Deep learning-based department semantic information extraction method and device

A technology of semantic information and deep learning, applied in the field of department semantic information extraction based on deep learning, can solve the problems of inaccurate identification of specific noun boundaries, difficulty in building a large number of department information sample sets, etc., to solve the problem of polysemy, reduce Quantity, the effect of improving accuracy

Pending Publication Date: 2021-08-17
北京汇声汇语科技有限公司
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Problems solved by technology

[0005] At present, deep learning is still in its infancy in text information extraction. In some cases, existing neural network methods still have the following problems in solving departmental semantic information extraction: it is difficult to construct a large number of departmental information sample sets, and there are special nouns. The problem of inaccurate boundary recognition

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  • Deep learning-based department semantic information extraction method and device
  • Deep learning-based department semantic information extraction method and device
  • Deep learning-based department semantic information extraction method and device

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Embodiment Construction

[0085] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will describe in detail with reference to the drawings and specific embodiments.

[0086] An embodiment of the present invention provides a method for extracting departmental semantic information based on deep learning, which can be implemented by an electronic device, which can be a terminal or a server. Such as figure 1 The flow chart of the method for extracting sectoral semantic information based on deep learning is shown, as shown in figure 2 Shown is a schematic block diagram of a method for extracting departmental semantic information based on deep learning, and the processing flow of this method may include the following steps:

[0087] Step 101, preprocessing the acquired user question data to obtain preprocessed data;

[0088] Step 102, input the pre-processed data into the pre-trained BERT word encoding model to obtain wo...

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Abstract

The invention relates to the technical field of language information processing, in particular to a deep learning-based department semantic information extraction method and device, and the method comprises the steps: carrying out the preprocessing of obtained user question data, and obtaining the preprocessing data; inputting the preprocessed data into a pre-trained BERT word coding model to obtain word vector data; inputting the word vector data into a pre-trained position attention mechanism BiLSTM model to obtain a data labeling result; and based on the data annotation result, extracting department semantic information in the user question data. According to the invention, the accuracy of department information extraction can be improved.

Description

technical field [0001] The present invention relates to the technical field of language information processing, in particular to a method and device for extracting departmental semantic information based on deep learning. Background technique [0002] Semantic information extraction of departments is to identify entities related to departments from telecom user questions, such as unit name, number, department name, number and other entities. Semantic information extraction is the basis of natural language processing tasks such as question answering systems and machine translation. The current research methods mainly include dictionary-based, machine learning-based and deep learning-based methods. [0003] Among them, the dictionary-based method constructs a large number of dictionaries and matches the text to be extracted to extract the departmental information in the text. The dictionary mainly includes information such as units and departments, and the quality of the dict...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/332G06F16/33G06F40/126G06F40/194G06F40/30G06N3/04G06N3/08
CPCG06F16/3329G06F16/3344G06F40/126G06F40/194G06F40/30G06N3/08G06N3/044
Inventor 郝朋丽魏伊赛
Owner 北京汇声汇语科技有限公司
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