Named Entity Recognition of TCM Text Based on Bert Model

Objective To address the issues in named entity recognition (NER) in the field of traditional Chinese medicine (TCM), this study proposes a method for identifying entities in TCM herbal literature; Methods We identify and describe the types of knowledge entities and entity relationships involved in herbal literature. We apply the BIO sequence labeling method to generate a training corpus dataset and use our self-developed CNLP text annotation system for text annotation. The Bert model is employed for recognizing named entities; Results The Bert model achieved entity recognition results for various entities in TCM herbal literature with precision (P) of 71.49%, recall (R) of 72.33%, and F1 score of 71.91%; Conclusion The Bert model demonstrates a certain level of applicability in recognizing various entities in TCM herbal literature. This model is helpful in extracting valuable structured information from a large volume of text data.

In recent years, as cybersecurity threats have become increasingly severe and cyberattacks have occurred frequently, higher requirements have been put forward for cybersecurity protection. Therefore, the Named Entity Recognition (NER) technique, which is the cornerstone of Cyber Threat Intelligence (CTI) analysis, is particularly important. However, most existing NER studies are limited to recognizing single-layer flat entities, ignoring the possible nested entities in CTI. On the other hand, most of the existing studies focus on English CTIs, and the existing models performed poorly in a limited number of Chinese CTI studies. Given the above challenges, we propose in this paper a novel unified model, RBTG, which aims to identify flat and nested entities in Chinese CTI effectively. To overcome the difficult boundary recognition problem and the direction-dependent and distance-dependent properties in Chinese CTI NER, we use Global Pointer as the decoder and TENER as the encoder layer, respectively. Specifically, the Global Pointer layer solves the problem of the insensitivity of general NER methods to entity boundaries by utilizing the relative position information and the multiplicative attention mechanism. The TENER layer adapts to the Chinese CTI NER task by introducing an attention mechanism with direction awareness and distance awareness. Meanwhile, to cope with the complex feature capture of hierarchical structure and dependencies among Chinese CTI nested entities, the TENER layer solves the problem by following the structure of multiple self-attention layers and feed-forward network layers superimposed on each other in the Transformer. In addition, to fill the gap in the Chinese CTI nested entity dataset, we further apply the Large Language Modeling (LLM) technique and domain knowledge to construct a high-quality Chinese CTI nested entity dataset, CDTinee, which consists of six entity types selected from STIX, including nearly 4000 entity types extracted from more than 3000 threatening sentences. In the experimental session, we conduct extensive experiments on multiple datasets, and the results show that the proposed model RBTG outperforms the baseline model in both flat NER and nested NER.

Text mining is increasingly used to manage the accelerating pace of the biomedical literature. Many text mining applications depend on accurate named entity recognition (NER) and normalization (grounding). While high performing machine learning methods trainable for many entity types exist for NER, normalization methods are usually specialized to a single entity type. NER and normalization systems are also typically used in a serial pipeline, causing cascading errors and limiting the ability of the NER system to directly exploit the lexical information provided by the normalization. We propose the first machine learning model for joint NER and normalization during both training and prediction. The model is trainable for arbitrary entity types and consists of a semi-Markov structured linear classifier, with a rich feature approach for NER and supervised semantic indexing for normalization. We also introduce TaggerOne, a Java implementation of our model as a general toolkit for joint NER and normalization. TaggerOne is not specific to any entity type, requiring only annotated training data and a corresponding lexicon, and has been optimized for high throughput. We validated TaggerOne with multiple gold-standard corpora containing both mention- and concept-level annotations. Benchmarking results show that TaggerOne achieves high performance on diseases (NCBI Disease corpus, NER f-score: 0.829, normalization f-score: 0.807) and chemicals (BioCreative 5 CDR corpus, NER f-score: 0.914, normalization f-score 0.895). These results compare favorably to the previous state of the art, notwithstanding the greater flexibility of the model. We conclude that jointly modeling NER and normalization greatly improves performance. The TaggerOne source code and an online demonstration are available at: http://www.ncbi.nlm.nih.gov/bionlp/taggerone zhiyong.lu@nih.gov Supplementary data are available at Bioinformatics online.

Infrared thermal imaging (ITI) is the potential imaging technique for the health care field of traditional Chinese medicine (TCM). Successful application demands obeying the characteristics and regularity of the ITI of human body and designing rigorous trials. First, the influence of time must be taken into account as the ITI of human body varies with time markedly. Second, relative magnitude is preferred to be the index of the image features. Third, scatter diagrams and the method of least square could present important information for evaluating the health care effect. A double-blind placebo-controlled randomized trial was undertaken to study the influences of Shengsheng capsule, one of the TCM health food with immunity adjustment function, on the ITI of human body. The results showed that the effect of Shengsheng capsule to people with weak constitution or in the period of being weak could be reflected objectively by ITI. The relative efficacy rate was 81.3% for the trial group and 30.0% for the control group, there was significant difference between the two groups (P=0.003). So the sensitivity and objectivity of ITI are of great importance to the health care field of TCM.

This project aims to construct a knowledge graph system applied to the field of traditional Chinese medicine (TCM) by extracting entities (such as drugs, diseases, etc.) and their relationships from TCM medical case data and storing them in a Neo4j database. The project process includes data reading, entity recognition and extraction, data formatting, and data import into the database. The project not only improved the individual's proficiency in Python data processing techniques (including regular expressions and JSON parsing) but also enhanced their skills in knowledge graph construction and database operations. In the future, there is a desire to further improve technical capabilities, explore more cutting-edge technologies in the TCM field, and promote project progress through collaboration, contributing to the modernization of TCM and intelligent healthcare services.

Named Entity Recognition (NER) is a supervised machine learning task that finds various applications in automated content analysis, as the identification of entities is vital for understanding public discourse. However, sometimes the standard NER labels are not specific enough for a given domain. We introduce Public Entity Recognition (PER). PER is a domain-specific version of NER, that is trained for five entity types that are common to public discourse: politicians, parties, authorities, media, and journalists. PER can be used for pre-processing documents, in a pipeline with other classifiers or directly for analyzing information in texts. The taxonomy for PER is taken from the database of (German) public speakers and aims at low-threshold integration into computational social science research. We experiment with different training settings, involving weakly supervised training and training on manually annotated data. We evaluate multilingual transformer models of different sizes against rule-based entity matching and find that the models do not only outperform the baseline but also reach competitive absolute scores of around .8 and higher in F1. We further test for generalization and domain adaptation. We show that with only around 100–150 additional sentences, the model can be adapted to new languages.

Recent advances in deep neural networks (DNNs) have enabled us to achieve reliable named entity recognition (NER) models without handcrafting features. However, these are also some obstacles imposed by using those machine learning methods, in need of a large amount of manually labeled data. To avoid such limitations, we could replace human annotation with distant supervision, however there remain a technical challenge on the error label issue caused by ignoring the entities that are not included in the vocabulary, which should be addressed to achieve the effective NER model. Then, we propose a novel back-labeling approach and integrate it into a tagging scheme, especially, we apply this scheme to handle the NER task in traditional Chinese medicine (TCM) field. In addition, we discuss how to use distant supervision methods to achieve better performance of the NER model. We conduct some experiments and verify that our scheme can effectively improve the entity recognition on the basis of distant supervision.

During the operation and maintenance of power equipment, a large amount of text data is accumulated, and it is of great importance to mine valuable information and evaluate the operation status of the equipment. Among them, named entity recognition technology is a key prerequisite for downstream tasks. However, with the development of natural language processing technology, while improving the accuracy of entity recognition, the existing models are gradually unable to meet the requirements of time and equipment cost for model training in practice. In this paper, we propose a low-cost ALBERT-BiLSTM-CRF-based named entity recognition model applicable to power equipment defective text. The model achieves an F1 score of 92.47% in entity recognition in the power domain, outperforming the benchmark BERT model performance in terms of time cost and effect.

This paper describes our approach for the Chinese medical Named Entity Recognition (NER) and event extraction tasks organized by the China Conference on Knowledge Graph and Semantic Computing (CCKS) 2021. For the NER task, we need to identify the entity boundaries and category labels of six types of entities from Chinese electronic medical records (EMR). And for the Event Extraction task, we need to recognizes a type of tumor event from Chinese EMR, which contains three tumor-related attributes. This medical entity and event extraction task has two main challenges: 1) How to build a unified modeling framework for entity and event extraction. 2) How to improve the generalization ability of medical entity extraction. For these two challenges, we use a sequence labeling framework based on entity extraction to unify the above two tasks. Based on the pre-trained model, we propose a combined strategy of unsupervised text mode enhancement and label mode enhancement. In the end, it ranked second without any post-processing.KeywordsNamed Entity RecognitionEvent extractionElectronic medical records

Traditional Chinese medicine (TCM) has protected the health of Chinese people for thousands of years. With the rapid development of artificial intelligence (AI), various fields of TCM are facing both opportunities and challenges. This review discusses the development prospects and challenges of Chinese medicine in the AI era, emphasizing that AI, as an important tool in the process of Chinese medicine healthcare services, can assist doctors in making objective, rational and professional treatment decisions, and that AI has a strong potential for development in the field of Chinese medicine. However, the emotions, complex thoughts, and humanistic values of doctors are qualities that AI is currently unable to realize, so as the dominant player, the doctor is indispensable to the medical process. By summarizing and analyzing the current development status of AI in diagnosis, drug research, health management and education in TCM, this paper reveals the development prospects and potential risks of combining TCM with AI, and suggests that AI is an important aid for modernizing and improving the quality of TCM medical care in a coordinated manner.

Medical named entity recognition can assist doctors to quickly identifying key content and improving clinical work efficiency. Chinese named entity recognition methods based on pre-trained language models have achieved remarkable performance. However, most of these models have the following problems for medical named entity recognition: these models are designed for flat named entity recognition tasks but not for nested entities. Furthermore, the medical entities are hard to be recognized due to the lack of medical domain knowledge. To tackle these problems, we propose a Chinese medical nested named entity recognition model based on feature fusion and a bidirectional lattice embedding graph. The problem of poor recognition of medical entities due to the lack of medical domain knowledge is solved by introducing a medical lexicon. The problem of Chinese polyphonic characters with different meanings in the same form is solved by introducing pinyin information. The model considers the similarity between different entity types to improve the model’s effectiveness. The results on a Chinese medical nested named entity dataset CBLUE-CMeEE demonstrate the outperform performance and effectiveness of the model.

In the last decades, a huge number of documents has been digitised, before undergoing optical character recognition (OCR) to extract their textual content. This step is crucial for indexing the documents and to make the resulting collections accessible. However, the fact that documents are indexed through their OCRed content is posing a number of problems, due to the varying performance of OCR methods over time. Indeed, OCR quality has a considerable impact on the indexing and therefore the accessibility of digital documents. Named entities are among the most adequate information to index documents, in particular in the case of digital libraries, for which log analysis studies have shown that around 80% of user queries include a named entity. Taking full advantage of the computational power of modern natural language processing (NLP) systems, named entity recognition (NER) can be operated over enormous OCR corpora efficiently. Despite progress in OCR, resulting text files still have misrecognised words (or noise for short) which are harming NER performance. In this paper, to handle this challenge, we apply a spelling correction method to noisy versions of a corpus with variable OCR error rates in order to quantitatively estimate the contribution of post-OCR correction to NER. Our main finding is that we can indeed consistently improve the performance of NER when the OCR quality is reasonable (error rates respectively between 2% and 10% for characters (CER) and between 10% and 25% for words (WER)). The noise correction algorithm we propose is both language-independent and with low complexity.

Named entity recognition (NER) is a basic technology of Natural Language Processing (NLP). It is mainly used to identify entities and entity types. Compared with traditional entity recognition, fine-grained entity recognition can provide more precise semantics. In order to improve the effect of fine-grained Chinese N ER, w e propose a model based on RoBERTa-WWM-BiLSTM-CRF and compare it with other high-quality models. The experimental results show that this model has better effect on the CLUENER2020 dataset of fine-grained Chinese NER.

Deep language models, like ELMo, BERT and GPT, have achieved impressive results on several natural language tasks. These models are pretrained on large corpora of unlabeled general domain text and later supervisedly trained on downstream tasks. An optional step consists of finetuning the language model on a large intradomain corpus of unlabeled text, before training it on the final task. This aspect is not well explored in the current literature. In this work, we investigate the impact of this step on named entity recognition (NER) for Portuguese legal documents. We explore different scenarios considering two deep language architectures (ELMo and BERT), four unlabeled corpora and three legal NER tasks for the Portuguese language. Experimental findings show a significant improvement on performance due to language model finetuning on intradomain text. We also evaluate the finetuned models on two general-domain NER tasks, in order to understand whether the aforementioned improvements were really due to domain similarity or simply due to more training data. The achieved results also indicate that finetuning on a legal domain corpus hurts performance on the general-domain NER tasks. Additionally, our BERT model, finetuned on a legal corpus, significantly improves on the state-of-the-art performance on the LeNER-Br corpus, a Portuguese language NER corpus for the legal domain.

The clinical comprehensive evaluation of Chinese patent medicine is an important direction in the evaluation of traditional Chinese medicine(TCM), which positively promotes the development of TCM industry. The evaluation system of Chinese patent medicine is helpful to comprehensively evaluate the clinical value of different Chinese patent medicine in the same category, different dosage forms, and specifications, from different manufacturers on the basis of evidence and value. The establishment of a scientific and reasonable comprehensive evaluation index system for Chinese patent medicine is an important prerequisite to ensure clinical value. However, there has been neither a recognized systematic review on the clinical comprehensive evaluation of Chinese patent medicine nor a methodological system used for reference. The evidence and value: impact on decision-making(EVIDEM), developed by the international research team, is used to evaluate the comprehensive value of medical interventions. EVIDEM provides a methodological tool for scientific decision-making to evaluate evidence and value for health technologies on the basis of the multi-criteria decision analysis(MCDA) model and health technology assessment(HTA). Based on the ongoing EVIDEM research, the present study put forward that EVIDEM-based clinical comprehensive evaluation of Chinese patent medicine consisted of four aspects, seven modules, and ten steps, which is expected to references and practical experience for the follow-up comprehensive evaluation of Chinese patent medicine in the TCM field.