Multilingual Named Entity Recognition Model for Indonesian Health Insurance Question Answering System

2020 ◽  
Author(s):  
Budi Sulistiyo Jati ◽  
ST Widyawan ◽  
S.T. Muhammad Nur Rizal
Keyword(s):  
Health Insurance ◽  
Question Answering ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Question Answering System ◽  
Recognition Model ◽  
Named Entity

scholarly journals An Algorithm of Vocabulary Enhanced Intelligent Question Answering Based on FLAT1

2021 ◽  
Author(s):  
Jing Sheng Lei ◽  
Shi Chao Ye ◽  
Sheng Ying Yang ◽  
Wei Song ◽  
Guan Mian Liang
Keyword(s):  
Question Answering ◽  
Named Entity Recognition ◽  
Recognition Task ◽  
Recognition Algorithm ◽  
Entity Recognition ◽  
Knowledge Graph ◽  
Question Answering System ◽  
Named Entity ◽  
Natural Language Question ◽  
Ultimate Failure

The main purpose of the intelligent question answering system based on the knowledge graph is to accurately match the natural language question and the triple information in the knowledge graph. Among them, the entity recognition part is one of the key points. The wrong entity recognition result will cause the error to be done propagated, resulting in the ultimate failure to get the correct answer. In recent years, the lexical enhancement structure of word nodes combined with word nodes has been proved to be an effective method for Chinese named entity recognition. In order to solve the above problems, this paper proposes a vocabulary-enhanced entity recognition algorithm (KGFLAT) based on FLAT for intelligent question answering system. This method uses a new dictionary that combines the entity information of the knowledge graph, and only uses layer normalization for the removal of residual connection for the shallower network model. The system uses data provided by the NLPCC 2018 Task7 KBQA task for evaluation. The experimental results show that this method can effectively solve the entity recognition task in the intelligent question answering system and achieve the improvement of the FLAT model, and the average F1 value is 94.72

scholarly journals Eve: An Automated Question Answering System for Events Information

2017 ◽  
Vol 8 (1) ◽  
pp. 15
Author(s):  
Ivan Christanno ◽  
Priscilla Priscilla ◽  
Jody Johansyah Maulana ◽  
Derwin Suhartono ◽  
Rini Wongso
Keyword(s):  
Question Answering ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Closed Domain ◽  
Word Similarity ◽  
Question Answering System ◽  
Named Entity ◽  
Document Indexing ◽  
Matching Technique ◽  
Processing Information

The objective of this research was to create a closed-domain of automated question answering system specifically for events called Eve. Automated Question Answering System (QAS) is a system that accepts question input in the form of natural language. The question will be processed through modules to finally return the most appropriate answer to the corresponding question instead of returning a full document as an output. Thescope of the events was those which were organized by Students Association of Computer Science (HIMTI) in Bina Nusantara University. It consisted of 3 main modules namely query processing, information retrieval, and information extraction. Meanwhile, the approaches used in this system included question classification, document indexing, named entity recognition and others. For the results, the system can answer 63 questions for word matching technique, and 32 questions for word similarity technique out of 94 questions correctly.

scholarly journals Clinical Named Entity Recognition from Chinese Electronic Medical Records Based on Deep Learning Pretraining

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Lejun Gong ◽  
Zhifei Zhang ◽  
Shiqi Chen
Keyword(s):  
Deep Learning ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Named Entity Recognition ◽  
Clinical Entity ◽  
Fine Tuning ◽  
Entity Recognition ◽  
Recognition Model ◽  
Named Entity ◽  
Model Based

Background. Clinical named entity recognition is the basic task of mining electronic medical records text, which are with some challenges containing the language features of Chinese electronic medical records text with many compound entities, serious missing sentence components, and unclear entity boundary. Moreover, the corpus of Chinese electronic medical records is difficult to obtain. Methods. Aiming at these characteristics of Chinese electronic medical records, this study proposed a Chinese clinical entity recognition model based on deep learning pretraining. The model used word embedding from domain corpus and fine-tuning of entity recognition model pretrained by relevant corpus. Then BiLSTM and Transformer are, respectively, used as feature extractors to identify four types of clinical entities including diseases, symptoms, drugs, and operations from the text of Chinese electronic medical records. Results. 75.06% Macro-P, 76.40% Macro-R, and 75.72% Macro-F1 aiming at test dataset could be achieved. These experiments show that the Chinese clinical entity recognition model based on deep learning pretraining can effectively improve the recognition effect. Conclusions. These experiments show that the proposed Chinese clinical entity recognition model based on deep learning pretraining can effectively improve the recognition performance.

Named Entity Recognition Model of Chinese Clinical Electronic Medical Record Based on XLNet-BiLSTM

2021 ◽  
Author(s):  
Shen Zhou Feng ◽  
Su Qian Min ◽  
Guo Jing Lei
Keyword(s):  
Medical Record ◽  
Electronic Medical Record ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Vector Model ◽  
Global Maximum ◽  
Data Set ◽  
Recognition Model ◽  
Named Entity ◽  
Feature Sequence

Abstract The recognition of named entities in Chinese clinical electronic medical records is one of the basic tasks to realize smart medical care. Aiming at the insufficient text semantic representation of the traditional word vector model and the inability of the recurrent neural network (RNN) model to solve the problems of long-term dependence, a Chinese clinical electronic medical record named entity recognition model XLNet-BiLSTM-MHA-CRF based on XLNet is proposed. Use the XLNet pre-training language model as the embedding layer to vectorize the medical record text to solve the problem of ambiguity; use the bidirectional long and short-term memory network (BiLSTM) gate control unit to obtain the forward and backward semantic feature information of the sentence; Then input the feature sequence to the multi-head attention layer (multi-head attention, MHA), use MHA to obtain information represented by different subspaces of the feature sequence, enhance the relevance of context semantics and eliminate noise; finally, input the conditional random field CRF to identify the global maximum 优 sequence. The experimental results show that the XLNet-BiLSTM-Attention-CRF model has achieved good results on the CCKS-2017 named entity recognition data set.

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