Nested Named Entity Recognition Based on Semantic Segmentation

doi:10.3969/j.issn.1006-2475.2024.02.011

Abstract

Abstract: Abstract： Named entity recognition aims to extract entities from an unstructured text， and a nested structure often exists between entities. However， most of the previous studies only focused on the recognition of flat named entities while ignoring nested entities. Therefore， a nested named entity recognition method based on semantic segmentation is proposed， which describes the task of nested named entity recognition as a semantic segmentation task. First， we calculate the element similarity， cosine similarity and bilinear similarity between words and words. Then， the 3 similarity features are spliced as an image which will be input into the semantic segmentation model to obtain the relationship matrix between words and words. Finally， we extract nested entity from the relationship matrix. The experimental results show that the proposed method can effectively recognize nested entities， and the F1 value on the public nested named entity recognition dataset GENIA reaches 80.0%， which is superior to most existing nested entity recognition methods.

Key words: Key words： nested named entity recognition, relation matrix, semantic segmentation, correlation feature

CLC Number:

TP391.1

CUI Shao-guo, HU Guang-ping. Nested Named Entity Recognition Based on Semantic Segmentation[J]. Computer and Modernization, 2024, 0(02): 69-74.

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