A text representation method and device based on a hierarchical neural network

A neural network and text representation technology, applied in the field of text representation based on hierarchical neural network, can solve the problem of ignoring the internal structural features of text

Inactive Publication Date: 2019-05-07
NAT UNIV OF DEFENSE TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the existing text representation models are based on neural networks trained according to specific tasks, ignoring the internal structural characteristics of the text itself.

Method used

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  • A text representation method and device based on a hierarchical neural network
  • A text representation method and device based on a hierarchical neural network
  • A text representation method and device based on a hierarchical neural network

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Embodiment Construction

[0030] Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that the relative arrangements of components and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.

[0031] At the same time, it should be understood that, for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0032] The following description of at least one exemplary embodiment is merely illustrative in nature and in no way taken as limiting the invention, its application or uses.

[0033] figure 1 It is a schematic flow chart of an embodiment of the text representation method based on layered neural network of the present invention, such as figure 1 Shown:

[0034] Step 101, converting each word cons...

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Abstract

The invention discloses a text representation method and device based on a hierarchical neural network. The method comprises: converting each word forming a sentence into a vector; Inputting vectors corresponding to all words in the sentence into a neural network for aggregation, and outputting sentence representation corresponding to the sentence; Inputting all the sentence representations into aneural network to be aggregated, and generating document representations corresponding to all the sentence representations; And converting the document representation into a document classification vector through a full connection network, and obtaining prediction probability distribution of document classification based on the document classification vector. According to the method and a device,A hierarchical mechanism is introduced into a neural network model to solve a document representation problem for text classification; Interoperability of different tasks is better improved, a hierarchical neural system structure is fused into a neural network method, a new neural network model based on layering is caused, accuracy, performance and the like are obviously superior to those of an existing neural network model, and consumption is lower.

Description

technical field [0001] The present invention relates to the technical field of artificial intelligence, in particular to a text representation method and device based on a layered neural network. Background technique [0002] The learning of text representation is a challenging task for natural language processing (NLP). Transforming the text space into real-valued vectors or matrices is the key to machine understanding of text semantics. For the text generation framework (for words to generate sentences and sentences to generate text), the text representation can be divided into the following levels, word representation, phrase representation, sentence representation and document-level representation. Document representation has broad application prospects, such as sentiment classification, text retrieval, text sorting, etc. The usual text representation methods are bag-of-words model BoW with inverse text frequency TF-IDF and N-gram model N-gram. However, this statistics...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/332G06F16/35G06F17/27G06N3/04G06N3/08
Inventor 陈洪辉邵太华蔡飞舒振陈涛郝泽鹏陈皖玉潘志强郑建明
Owner NAT UNIV OF DEFENSE TECH
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