Method for recognizing text segments by using sequence annotation

Abstract

The invention provides a method for recognizing text segments by using sequence annotation, which comprises the following steps: A, respectively segmenting different text segments of a sample set intoclause sets, and annotating the clause sets by using semantic feature vectors to form semantic feature vector sets; B, performing clustering training on the semantic feature vector set to obtain a clustering model, and performing cluster numbering on each object of the clustering model to form a sequence model; C, establishing mapping between the sequence model and the different text fields, andtraining a sequence labeling model for the mapped cluster sequence; and D, sequentially applying the sequence model and the sequence labeling model, and segmenting the text to be segmented. The methodperforms standardized modeling by taking the sample set as a database template. And during subsequent text segmentation recognition, the method includes standardizing the sentence pattern model in the to-be-segmented text, and mapping the standardized sentence to the sentence features according to the model, so that different expressions representing the same semantics can be expressed to complete text segmentation recognition.

Description

technical field [0001] The invention relates to the technical field of word processing, in particular to a method for identifying text segments using sequence annotation. Background technique [0002] With the advent of the era of artificial intelligence, the requirements for the ability of machines to understand large sections of text are getting higher and higher. Due to the complex information content and various writing methods of long texts, it is difficult for machines to directly understand them. Therefore, long texts can be decomposed first, and their key information fragments can be disassembled, and then the fragment information can be further extracted and analyzed one by one. It is a mainstream practice in the industry to adopt the method of dividing and conquering. [0003] In the division of target fragments for long texts, a multi-classification method is currently used, that is, a multi-classification model is established for the clauses, and then the clause...

AI Technical Summary

Problems solved by technology

However, this method lacks context information, and it is difficult to cope with the scattered clause types.

[0004] There is also a deep learning question answering model method, which can achieve end-to-end recognition, but it has relatively high requirements for data volume and data quality, and at the same time, the computational complexity is also high, and the application cost is relatively high.

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