourse relationship analysis method based on deep learning. The present invention relates to a textual relationship analysis method, which belongs to the technical field of natural language processing applications. Background technique [0002] Text analysis has always been the core task of natural language processing. The text context information and text-level semantic information it provides are of great significance to other tasks of natural language processing such as machine translation, sentiment analysis, and automatic question answering. Discourse structure analysis is one of the important ways of discourse analysis, which aims to study the composition ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F40/211G06F40/30G06K9/62
CPCG06F40/211G06F40/30G06F18/2414G06F18/2411
Inventor 鉴萍张鹏程黄河燕
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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