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Interaction between Phonological and Semantic Processes in Visual Word Recognition using Electrophysiology

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Kcr-FLAT: A Chinese-Named Entity Recognition Model with Enhanced Semantic Information.

Zhenrong Deng^1,2, Yong Tao², Rushi Lan²

¹Guangxi Key Laboratory of Images and Graphics Intelligent Processing, Guilin University of Electronic Technology, Guilin 541004, China.

Sensors (Basel, Switzerland)

|February 28, 2023

Summary

This study introduces Kcr-FLAT, a novel approach to enhance Chinese named entity recognition (NER) by integrating syntactic and character-level information. The model significantly improves performance on benchmark datasets.

Keywords:

Chinese-named entity recognition cross-transformer regularity perception semantic information syntactic information

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Examining Online Syntactic Processing of Spoken Complex Sentences in Chinese Using Dual-Modal Interference Tasks

Examining Online Syntactic Processing of Spoken Complex Sentences in Chinese Using Dual-Modal Interference Tasks

Published on: September 5, 2019

Area of Science:

Natural Language Processing
Computational Linguistics
Artificial Intelligence

Background:

Chinese Named Entity Recognition (NER) performance has advanced using external data and word enhancement.
However, syntactic composition at the sentence level and inner regularity at the character level remain underexplored for Chinese NER.
Chinese characters exhibit high sensitivity to sentential syntactic data, leading to varied word decompositions based on context.

Purpose of the Study:

To propose Kcr-FLAT, a novel model designed to enhance Chinese NER performance by incorporating enhanced semantic information.
To address the limitations of previous Chinese NER models by focusing on syntactic composition and character-level inner regularity.
To improve the accuracy and robustness of Chinese NER systems.

Main Methods:

Extraction of diverse syntactic data and functionalization using a key-value memory network (KVMN).
Fusion of syntactic information via an attention mechanism.
Integration of syntactic and lexical information using a cross-transformer architecture.
Utilization of an inner regularity perception module for enhanced entity type prediction.

Main Results:

The proposed Kcr-FLAT model achieved high performance on benchmark datasets.
Achieved F1 scores of 96.51% on the MSRA dataset.
Achieved F1 scores of 96.81% on the resume dataset.
Achieved F1 scores of 70.12% on the Weibo dataset.

Conclusions:

Kcr-FLAT effectively enhances Chinese NER by leveraging syntactic and character-level internal regularity.
The model demonstrates superior performance compared to existing methods on multiple Chinese NER datasets.
The findings highlight the importance of considering both sentence-level syntax and character-level patterns for robust Chinese NER.