Sequence labeling method based on deep learning and application

Abstract

The invention discloses a sequence labeling method based on deep learning and application. The method comprises the following steps: acquiring a to-be-labeled text, and preprocessing the text; performing text translation processing and error correction processing on the preprocessed text; constructing a rule dictionary and a regular expression, pre-annotating the text after error correction processing based on the rule dictionary and the regular expression, and outputting pre-annotated content; and inputting the pre-labeled text into a plurality of sequence labeling models based on deep learning, and calculating the optimal labeling content according to the weight values of the plurality of sequence labeling models for the output result. According to the method, the sequence labeling efficiency and accuracy can be improved.

Description

technical field [0001] The invention belongs to the technical field of NLP natural language processing, and relates to a sequence labeling method and application based on a deep learning model. Background technique [0002] In the application process of knowledge graph, sequence tagging is an indispensable link. Sequence tagging models are widely used in text processing related fields, such as word segmentation, part-of-speech tagging, and named entity recognition. In the past, data labeling required a lot of manpower and material resources for labeling, which was inefficient. Ordinary labeling software could not adapt to multi-language labeling. The entity pronoun model of the corpus could not be well identified and extracted, and ordinary labeling tools could not be included in a targeted manner. Thesaurus cannot realize automatic labeling of labeled corpus. Although the previous labeling software has multiple models, it does not use multiple models to jointly label, or si...

AI Technical Summary

Problems solved by technology

In the past, data labeling required a lot of manpower and material resources for labeling, which was inefficient. Common labeling software could not adapt to multilingual labeling. The entity pronoun model of the corpus could not be well identified and extracted, and common labeling tools could not be targeted. Thesaurus, for the tagged corpus, cannot be automatically tagged. Although the previous tagging software has multiple models, it does not use multiple models for joint tagging, or simply predicts the results separately with multiple models, and takes the mode to determine the final result. Labeling results, labeling speed and accuracy greatly affect the update of the model, so improving the efficiency and accuracy of labeling tools is very important for the construction of knowledge graphs

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