Chinese Character Relationship Extraction Method Based on BERT

2021 ◽  
Author(s):  
Dengtao Liu ◽  
Qianchao Wang
Keyword(s):  
Extraction Method ◽  
Chinese Character ◽  
Relationship Extraction

scholarly journals Relationship Classification based on Dependency Parsing and Pre-training Model

2021 ◽  
Author(s):  
Baosheng Yin ◽  
Yifei Sun
Keyword(s):  
Information Extraction ◽  
Extraction Method ◽  
Training Model ◽  
Context Information ◽  
Dependency Parsing ◽  
Improve Performance ◽  
Natural Language Text ◽  
Relationship Extraction ◽  
Entity Relationship ◽  
Language Text

Abstract As an important part of information extraction, relationship extraction aims to extract the relationships between given entities from natural language text. On the basis of the pre-training model R-BERT, this paper proposes an entity relationship extraction method that integrates entity dependency path and pre-training model, which generates a dependency parse tree by dependency parsing, obtains the dependency path of entity pair via a given entity, and uses entity dependency path to exclude such information as modifier chunks and useless entities in sentences. This model has achieved good F1 value performance on the SemEval2010 Task 8 dataset. Experiments on dataset show that dependency parsing can provide context information for models and improve performance.

A NEW STROKE EXTRACTION METHOD OF CHINESE CHARACTERS

2001 ◽  
Vol 15 (04) ◽  
pp. 707-721 ◽  
Author(s):  
XINGMING SUN ◽  
LIHUA YANG ◽  
Y. Y. TANG ◽  
YUNFA HU
Keyword(s):  
Information Processing ◽  
Character Recognition ◽  
Extraction Method ◽  
Chinese Character ◽  
Document Analysis ◽  
Chinese Characters ◽  
Stroke Extraction ◽  
Novel Method ◽  
And Storage ◽  
Document Compression

Stroke extraction of Chinese characters plays an important role in Chinese character information processing such as character recognition, document analysis, document compression and storage, font automation and so on. By analyzing the structure of Chinese characters deeply, this paper developed a novel method to extract strokes of Chinese characters directly from the original character pattern image. Two theorems, eight rules and an algorithm for stroke extraction of Chinese characters are presented. This method can overcome the difficulties encountered in disposing the intersection or connection of different strokes, and can eliminate noises successfully. Our experiments have shown that this method can extract strokes both accurately and efficiently.

Research on Feature Extraction Method for Handwritten Chinese Character Recognition Based on Supervised Independent Component Analysis

2013 ◽  
Vol 774-776 ◽  
pp. 1636-1641
Author(s):  
Ze Min Liu ◽  
Zhi Guo He ◽  
Yu Dong Cao
Keyword(s):  
Feature Extraction ◽  
Independent Component Analysis ◽  
Character Recognition ◽  
Extraction Method ◽  
Chinese Character ◽  
Complex Structure ◽  
Recognition Rate ◽  
Component Analysis ◽  
Independent Component ◽  
Feature Extraction Method

Feature extraction is very difficult for handwritten Chinese character because of large Chinese characters set, complex structure and very large shape variations. The recognition rate by currently used feature extraction methods is far from the requirements of the people. For this problem, a new supervised independent component analysis (SICA) algorithm based on J-divergence entropy is proposed for feature extraction, which can measure the difference between different categories. The scheme takes full advantage of good extraction local features capability and powerful capability to handle data with non-Gaussian distribution by ICA, and the extracted feature component and classification can be tightly combined. The experiments show that the feature extraction method based on SICA is superior to that of gradient-based and that of ICA.

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