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Chinese Named Entity Recognition Within the Electric Power Domain 1

4th International Conference on Emerging Information Security and Applications (EISA)

作者： Feng, Jun Wang, Hongkai Peng, Liangying Wang, Yidan Song, Haomin Guo, Hongju State Grid Zhejiang Elect Power Corp Informat & T Hangzhou Peoples R China Nanjing Duotuo Intelligent Technol Ltd Liabil Co Nanjing Peoples R China

ISBN: (数字)9789819996148

ISBN: (纸本)9789819996131;9789819996148

The field of electrical power encompasses a vast array of diverse information modalities, with textual data standing as a pivotal constituent of this domain. In this study, we harness an extensive corpus of textual data drawn from the electrical power systems domain, comprising regulations, reports, and other pertinent materials. Leveraging this corpus, we construct an Electrical Power Systems Corpus and proceed to annotate entities within this text, thereby introducing a novel Named Entity Recognition (NER) dataset tailored specifically for the electrical power domain. We employ an end-to-end deep learning model, the BERT-BiLSTM-CRF model, for named entity recognition on our custom electrical power domain dataset. This NER model integrates the BERT pre-trained model into the traditional BiLSTM-CRF model, enhancing its ability to capture contextual and semantic information within the text. Results demonstrate that the proposed model outperforms both the BiLSTM-CRF model and the BERT-softmax model in NER tasks across the electrical power domain and various other domains. This study contributes to the advancement of NER applications in the electrical power domain and holds significance for furthering the construction of knowledge graphs and databases related to electrical power systems.

关键词： Electric power domain Named entity recognition bidirectional encoder representations from transformers bidirectional long short-term memory Conditional random fields

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Cross-language Text Generation Using mBERT and XLM-R: English-Chinese Translation Task 24

Cross-language Text Generation Using mBERT and XLM-R: Englis...

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International Conference on Machine Intelligence and Digital Applications (MIDA)

作者： Ma, Ying Wuhan Business Univ Wuhan 430056 Hubei Peoples R China

ISBN: (纸本)9798400718144

Traditional methods often require a large number of parallel corpora, that is, texts from one language and corresponding texts from another language. The acquisition and construction of this data is very expensive and time-consuming, which limits the application scope and feasibility of the model. This article used mBERT and XLM-R to solve the problem of relying on parallel corpora in traditional methods, and improved the quality of English-Chinese translation through the model's own multilingual representation ability. A publicly available parallel corpus of English-Chinese was collected for translation tasks, and the collected corpus was preprocessed with segmentation, punctuation, and other techniques. mBERT and XLM-R were used for English-Chinese translation respectively, and Ensemble method was adopted to weight and fuse the results of mBERT and XLM-R. BLEU (Bilingual Evaluation Understudy), METEOR (Metric for Evaluation of Translation with Explicit Ordering), and ROUGE (Recall-Oriented Understudy for Gisting Evaluation) were used to evaluate the quality of the translations. The average BLEU, average METEOR, and average ROUGE-L values of mBERT-XLM-R were 0.98, 0.97, and 0.98, respectively, according to the results. Therefore, mBERT and XLM-R working together may significantly enhance the quality of English-Chinese translation.

关键词： Cross-language Text Generation English-Chinese Translation Parallel Corpus bidirectional encoder representations from transformers

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Integrated AI-Driven Healthcare System: Predictive Diagnosis, Personalized Medical-Report Content Generation, and Physician Recommendation 2

Integrated AI-Driven Healthcare System: Predictive Diagnosis...

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2nd International Conference on Sustainable Computing and Smart Systems (ICSCSS)

作者： Adru, Sri Likhita Srijayanthi, S. RMK Engn Coll Artificial Intelligence & Data Sci Kavaraipettai India

ISBN: (纸本)9798350391558;9798350379990

Accurate medical diagnosis remains elusive due to limitations in symptom analysis and personalized reports. Current systems often rely on keyword matching, leading to misdiagnosis and failing to adapt to nuanced medical narratives. This paper proposes a novel web application that addresses these challenges using state-of-the-art natural language processing techniques. Users input symptoms, triggers, and illness duration using a Likert scale. The proposed system employs a two-step process: initial diagnosis with a BM25 model followed by refinement using a pre-trained BERT model for a more contextual understanding. A comprehensive medical report is then generated, outlining the likely condition, proposing a treatment plan, and categorizing the report based on extracted keywords. Finally, K-means clustering suggests relevant specialist doctors based on the report category. This innovative approach enhances healthcare accessibility by providing tailored and swift recommendations, ultimately empowering users with informed decisions and fostering efficient doctor-patient connections.

关键词： BM25 Likert Scale bidirectional encoder representations from transformers K-Means

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Enhancing Chinese Named Entity Recognition with Disentangled Expert Knowledge 1

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4th International Conference on Emerging Information Security and Applications (EISA)

作者： Wang, Hongkai Feng, Jun Wang, Yidan Pan, Sichen Zhao, Shuai Xue, Yi State Grid Zhejiang Elect Power Corp Informat & T Hangzhou Peoples R China Nanjing Duotuo Intelligent Technol Ltd Liabil Co Nanjing Peoples R China

ISBN: (数字)9789819996148

ISBN: (纸本)9789819996131;9789819996148

Chinese Named Entity Recognition (NER) requires model identify entity boundaries in the sentence i.e., entity segmentation, and meanwhile assign entities to pre-defined categories, i.e., entity classification. Current NER tasks follows sequence tagging scheme and assign the characters to different labels by considering both segmentation position and entity categories. In such a scheme, the characters in the same entity will be regarded as different classes in the training process according to different positions. In fact, the knowledge of entity segmentation is shared across different entity categories, while entity category knowledge is relatively independent of entity segmentation. Such labeling scheme will lead to the entanglement of these two objectives, hindering the effective knowledge acquisition by the models. To address the entanglement issue and comprehensively extract useful knowledge of two objectives, we propose a novel framework that disentangle the original NER labels into two additional training labels for entity segmentation and entity classification respectively. Then we introduce two dedicated expert models to effectively extract specific knowledge from the disentangled labels. Afterwards, their predictions will be integrated into the original model as auxiliary knowledge, further enhancing the primary NER model learning process. We conduct experiments on three publicly available datasets to demonstrate the effectiveness of our proposed method.

关键词： Chinese Named Entity Recognition bidirectional encoder representations from transformers Information Extraction

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Enhancing Academic Decision-Making using Semantic Analysis based Course Recommendation System 4

Enhancing Academic Decision-Making using Semantic Analysis b...

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4th International Conference on Pervasive Computing and Social Networking (ICPCSN)

作者： Gopi, Kollipara Monish Sai Kanmani, Sujithra R. Elakiya, E. Surendiran, B. Vellore Inst Technol Sch Comp Sci & Engn Chennai Tamil Nadu India Natl Inst Technol Puducherry Dept Comp Sci & Engn Pondicherry India

ISBN: (纸本)9798350386356;9798350386349

The existing course recommendation systems around the world rely on student's past performances, grades and CGPA when giving personalized suggestions. The goal is to provide a customized course recommendation system using Machine Learning (ML) techniques for students. In this project, Machine Learning techniques are used to analyze student feedback about teachers teaching skills, teacher friendliness and course difficulty from the students who already finished said course, this feedback data will be analyzed using semantic analysis. The software developed will be able to give alternative suggestions to students based on their academic performance. By combining the past students' feedback and making real-time suggestions the program will be able to give a clear perspective about the course and the teachers. The final purpose is to give the students a better academic experience by giving them more choices and better course recommendations that will guide them to follow their college career.

关键词： recommendation content-based filtering collaborative filtering feedback semantic analysis K Nearest Neighbours Support Vector Classifier personalized suggestions ratings Long Short-Term Memory transformers bidirectional encoder representations from transformers Context Aware Recommenders

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CAN-BERT do it? Controller Area Network Intrusion Detection System based on BERT Language Model 19

CAN-BERT do it? Controller Area Network Intrusion Detection ...

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19th IEEE/ACS International Conference on Computer Systems and Applications (AICCSA)

作者： Alkhatib, Natasha Mushtaq, Maria Ghauch, Hadi Danger, Jean-Luc Telecom Paris IP Paris Palaiseau France

ISBN: (纸本)9798350310085

Due to the rising number of sophisticated customer functionalities, electronic control units (ECUs) are increasingly integrated into modern automotive systems. However, the high connectivity between the in-vehicle and the external networks paves the way for hackers who could exploit in-vehicle network protocols' vulnerabilities. Among these protocols, the Controller Area Network (CAN), known as the most widely used in-vehicle networking technology, lacks encryption and authentication mechanisms, making the communications delivered by distributed ECUs insecure. Inspired by the outstanding performance of bidirectional encoder representations from transformers (BERT) for improving many natural language processing tasks, we propose in this paper "CAN-BERT", a deep learning based network intrusion detection system, to detect cyber attacks on CAN bus protocol. We show that the BERT model can learn the sequence of arbitration identifiers (IDs) in the CAN bus for anomaly detection using the "masked language model" unsupervised training objective. The experimental results on the "Car Hacking: Attack & Defense Challenge 2020" dataset show that "CAN-BERT" outperforms state-of-the-art approaches. In addition to being able to identify in-vehicle intrusions in real-time within 0.8 ms to 3 ms w.r.t CAN ID sequence length, it can also detect a wide variety of cyberattacks with an F1-score of between 0.81 and 0.99.

关键词： controller area network CAN Intrusion Detection bidirectional encoder representations from transformers BERT in-vehicle network cyberattacks

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When BERT Meets Bilbo: A Learning Curve Analysis of Pretrained Language Model on Disease Classification 8

When BERT Meets Bilbo: A Learning Curve Analysis of Pretrain...

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8th IEEE International Conference on Healthcare Informatics (ICHI)

作者： Li, Xuedong Yuan, Walter Peng, Dezhong Mei, Qiaozhu Wang, Yue Sichuan Univ Coll Comp Sci Chengdu Peoples R China MobLab Inc Pasadena CA USA Univ Michigan Sch Informat Ann Arbor MI USA Univ North Carolina Chapel Hill Sch Informat & Lib Sci Chapel Hill NC USA

ISBN: (纸本)9781728153827

Natural language processing tasks in the health domain often deal with limited amount of labeled data. Pretrained language models show us a promising way to compensate for the lake of training data, such as bidirectional encoder representations from transformers (BERT). However, previous downstream tasks often used training data at such a large scale that is unlikely to obtain in health domain. In this work, We conducted a learning curve analysis on a disease classification task to study the behavior of BERT and baseline models can still benefit downstream tasks when training data are relatively small in the context of health NLP.(1)

关键词： learning curve bidirectional encoder representations from transformers disease classification

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Improving the Identification fi cation of Diabetic Retinopathy and Related Conditions in the Electronic Health Record Using Natural Language Processing Methods

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OPHTHALMOLOGY SCIENCE 2024年第6期4卷 100578-100578页

作者： Harrigian, Keith Tran, Diep Tang, Tina Gonzales, Anthony Nagy, Paul Kharrazi, Hadi Dredze, Mark Cai, Cindy X. Johns Hopkins Univ Dept Comp Sci Baltimore MD USA Johns Hopkins Sch Med Wilmer Eye Inst Baltimore MD USA Johns Hopkins Univ Johns Hopkins Sch Med Dept Biomed Informat & Data Sci Baltimore MD USA Johns Hopkins Univ Ctr Populat Hlth Informat Technol Bloomberg Sch Publ Hlth Baltimore MD USA

Purpose: To compare the performance of 3 phenotyping methods in identifying diabetic retinopathy (DR) and related clinical conditions. Design: Three phenotyping methods were used to identify clinical conditions including unspecified DR, nonproliferative DR (NPDR) (mild, moderate, severe), consolidated NPDR (unspecified DR or any NPDR), proliferative DR, diabetic macular edema (DME), vitreous hemorrhage, retinal detachment (RD) (tractional RD or combined tractional and rhegmatogenous RD), and neovascular glaucoma (NVG). The first method used only International Classification of Diseases, 10th Revision (ICD-10) diagnosis codes (ICD-10 Lookup System). The next 2 methods used a bidirectional encoder representations from transformers with a dense Multilayer Perceptron output layer natural language processing (NLP) framework. The NLP framework was applied either to free-text of provider notes (Text-Only NLP System) or both free-text and ICD-10 diagnosis codes (Text-and-International Classification of Diseases [ICD] NLP System). Subjects: Adults >= 18 years with diabetes mellitus seen at the Wilmer Eye Institute. Methods: We compared the performance of the 3 phenotyping methods in identifying the DR related conditions with gold standard chart review. We also compared the estimated disease prevalence using each method. Main Outcome Measures: Performance of each method was reported as the macro F1 score. The agreement between the methods was calculated using the kappa statistic. Prevalence estimates were also calculated for each method. Results: A total of 91 097 patients and 692 486 office visits were included in the study. Compared with the gold standard, the Text-and-ICD NLP System had the highest F1 score for most clinical conditions (range 0.39-0.64). The agreement between the ICD-10 Lookup System and Text-Only NLP System varied (kappa of 0.21-0.81). The prevalence of DR and related conditions ranged from 1.1% for NVG to 17.9% for DME (using the Text-and-ICD NLP System)

关键词： Clinical free-text notes Diabetic retinopathy Electronic health record Natural language processing Prevalence BERT bidirectional encoder representations from transformers DME diabetic macular edema DR diabetic retinopathy EHR electronic health record ICD International Classification of Diseases ICD-10 International Classification of Diseases, 10th Revision ME macular edema MLP Multilayer Perceptron NLP natural language processing NPDR nonproliferative diabetic retinopathy NVG neovascular glaucoma PDR proliferative diabetic retinopathy RD retinal detachment TRD tractional retinal detachment VH vitreous hemorrhage

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Intelligent Prescription-Generating Models of Traditional Chinese Medicine Based on Deep Learning

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World Journal of Traditional Chinese Medicine 2021年第3期7卷 361-369页

作者： Qing-Yang Shi Li-Zi Tan Lim Lian Seng Hui-Jun Wang Department of Traditional Chinese Medicine Tianjin University of Traditional Chinese MedicineTianjinChina Department of Traditional Chinese Medicine Malaysia Chinese Medical AssociationKuala LumpurMalaysia

Objective:This study aimed to construct an intelligent prescription-generating(IPG)model based on deep-learning natural language processing(NLP)technology for multiple prescriptions in Chinese *** and Methods:We selected the Treatise on Febrile Diseases and the Synopsis of Golden Chamber as basic datasets with EDA data augmentation,and the Yellow Emperor’s Canon of Internal Medicine,the Classic of the Miraculous Pivot,and the Classic on Medical Problems as supplementary datasets for *** selected the word-embedding model based on the Imperial Collection of Four,the bidirectional encoder representations from transformers(BERT)model based on the Chinese Wikipedia,and the robustly optimized BERT approach(RoBERTa)model based on the Chinese Wikipedia and a general *** addition,the BERT model was fine-tuned using the supplementary datasets to generate a Traditional Chinese Medicine-BERT *** IPG models were constructed based on the pretraining strategy and experiments were *** of precision,recall,and F1-score were used to assess the model *** on the trained models,we extracted and visualized the semantic features of some typical texts from treatise on febrile diseases and investigated the ***:Among all the trained models,the RoBERTa-large model performed the best,with a test set precision of 92.22%,recall of 86.71%,and F1-score of 89.38%and 10-fold cross-validation precision of 94.5%±2.5%,recall of 90.47%±4.1%,and F1-score of 92.38%±2.8%.The semantic feature extraction results based on this model showed that the model was intelligently stratified based on different meanings such that the within-layer’s patterns showed the associations of symptom–symptoms,disease–symptoms,and symptom–punctuations,while the between-layer’s patterns showed a progressive or dynamic symptom and disease ***:Deep-learning-based NLP technology significantly improves the performance of IPG mo

关键词： Ancient books of Chinese medicine bidirectional encoder representations from transformers deep learning intelligent prescription-generating models pretrained models

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Intelligent Recommender Systems in Mobile-assisted Language Learning (MALL): A Study of BERT-based Vocabulary Learning 3

Intelligent Recommender Systems in Mobile-assisted Language ...

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3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Zhao, Yanmei Muhamad, Mohd Mokhtar Mustakim, Siti Salina Li, Wenling Wu, Xuanyi Wang, Aiping School of Foreign Languages Yuxi Normal University Yuxi China Universiti Putra Malaysia Faculty of Educational Studies Selangor Malaysia Universiti Putra Malaysia Faculty of Modern Languages and Communication Selangor Malaysia

ISBN: (纸本)9798350317022

With the popularity and development of mobile technology, mobile-assisted language learning (MALL) has gradually become essential to language learning. Intelligent recommender systems are increasingly important in MALL to improve learning efficiency and personalized experience. This paper explores how BERT (bidirectional encoder representations from transformers), a model with significant performance in natural language processing, can provide intelligent recommendations for vocabulary learning in MALL. First, it briefly introduces the basics of BERT and how it works. Then, it delves into how BERT can be utilized for data pre-processing, feature extraction, and model training in a MALL environment. In addition, this paper describes how to design an intelligent recommender system based on the user's learning history and preferences and proposes some ideas for integration with other technologies. This study suggests a new methodological framework by applying BERT technology in intelligent recommender systems within the MALL environment. Our research demonstrates how the deep learning capabilities of BERT can potentially enhance the personalized learning experience and efficiency of MALL systems. It provides a new perspective and theoretical foundation for applying deep learning technology in language learning, although further empirical research is required to validate these theoretical assumptions. Finally, an outlook on the future of BERT application in MALL and some suggestions are introduced. This paper aims to provide a reference framework for researchers and educators to understand and apply BERT in the field of MALL deeply. © 2023 IEEE.

关键词： bidirectional encoder representations from transformers intelligent recommender systems mobile-assisted language learning vocabulary learning

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