TCMNER and PubMed: A Novel Chinese Character-Level-Based Model and a Dataset for TCM Named Entity Recognition

Abstract

Intelligent traditional Chinese medicine (TCM) has become a popular research field by means of prospering of deep learning technology. Important achievements have been made in such representative tasks as automatic diagnosis of TCM syndromes and diseases and generation of TCM herbal prescriptions. However, one unavoidable issue that still hinders its progress is the lack of labeled samples, i.e., the TCM medical records. As an efficient tool, the named entity recognition (NER) models trained on various TCM resources can effectively alleviate this problem and continuously increase the labeled TCM samples. In this work, on the basis of in-depth analysis, we argue that the performance of the TCM named entity recognition model can be better by using the character-level representation and tagging and propose a novel word-character integrated self-attention module. With the help of TCM doctors and experts, we define 5 classes of TCM named entities and construct a comprehensive NER dataset containing the standard content of the publications and the clinical medical records. The experimental results on this dataset demonstrate the effectiveness of the proposed module.

A Multigranularity Text Driven Named Entity Recognition CGAN Model for Traditional Chinese Medicine Literatures

“…For the TCM NER task, we chose several typical methods with which to compare the proposed approach. We selected two advanced methods: the BiLSTM-CRF architecture [ 4 ], BERT-BiLSTM-CRF architecture [ 13 ], and a variant of the BERT model architecture that can fuse multigranularity information of text (Roberta-c) [ 33 ]. These methods are described as follows.…”

“…Roberta-c [ 33 ] is a variant of the BERT model and outperforms the BERT model in Chinese NLP tasks. C denotes a word-character set in a self-attention module and it can integrate character and word information.…”

Surgical procedure long terms recognition from Chinese literature incorporating structural feature

“…NER is widely used in Chinese medical texts, involving electronic medical records [ 18 , 19 ], traditional Chinese medicine texts [ 20 , 21 ], clinical guidelines [ 22 ], disease subtypes [ 23 ], admission notes [ 24 ], PICO [ 25 ] disease and plant name [ 26 ], e. tc. Most approaches focus on feature extraction either by rule and dictionary-based means or by machine learning and deep learning means [ 27 , 28 ].…”

A Named Entity Recognition Method Enhanced with Lexicon Information and Text Local Feature

“…This problem makes many named entity recognition models designed for English corpus ineffective in the dataset of ancient Chinese medicine books. In view of the unclear boundary of Chinese medicine entities, the existing Chinese medicine named entity recognition methods mainly use combined characters and word embedding as the input of the model [2,3] to help the model more accurately model word features. In these methods, the word acquisition method is the Chinese word segmentation tool, while the text language of ancient Chinese medicine books is mostly ancient Chinese, which has a large gap with modern Chinese, leading to the occurrence of incorrect word segmentation, and thus affecting the performance of the model.…”