Objective Natural language processing (NLP) was used to excavate and visualize the core content of syndrome element syndrome differentiation (SESD). Methods The first step was to build a text mining and analysis envir...Objective Natural language processing (NLP) was used to excavate and visualize the core content of syndrome element syndrome differentiation (SESD). Methods The first step was to build a text mining and analysis environment based on Python language, and built a corpus based on the core chapters of SESD. The second step was to digitalize the corpus. The main steps included word segmentation, information cleaning and merging, document-entry matrix, dictionary compilation and information conversion. The third step was to mine and display the internal information of SESD corpus by means of word cloud, keyword extraction and visualization. Results NLP played a positive role in computer recognition and comprehension of SESD. Different chapters had different keywords and weights. Deficiency syndrome elements were an important component of SESD, such as "Qi deficiency""Yang deficiency" and "Yin deficiency". The important syndrome elements of substantiality included "Blood stasis""Qi stagnation", etc. Core syndrome elements were closely related. Conclusions Syndrome differentiation and treatment was the core of SESD. Using NLP to excavate syndromes differentiation could help reveal the internal relationship between syndromes differentiation and provide basis for artificial intelligence to learn syndromes differentiation.展开更多
As Natural Language Processing(NLP)continues to advance,driven by the emergence of sophisticated large language models such as ChatGPT,there has been a notable growth in research activity.This rapid uptake reflects in...As Natural Language Processing(NLP)continues to advance,driven by the emergence of sophisticated large language models such as ChatGPT,there has been a notable growth in research activity.This rapid uptake reflects increasing interest in the field and induces critical inquiries into ChatGPT’s applicability in the NLP domain.This review paper systematically investigates the role of ChatGPT in diverse NLP tasks,including information extraction,Name Entity Recognition(NER),event extraction,relation extraction,Part of Speech(PoS)tagging,text classification,sentiment analysis,emotion recognition and text annotation.The novelty of this work lies in its comprehensive analysis of the existing literature,addressing a critical gap in understanding ChatGPT’s adaptability,limitations,and optimal application.In this paper,we employed a systematic stepwise approach following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)framework to direct our search process and seek relevant studies.Our review reveals ChatGPT’s significant potential in enhancing various NLP tasks.Its adaptability in information extraction tasks,sentiment analysis,and text classification showcases its ability to comprehend diverse contexts and extract meaningful details.Additionally,ChatGPT’s flexibility in annotation tasks reducesmanual efforts and accelerates the annotation process,making it a valuable asset in NLP development and research.Furthermore,GPT-4 and prompt engineering emerge as a complementary mechanism,empowering users to guide the model and enhance overall accuracy.Despite its promising potential,challenges persist.The performance of ChatGP Tneeds tobe testedusingmore extensivedatasets anddiversedata structures.Subsequently,its limitations in handling domain-specific language and the need for fine-tuning in specific applications highlight the importance of further investigations to address these issues.展开更多
Language disorder,a common manifestation of Alzheimer’s disease(AD),has attracted widespread attention in recent years.This paper uses a novel natural language processing(NLP)method,compared with latest deep learning...Language disorder,a common manifestation of Alzheimer’s disease(AD),has attracted widespread attention in recent years.This paper uses a novel natural language processing(NLP)method,compared with latest deep learning technology,to detect AD and explore the lexical performance.Our proposed approach is based on two stages.First,the dialogue contents are summarized into two categories with the same category.Second,term frequency—inverse document frequency(TF-IDF)algorithm is used to extract the keywords of transcripts,and the similarity of keywords between the groups was calculated separately by cosine distance.Several deep learning methods are used to compare the performance.In the meanwhile,keywords with the best performance are used to analyze AD patients’lexical performance.In the Predictive Challenge of Alzheimer’s Disease held by iFlytek in 2019,the proposed AD diagnosis model achieves a better performance in binary classification by adjusting the number of keywords.The F1 score of the model has a considerable improvement over the baseline of 75.4%,and the training process of which is simple and efficient.We analyze the keywords of the model and find that AD patients use less noun and verb than normal controls.A computer-assisted AD diagnosis model on small Chinese dataset is proposed in this paper,which provides a potential way for assisting diagnosis of AD and analyzing lexical performance in clinical setting.展开更多
This article investigates the dynamic relationship between technology and AI(artificial intelligence)and the role that societal requirements play in pushing AI research and adoption.Technology has advanced dramaticall...This article investigates the dynamic relationship between technology and AI(artificial intelligence)and the role that societal requirements play in pushing AI research and adoption.Technology has advanced dramatically throughout the years,providing the groundwork for the rise of AI.AI systems have achieved incredible feats in various disciplines thanks to advancements in computer power,data availability,and complex algorithms.On the other hand,society’s needs for efficiency,enhanced healthcare,environmental sustainability,and personalized experiences have worked as powerful accelerators for AI’s progress.This article digs into how technology empowers AI and how societal needs dictate its progress,emphasizing their symbiotic relationship.The findings underline the significance of responsible AI research,which considers both technological prowess and ethical issues,to ensure that AI continues to serve the greater good.展开更多
随着计算机算力的提升和智能设备的普及,社会逐步进入智慧化时代。高校图书馆作为高校的文献信息中心,进行智慧化转型提升服务质量是时代所需。因此,文章借助智能问答技术,设计了基于自然语言处理(Natural Language Processing,NLP)的...随着计算机算力的提升和智能设备的普及,社会逐步进入智慧化时代。高校图书馆作为高校的文献信息中心,进行智慧化转型提升服务质量是时代所需。因此,文章借助智能问答技术,设计了基于自然语言处理(Natural Language Processing,NLP)的图书馆智能问答系统,创新图书馆参考咨询服务模式,提高图书馆服务水平和效率。展开更多
随着人工智能技术的快速发展,自然语言处理(Natural Language Processing,NLP)技术在各个领域得到了广泛应用。文章提出一种基于NLP技术的智能培训系统中知识点与题库关联方法,该方法利用NLP技术对培训材料进行文本分析,自动提取知识点...随着人工智能技术的快速发展,自然语言处理(Natural Language Processing,NLP)技术在各个领域得到了广泛应用。文章提出一种基于NLP技术的智能培训系统中知识点与题库关联方法,该方法利用NLP技术对培训材料进行文本分析,自动提取知识点,并基于知识点和题库之间建立关联模型,实现试卷题目的自动分配。该方法能够有效提高培训系统的智能化水平,提高培训效率和质量。展开更多
随着自然语言处理(Natural Language Processing,NLP)技术的发展,其对各行各业的发展注入了新的动力,同时在网络教育快速发展的背景下,二者的有机融合也便成为热点。本文提出基于浏览器/服务器(Browser/Server,B/S)模式,通过建立录题模...随着自然语言处理(Natural Language Processing,NLP)技术的发展,其对各行各业的发展注入了新的动力,同时在网络教育快速发展的背景下,二者的有机融合也便成为热点。本文提出基于浏览器/服务器(Browser/Server,B/S)模式,通过建立录题模板实现对试题的分割和录入,借助Textrank4zh和word2vec模块,建立以TextRank算法为基础的隐马尔可夫模型完成组卷功能,完成以Vue.js框架为前端和Flask框架为后端的题库考试系统的设计与实现。该项目在减轻教师工作量的同时可更好地考察学生知识掌握的程度。展开更多
Background:In this investigation,we explore the literature regarding neuroregeneration from the 1700s to the present.The regeneration of central nervous system neurons or the regeneration of axons from cell bodies and...Background:In this investigation,we explore the literature regarding neuroregeneration from the 1700s to the present.The regeneration of central nervous system neurons or the regeneration of axons from cell bodies and their reconnection with other neurons remains a major hurdle.Injuries relating to war and accidents attracted medical professionals throughout early history to regenerate and reconnect nerves.Early literature till 1990 lacked specific molecular details and is likely provide some clues to conditions that promoted neuron and/or axon regeneration.This is an avenue for the application of natural language processing(NLP)to gain actionable intelligence.Post 1990 period saw an explosion of all molecular details.With the advent of genomic,transcriptomics,proteomics,and other omics-there is an emergence of big data sets and is another rich area for application of NLP.How the neuron and/or axon regeneration related keywords have changed over the years is a first step towards this endeavor.Methods:Specifically,this article curates over 600 published works in the field of neuroregeneration.We then apply a dynamic topic modeling algorithm based on the Latent Dirichlet allocation(LDA)algorithm to assess how topics cluster based on topics.Results:Based on how documents are assigned to topics,we then build a recommendation engine to assist researchers to access domain-specific literature based on how their search text matches to recommended document topics.The interface further includes interactive topic visualizations for researchers to understand how topics grow closer and further apart,and how intra-topic composition changes over time.Conclusions:We present a recommendation engine and interactive interface that enables dynamic topic modeling for neuronal regeneration.展开更多
The Memorable Tourist Experience(MTE)is a scientific concept within the studies on tourism that is developed based on several related constructions:Perceived Confidence,Sincerity,Authenticity,and Satisfaction.This wor...The Memorable Tourist Experience(MTE)is a scientific concept within the studies on tourism that is developed based on several related constructions:Perceived Confidence,Sincerity,Authenticity,and Satisfaction.This work takes this model established by the work of Dr.Babak Taheri in 2018 on Monuments World Heritage of UNESCO,adopting an alternative data collection method to the face-to-face survey.Therefore,this work takes as a source of data the reviews collected in the recommendation platform TripAdvisor,working the same constructions of the MTE,with the collection of similar terms and the relationships between them.In order to highlight the terms,a first step is established with the use of Natural Language Processing(NLP),followed by the use of Machine Learning(ML)techniques to generate the relationships between the constructors defined in the models.The study makes a comparison using the method,in immaterial nature such as a flamenco show in the city of Seville;Flamenco has been declared by UNESCO an intangible World Heritage Site since 2010.The results of the study go in two directions:on the one hand to find similarities in the study of the specific MTE of both monuments with the hypotheses worked in the original model of Taheri.In addition to highlighting possible distinctive elements of each case and,and furthermore within the value contribution of the visit when it is led by an official tour guide,on the other hand,give presence to the model of obtaining data by reviews as a complementary data source of any tourist study.The data collection and analysis from both NLP and ML techniques permit the scientific study and the tourist operators to develop better value propositions to users and understanding of heterogeneous behaviors in the tourism industry.The study of reviews within the MTE allows identifying the stimulus that leads the user to choose an activity and hire it.These studies are extendable to other industries and business models,given the importance that references acquire within the consumer willing to buy.For the scientific community,the use of ML is a solid way to initiate studies on behavioral models,supplement them,and accept or reject hypotheses.When the source of the data is taken from free expressions,such as reviews,the appearance of bias in the behavior is attenuated.展开更多
基金the funding support from the National Natural Science Foundation of China (No. 81874429)Digital and Applied Research Platform for Diagnosis of Traditional Chinese Medicine (No. 49021003005)+1 种基金2018 Hunan Provincial Postgraduate Research Innovation Project (No. CX2018B465)Excellent Youth Project of Hunan Education Department in 2018 (No. 18B241)
文摘Objective Natural language processing (NLP) was used to excavate and visualize the core content of syndrome element syndrome differentiation (SESD). Methods The first step was to build a text mining and analysis environment based on Python language, and built a corpus based on the core chapters of SESD. The second step was to digitalize the corpus. The main steps included word segmentation, information cleaning and merging, document-entry matrix, dictionary compilation and information conversion. The third step was to mine and display the internal information of SESD corpus by means of word cloud, keyword extraction and visualization. Results NLP played a positive role in computer recognition and comprehension of SESD. Different chapters had different keywords and weights. Deficiency syndrome elements were an important component of SESD, such as "Qi deficiency""Yang deficiency" and "Yin deficiency". The important syndrome elements of substantiality included "Blood stasis""Qi stagnation", etc. Core syndrome elements were closely related. Conclusions Syndrome differentiation and treatment was the core of SESD. Using NLP to excavate syndromes differentiation could help reveal the internal relationship between syndromes differentiation and provide basis for artificial intelligence to learn syndromes differentiation.
文摘As Natural Language Processing(NLP)continues to advance,driven by the emergence of sophisticated large language models such as ChatGPT,there has been a notable growth in research activity.This rapid uptake reflects increasing interest in the field and induces critical inquiries into ChatGPT’s applicability in the NLP domain.This review paper systematically investigates the role of ChatGPT in diverse NLP tasks,including information extraction,Name Entity Recognition(NER),event extraction,relation extraction,Part of Speech(PoS)tagging,text classification,sentiment analysis,emotion recognition and text annotation.The novelty of this work lies in its comprehensive analysis of the existing literature,addressing a critical gap in understanding ChatGPT’s adaptability,limitations,and optimal application.In this paper,we employed a systematic stepwise approach following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)framework to direct our search process and seek relevant studies.Our review reveals ChatGPT’s significant potential in enhancing various NLP tasks.Its adaptability in information extraction tasks,sentiment analysis,and text classification showcases its ability to comprehend diverse contexts and extract meaningful details.Additionally,ChatGPT’s flexibility in annotation tasks reducesmanual efforts and accelerates the annotation process,making it a valuable asset in NLP development and research.Furthermore,GPT-4 and prompt engineering emerge as a complementary mechanism,empowering users to guide the model and enhance overall accuracy.Despite its promising potential,challenges persist.The performance of ChatGP Tneeds tobe testedusingmore extensivedatasets anddiversedata structures.Subsequently,its limitations in handling domain-specific language and the need for fine-tuning in specific applications highlight the importance of further investigations to address these issues.
基金the Natural Science Foundation of Zhejiang Province(No.GF20F020063)the Fujian Province Young and Middle-Aged Teacher Education Research Project(No.JAT170480)。
文摘Language disorder,a common manifestation of Alzheimer’s disease(AD),has attracted widespread attention in recent years.This paper uses a novel natural language processing(NLP)method,compared with latest deep learning technology,to detect AD and explore the lexical performance.Our proposed approach is based on two stages.First,the dialogue contents are summarized into two categories with the same category.Second,term frequency—inverse document frequency(TF-IDF)algorithm is used to extract the keywords of transcripts,and the similarity of keywords between the groups was calculated separately by cosine distance.Several deep learning methods are used to compare the performance.In the meanwhile,keywords with the best performance are used to analyze AD patients’lexical performance.In the Predictive Challenge of Alzheimer’s Disease held by iFlytek in 2019,the proposed AD diagnosis model achieves a better performance in binary classification by adjusting the number of keywords.The F1 score of the model has a considerable improvement over the baseline of 75.4%,and the training process of which is simple and efficient.We analyze the keywords of the model and find that AD patients use less noun and verb than normal controls.A computer-assisted AD diagnosis model on small Chinese dataset is proposed in this paper,which provides a potential way for assisting diagnosis of AD and analyzing lexical performance in clinical setting.
文摘This article investigates the dynamic relationship between technology and AI(artificial intelligence)and the role that societal requirements play in pushing AI research and adoption.Technology has advanced dramatically throughout the years,providing the groundwork for the rise of AI.AI systems have achieved incredible feats in various disciplines thanks to advancements in computer power,data availability,and complex algorithms.On the other hand,society’s needs for efficiency,enhanced healthcare,environmental sustainability,and personalized experiences have worked as powerful accelerators for AI’s progress.This article digs into how technology empowers AI and how societal needs dictate its progress,emphasizing their symbiotic relationship.The findings underline the significance of responsible AI research,which considers both technological prowess and ethical issues,to ensure that AI continues to serve the greater good.
文摘随着计算机算力的提升和智能设备的普及,社会逐步进入智慧化时代。高校图书馆作为高校的文献信息中心,进行智慧化转型提升服务质量是时代所需。因此,文章借助智能问答技术,设计了基于自然语言处理(Natural Language Processing,NLP)的图书馆智能问答系统,创新图书馆参考咨询服务模式,提高图书馆服务水平和效率。
文摘随着人工智能技术的快速发展,自然语言处理(Natural Language Processing,NLP)技术在各个领域得到了广泛应用。文章提出一种基于NLP技术的智能培训系统中知识点与题库关联方法,该方法利用NLP技术对培训材料进行文本分析,自动提取知识点,并基于知识点和题库之间建立关联模型,实现试卷题目的自动分配。该方法能够有效提高培训系统的智能化水平,提高培训效率和质量。
文摘随着自然语言处理(Natural Language Processing,NLP)技术的发展,其对各行各业的发展注入了新的动力,同时在网络教育快速发展的背景下,二者的有机融合也便成为热点。本文提出基于浏览器/服务器(Browser/Server,B/S)模式,通过建立录题模板实现对试题的分割和录入,借助Textrank4zh和word2vec模块,建立以TextRank算法为基础的隐马尔可夫模型完成组卷功能,完成以Vue.js框架为前端和Flask框架为后端的题库考试系统的设计与实现。该项目在减轻教师工作量的同时可更好地考察学生知识掌握的程度。
文摘Background:In this investigation,we explore the literature regarding neuroregeneration from the 1700s to the present.The regeneration of central nervous system neurons or the regeneration of axons from cell bodies and their reconnection with other neurons remains a major hurdle.Injuries relating to war and accidents attracted medical professionals throughout early history to regenerate and reconnect nerves.Early literature till 1990 lacked specific molecular details and is likely provide some clues to conditions that promoted neuron and/or axon regeneration.This is an avenue for the application of natural language processing(NLP)to gain actionable intelligence.Post 1990 period saw an explosion of all molecular details.With the advent of genomic,transcriptomics,proteomics,and other omics-there is an emergence of big data sets and is another rich area for application of NLP.How the neuron and/or axon regeneration related keywords have changed over the years is a first step towards this endeavor.Methods:Specifically,this article curates over 600 published works in the field of neuroregeneration.We then apply a dynamic topic modeling algorithm based on the Latent Dirichlet allocation(LDA)algorithm to assess how topics cluster based on topics.Results:Based on how documents are assigned to topics,we then build a recommendation engine to assist researchers to access domain-specific literature based on how their search text matches to recommended document topics.The interface further includes interactive topic visualizations for researchers to understand how topics grow closer and further apart,and how intra-topic composition changes over time.Conclusions:We present a recommendation engine and interactive interface that enables dynamic topic modeling for neuronal regeneration.
文摘The Memorable Tourist Experience(MTE)is a scientific concept within the studies on tourism that is developed based on several related constructions:Perceived Confidence,Sincerity,Authenticity,and Satisfaction.This work takes this model established by the work of Dr.Babak Taheri in 2018 on Monuments World Heritage of UNESCO,adopting an alternative data collection method to the face-to-face survey.Therefore,this work takes as a source of data the reviews collected in the recommendation platform TripAdvisor,working the same constructions of the MTE,with the collection of similar terms and the relationships between them.In order to highlight the terms,a first step is established with the use of Natural Language Processing(NLP),followed by the use of Machine Learning(ML)techniques to generate the relationships between the constructors defined in the models.The study makes a comparison using the method,in immaterial nature such as a flamenco show in the city of Seville;Flamenco has been declared by UNESCO an intangible World Heritage Site since 2010.The results of the study go in two directions:on the one hand to find similarities in the study of the specific MTE of both monuments with the hypotheses worked in the original model of Taheri.In addition to highlighting possible distinctive elements of each case and,and furthermore within the value contribution of the visit when it is led by an official tour guide,on the other hand,give presence to the model of obtaining data by reviews as a complementary data source of any tourist study.The data collection and analysis from both NLP and ML techniques permit the scientific study and the tourist operators to develop better value propositions to users and understanding of heterogeneous behaviors in the tourism industry.The study of reviews within the MTE allows identifying the stimulus that leads the user to choose an activity and hire it.These studies are extendable to other industries and business models,given the importance that references acquire within the consumer willing to buy.For the scientific community,the use of ML is a solid way to initiate studies on behavioral models,supplement them,and accept or reject hypotheses.When the source of the data is taken from free expressions,such as reviews,the appearance of bias in the behavior is attenuated.
