Chinese Mathematical Knowledge Entity Recognition Based on Linguistically Motivated Bidirectional Encoder Representation from Transformers

The rapid advancement of artificial intelligence and intelligent algorithms has led to the widespread adoption of natural language processing technology. Named entity recognition (NER), an important task in this field, takes a pivotal part in information extraction, machine translation, and knowledge graph construction. However, for a small language such as Lao, traditional methods have obvious deficiencies in recognition effectiveness due to its complex syllable structure, fuzzy word boundaries and scarce corpus resources. To address this problem, the study designs a named entity recognition model for Lao that incorporates a Bi-directional Long Short-Term Memory (BiLSTM) network, an attention mechanism and a conditional random field. The model first vectorizes multi-granularity features such as characters, syllables and lexemes through an embedding layer, then captures contextual dependencies by a BiLSTM network, introduces an Attention to highlight key information, and finally establishes a global label transfer relation through a conditional random field to ensure the consistency of the predicted sequence. The findings denote that the model achieves 91.5%, 91.0%, 90.7%, and 90.9% in accuracy rate, precision rate, recall rate, and F1 score, respectively, and the mean absolute error and mean square error are reduced to 0.062 and 0.011, which are significantly better than the comparison model. The results show that combining multi-level feature modeling and global constraint mechanism can effectively enhance the performance of named entity recognition in Lao, which provides a valuable reference and practical path for natural language processing in small languages.

BackgroundThe unstructured data of Chinese cancer electronic health records (EHRs) contains valuable medical expertise. Accurate medical entity recognition is crucial for building a medical-assisted decision system. Named entity recognition (NER) in cancer EHRs typically uses general models designed for English medical records. There is a lack of specialized handling for cancer-specific records and limited application to Chinese medical records.ObjectiveThis study aims to propose a specific NER model to enhance the recognition of medical entities in Chinese cancer EHRs.MethodsDesensitized inpatient EHRs related to breast cancer were collected from a leading hospital in Beijing. Building upon the MC Bidirectional Encoder Representations from Transformers (BERT) foundation, the study further incorporated a Chinese cancer corpus for pretraining, resulting in the construction of the ChCancerBERT pretrained model. In conjunction with dilated-gated convolutional neural networks, bidirectional long short-term memory, multihead attention mechanism, and a conditional random field, this model forms a multimodel, multilevel integrated NER approach.ResultsThis approach effectively extracts medical entity features related to symptoms, signs, tests, treatments, and time in Chinese breast cancer EHRs. The entity recognition performance of the proposed model surpasses that of the baseline model and other models compared in the experiment. The F1-score reached 86.93%, precision reached 87.24%, and recall reached 86.61%. The model introduced in this study demonstrates exceptional performance on the CCKS2019 dataset, attaining a precision rate of 87.26%, a recall rate of 87.27%, and an impressive F1-score of 87.26%, surpassing that of existing models.ConclusionsThe experiments demonstrate that the approach proposed in this study exhibits excellent performance in NER within breast cancer EHRs. This advancement will further contribute to clinical decision support for cancer treatment and research. In addition, the study reveals that incorporating domain-specific corpora in clinical NER tasks can further enhance the performance of BERT models in specialized domains.

The accuracy of entity recognition is particularly important for knowledge graph construction. The traditional Named Entity recognition (NER) model mainly includes HMM, CRF, BiLSTM, BiLSTM-CRF, etc. It is difficult to solve the problem of word meaning confusion resulting from the wrong separation at the end of the entity when using the four models for the labeled nuclear technology knowledge data sets. In order to improve the entity recognition effect and address the problem of polysemy in nuclear technology knowledge data set, an improved nuclear technology entity recognition method based on the BERT-BiLSTM-CRF combination model was proposed by comparative experiments. According to the results, it can conclude that the application of the BERT model instead of the Word2Vec algorithm for word vector training is helpful to the model recognition, and the exclusive dictionary word segmentation and part of speech classification of nuclear technology texts contribute to improving the quality of labeled data. The experiment verified that the usage of BERT pre-training model can solve the problem of polysemy in NER to some extent. Meanwhile, it validated the precision in nuclear technology knowledge entity recognition for collected data, which could be used in pre-procedure in nuclear technology knowledge graph construction.

<p>The traditional Chinese Named Entity Recognition (NER) method is difficult to define the entity category of the word according to the specific language environment, and the category is ambiguous, so it is difficult to accurately identify the entity. Named entity recognition based on deep learning can find entity categories in text, so it has received widespread attention. On the basis of the neural network model, this paper proposes a model based on TextCNN-BiLSTM-CRF and text classification (TextCNN-BiLSTM-TC-CRF) for Chinese NER. First, the TextCNN model is used to extract the word vector information of the text data; secondly, bidirectional LSTM is used the model extracts the contextual features of the text; then the neural network model is used to automatically extract the word features and the global features of the text for text classification; finally, the text sequence labeling and entity recognition are performed. Experiments verify that on a large-scale Chinese NER data set, the entity recognition model proposed in this paper has better evaluation indicators than other algorithms, with an F1 score of 98.7%.</p> <p> </p>

Traffic accident case named entity recognition, which helps mine key information in traffic accident texts, plays a vital role in downstream tasks such as the construction of knowledge graphs in road traffic and intelligent policing. In this paper, we construct a named entity recognition model based on the EDE (Entity Data Enhancement)-ERNIE-Bidirectional Gated Recurrent Unit Network (BiGRU)-Conditional Random Field (CRF) to address the current situation of low traffic accident case data and poor recognition of long-text entities. First, the amount of accident case data is enhanced using the entity random substitution method. Next, the text data of traffic accident cases are characterized as a dynamic word vector using the ERNIE pretraining model. Then, the BiGRU network learns the long-distance dependency relationship in the text to enhance the effect of the model on long-text entity recognition. Finally, the result sequence is constrained by the CRF layer to realize the named entity recognition model. The experimental part uses data related to real traffic accident cases in a domestic area. The data enhancement method increases the data volume three times compared to the original data volume. Experimental results show that the EDE-ERNIE-BiGRU-CRF model achieves better F1 values, recall and precision achieved better performance than the entity recognition methods of BERT-BiGRU-CRF, ERNIE-BiGRU-CRF, ERNIE-BiLSTM-CRF, ERNIE-CRF, ERNIE, BiGRU-CRF, ROBERTA-wwm-ext-BiGRU-CRF and verify its effectiveness for entity recognition in traffic accident cases.

With Entity recognition is an important basic tool for many natural language processing tasks such as information extraction, question answering systems, and syntactic analysis. Entity recognition is divided into general field and specific field. There were different recognition methods for different fields. At present, one of the more common entity recognition technologies is a rule-based method, and the other is a statistical method. Due to data sets scarcity in the aerospace engine field, there is a lack of corresponding entity recognition research in the aerospace engine field. This paper proposes a statistical-based entity recognition method to identify entities in a specific field based on conditional random fields. This method constructs a training set in the aerospace engine field through manual annotation, extracts entities in the aerospace engine field, uses words, word frequency, and part-of-speech features respectively, and conducts experiments. This method can more accurately identify entities in the aerospace engine field, and finally make the accuracy rate, Recall rate and F value reach 92.7%, 87.1%, 89.8%.

PurposeSafety management plays an important part in coal mine construction. Due to complex data, the implementation of the construction safety knowledge scattered in standards poses a challenge. This paper aims to develop a knowledge extraction model to automatically and efficiently extract domain knowledge from unstructured texts.Design/methodology/approachBidirectional encoder representations from transformers (BERT)-bidirectional long short-term memory (BiLSTM)-conditional random field (CRF) method based on a pre-training language model was applied to carry out knowledge entity recognition in the field of coal mine construction safety in this paper. Firstly, 80 safety standards for coal mine construction were collected, sorted out and marked as a descriptive corpus. Then, the BERT pre-training language model was used to obtain dynamic word vectors. Finally, the BiLSTM-CRF model concluded the entity’s optimal tag sequence.FindingsAccordingly, 11,933 entities and 2,051 relationships in the standard specifications texts of this paper were identified and a language model suitable for coal mine construction safety management was proposed. The experiments showed that F1 values were all above 60% in nine types of entities such as security management. F1 value of this model was more than 60% for entity extraction. The model identified and extracted entities more accurately than conventional methods.Originality/valueThis work completed the domain knowledge query and built a Q&A platform via entities and relationships identified by the standard specifications suitable for coal mines. This paper proposed a systematic framework for texts in coal mine construction safety to improve efficiency and accuracy of domain-specific entity extraction. In addition, the pretraining language model was also introduced into the coal mine construction safety to realize dynamic entity recognition, which provides technical support and theoretical reference for the optimization of safety management platforms.

BackgroundIn biomedical text mining, named entity recognition (NER) is an important task used to extract information from biomedical articles. Previously proposed methods for NER are dictionary- or rule-based methods and machine learning approaches. However, these traditional approaches are heavily reliant on large-scale dictionaries, target-specific rules, or well-constructed corpora. These methods to NER have been superseded by the deep learning-based approach that is independent of hand-crafted features. However, although such methods of NER employ additional conditional random fields (CRF) to capture important correlations between neighboring labels, they often do not incorporate all the contextual information from text into the deep learning layers.ResultsWe propose herein an NER system for biomedical entities by incorporating n-grams with bi-directional long short-term memory (BiLSTM) and CRF; this system is referred to as a contextual long short-term memory networks with CRF (CLSTM). We assess the CLSTM model on three corpora: the disease corpus of the National Center for Biotechnology Information (NCBI), the BioCreative II Gene Mention corpus (GM), and the BioCreative V Chemical Disease Relation corpus (CDR). Our framework was compared with several deep learning approaches, such as BiLSTM, BiLSTM with CRF, GRAM-CNN, and BERT. On the NCBI corpus, our model recorded an F-score of 85.68% for the NER of diseases, showing an improvement of 1.50% over previous methods. Moreover, although BERT used transfer learning by incorporating more than 2.5 billion words, our system showed similar performance with BERT with an F-scores of 81.44% for gene NER on the GM corpus and a outperformed F-score of 86.44% for the NER of chemicals and diseases on the CDR corpus. We conclude that our method significantly improves performance on biomedical NER tasks.ConclusionThe proposed approach is robust in recognizing biological entities in text.

Weaponry equipment names belong to an important military naming entity that is difficult to identify because of features, such as complex components, miscellaneous, and scarce annotation corpus. Here, the automatic recognition of weaponry equipment names is specifically explored, a NER (Named Entity Recognition) algorithm is proposed based on BI-LSTM-CRF (Bi-directional Long Short Term Memory Conditional Random Field), thereby demonstrating the effectiveness of domain features in domain-specific entity recognition. Firstly, Chinese characters are represented by word embedding and input into the model. Then, the input feature vector sequence is processed by BI-LSTM (Bi-directional Long Short Term Memory) NN (Neural Network) to extract context semantic learning features. Finally, the learned features are connected to the linear CRF (Conditional Random Field), the NEs (Named Entities) in the equipment support field are labeled, and the NER results are obtained and output. The experimental results show that the accuracy of the NER algorithm based on the BI-LSTM-CRF model is 92.02%, the recall rate is 93.21%, and the F1 value reaches 93.88%. The effect of this model is better than the BI-LSTM NN model and LSTM-CRF (Long Short Term Memory Conditional Random Field) NN model. The proposed model provides some references for entity recognition in the field of equipment support.

Named Entity Recognition (NER) is a vital task in natural language processing (NLP) that involves identifying and classifying named entities in text. This paper focuses on the comparison of four different algorithms of NER techniques in the context of India. The four different algorithms are CRF (Conditional Random Fields), Stanza, Spacy, and Nameparser. The accuracy will be compared with the expected accuracy of all four methods of NER. Accuracy will be obtained after running the method on a dataset or text. To calculate the accuracy of four algorithms a large dataset of Indian names will be used. A comparison graph was obtained to display the accuracy graph of all four algorithms. "NLP-Named Entity Recognition in India" which focuses on detecting person names, states, and districts, and suggesting names based on religion. Named Entity Recognition (NER) is a Natural Language Processing (NLP) task that identifies and classifies named entities in text. Named entities are typically proper nouns, such as person names, organization names, and location names. NER is a challenging task for Indian languages, due to the diversity of Indian languages and the lack of resources for training NER systems. The proposed system can be used to improve the accuracy and efficiency of a variety of NLP applications in India.

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.

Traditional Chinese Medicine (TCM) patents contain abundant medical, economic and legal information. The effective analysis and mining of TCM patents is of great significance to support medicine R&D and patent acquisition decision-making. Named entity recognition is a key step in the research of TCM patents annotation. In order to solve the problem that the entity recognition in TCM patents relies on manual work in a large extent and the degree of automation is not high, in this paper, under the framework of ERNIE (Enhanced Language Representation with Informative Entities) pre-training language model, combined with the Bi-directional Gating Recurrent Unit (BiGRU) and Conditional Random Field (CRF), two important entities, herbal names and medicine effects, are recognized in TCM patent texts. Experimental results show that the performance of the method based on ERNIE-BiGRU-CRF is significantly improved compared with other models.

Efficient recognition of proprietary entities is an important basic work for text data mining and intelligent application in power domain. Traditional power domain Named Entity Recognition (NER) methods rely on feature engineering seriously, which unable to learn power entity features automatically. In order to learn entity features automatically and extract power domain named entities efficiently, a model based on Bidirectional Long Short-Term Memory Neural Networks (Bi-LSTM) and Conditional Random Field (CRF) was proposed in this paper. Word representations were fed into the neural networks as an additional feature and Skip-gram embeddings were trained on power domain corpus. Experimental results showed the precision rate reaches higher than 88.25% and the recalling rate reaches higher than 88.04%, which confirm the method based on Bi-LSTM and CRF is effective for named entity recognition in the power domain.

Evaluation of clinical named entity recognition methods for Serbian electronic health records

ObjectivePituitary adenomas are the most common type of pituitary disorders, which usually occur in young adults and often affect the patient’s physical development, labor capacity and fertility. Clinical free texts noted in electronic medical records (EMRs) of pituitary adenomas patients contain abundant diagnosis and treatment information. However, this information has not been well utilized because of the challenge to extract information from unstructured clinical texts. This study aims to enable machines to intelligently process clinical information, and automatically extract clinical named entity for pituitary adenomas from Chinese EMRs.MethodsThe clinical corpus used in this study was from one pituitary adenomas neurosurgery treatment center of a 3A hospital in China. Four types of fine-grained texts of clinical records were selected, which included notes from present illness, past medical history, case characteristics and family history of 500 pituitary adenoma inpatients. The dictionary-based matching, conditional random fields (CRF), bidirectional long short-term memory with CRF (BiLSTM-CRF), and bidirectional encoder representations from transformers with BiLSTM-CRF (BERT-BiLSTM-CRF) were used to extract clinical entities from a Chinese EMRs corpus. A comprehensive dictionary was constructed based on open source vocabularies and a domain dictionary for pituitary adenomas to conduct the dictionary-based matching method. We selected features such as part of speech, radical, document type, and the position of characters to train the CRF-based model. Random character embeddings and the character embeddings pretrained by BERT were used respectively as the input features for the BiLSTM-CRF model and the BERT-BiLSTM-CRF model. Both strict metric and relaxed metric were used to evaluate the performance of these methods.ResultsExperimental results demonstrated that the deep learning and other machine learning methods were able to automatically extract clinical named entities, including symptoms, body regions, diseases, family histories, surgeries, medications, and disease courses of pituitary adenomas from Chinese EMRs. With regard to overall performance, BERT-BiLSTM-CRF has the highest strict F1 value of 91.27% and the highest relaxed F1 value of 95.57% respectively. Additional evaluations showed that BERT-BiLSTM-CRF performed best in almost all entity recognition except surgery and disease course. BiLSTM-CRF performed best in disease course entity recognition, and performed as well as the CRF model for part of speech, radical and document type features, with both strict and relaxed F1 value reaching 96.48%. The CRF model with part of speech, radical and document type features performed best in surgery entity recognition with relaxed F1 value of 95.29%.ConclusionsIn this study, we conducted four entity recognition methods for pituitary adenomas based on Chinese EMRs. It demonstrates that the deep learning methods can effectively extract various types of clinical entities with satisfying performance. This study contributed to the clinical named entity extraction from Chinese neurosurgical EMRs. The findings could also assist in information extraction in other Chinese medical texts.