Overview of CCKS 2020 Task 3: Named Entity Recognition and Event
                    Extraction in Chinese Electronic Medical Records

Many studies have demonstrated that incorporating lexical information into characters can effectively improve the performance of Chinese Named Entity Recognition (CNER). However, we argue that previous studies have not extensively explored the interactive relationship between characters and lexical information, and have only used the lexical information to enhance character-level representation. To address this limitation, we propose an interactive fusion approach that integrates characters and lexical information for CNER. Specifically, we first design graph attention networks to initially fuse character and lexical information within an interactive graph structure. Additionally, by introducing methods such as feedforward neural networks, residual connections, and layer normalization, the fusion effect of the graph attention network is further enhanced. Finally, concatenating and reducing dimensionality of character feature vectors and lexical feature vectors to achieve secondary fusion, thereby obtaining a more comprehensive feature representation. Experimental results on multiple datasets demonstrate that our proposed model outperforms other models that fuse lexical information. Particularly, on the CCKS2020 and Ontonotes datasets, our model achieves higher F1 scores than previous state-of-the-art models. The code is available via the link: https://github.com/wangye0523/The-interactive-fusion-of-characters-and-lexical-information-for-Chinese-named-entity-recognition.

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An effective deep learning method with multi-feature and attention mechanism for recognition of Chinese rice variety information

et al. 2022

Multimed Tools Appl

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Overview of CCKS 2020 Task 3: Named Entity Recognition and Event Extraction in Chinese Electronic Medical Records

Cited by 14 publications

References 19 publications

Hybrid Granularity-Based Medical Event Extraction in Chinese Electronic Medical Records

Hybrid Granularity-Based Medical Event Extraction in Chinese Electronic Medical Records

The interactive fusion of characters and lexical information for Chinese named entity recognition

An effective deep learning method with multi-feature and attention mechanism for recognition of Chinese rice variety information

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