Neural network-based boundary combination named entity recognition method

Abstract

The invention discloses a neural network-based boundary combination named entity recognition method, which comprises the following steps of 1, extracting the entity boundary information based on a neural network model, and constructing a boundary recognition model; 2, implementing a boundary combination strategy, and combining the entity boundaries to obtain a candidate entity set; and step 3, constructing a neural network classifier, and screening the candidate entity set. According to the method disclosed by the present invention, by employing the boundary combination strategies and introducing the neural network techniques, the characteristic that the neural network automatically extracts the high-dimensional abstract features in a layered manner is fully exerted; by dividing the entityrecognition into three steps of boundary recognition, boundary combination and candidate entity recognition, the defects of a traditional sequence model are overcome, and the problem of feature sparseness generated by a traditional machine learning method is avoided to a certain extent, so that the performance of the nested named entity recognition is improved, and a very good effect is achieved.

Description

technical field [0001] The invention relates to a named entity recognition method, in particular to a neural network-based boundary combination named entity recognition method, belonging to the technical fields of natural language processing and machine learning. Background technique [0002] With the popularity of computers and the rapid development of the Internet, a large amount of information appears in front of people in the form of electronic documents. In order to cope with the severe challenges brought by the information explosion, there is an urgent need for professional automated tools to extract truly valuable information from massive amounts of data, and information extraction has emerged as the times require. Named entities refer to the proper nouns in the text that represent the names of people, places, and organizations. As an important semantic knowledge carrier in the text, named entity recognition plays an important role in information extraction. After it ...

AI Technical Summary

Problems solved by technology

Sequence models set tags through each character at the sentence level to obtain the most probable labeling path, but cannot effectively identify internal nested entities; grammatical analysis is identified by using a grammatical analysis tree, but often depends on the effect of grammatical analysis; based on embedding The nested model can better deal with the nesting problem of named entity recognition

However, these methods have four shortcomings: first, they are all in the sentence expansion task, and there is a problem of sparse features; second, in the sequence model, changing the annotations of internal (or external) entities will not be conducive to feature weighting; third, Treating different classes separately will not be able to effectively use tag information; finally, entity recognition is affected to a certain extent by cascading errors caused by word segmentation or grammatical parsing

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