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
Software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty requirements.Researchers are exploring machine learn...Software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty requirements.Researchers are exploring machine learning to predict software bugs,but a more precise and general approach is needed.Accurate bug prediction is crucial for software evolution and user training,prompting an investigation into deep and ensemble learning methods.However,these studies are not generalized and efficient when extended to other datasets.Therefore,this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification problems.The methods involved feature selection,which is used to reduce the dimensionality and redundancy of features and select only the relevant ones;transfer learning is used to train and test the model on different datasets to analyze how much of the learning is passed to other datasets,and ensemble method is utilized to explore the increase in performance upon combining multiple classifiers in a model.Four National Aeronautics and Space Administration(NASA)and four Promise datasets are used in the study,showing an increase in the model’s performance by providing better Area Under the Receiver Operating Characteristic Curve(AUC-ROC)values when different classifiers were combined.It reveals that using an amalgam of techniques such as those used in this study,feature selection,transfer learning,and ensemble methods prove helpful in optimizing the software bug prediction models and providing high-performing,useful end mode.展开更多
In response to the challenges of generating Attribute-Based Access Control(ABAC)policies,this paper proposes a deep learning-based method to automatically generate ABAC policies from natural language documents.This me...In response to the challenges of generating Attribute-Based Access Control(ABAC)policies,this paper proposes a deep learning-based method to automatically generate ABAC policies from natural language documents.This method is aimed at organizations such as companies and schools that are transitioning from traditional access control models to the ABAC model.The manual retrieval and analysis involved in this transition are inefficient,prone to errors,and costly.Most organizations have high-level specifications defined for security policies that include a set of access control policies,which often exist in the form of natural language documents.Utilizing this rich source of information,our method effectively identifies and extracts the necessary attributes and rules for access control from natural language documents,thereby constructing and optimizing access control policies.This work transforms the problem of policy automation generation into two tasks:extraction of access control statements andmining of access control attributes.First,the Chat General Language Model(ChatGLM)isemployed to extract access control-related statements from a wide range of natural language documents by constructing unique prompts and leveraging the model’s In-Context Learning to contextualize the statements.Then,the Iterated Dilated-Convolutions-Conditional Random Field(ID-CNN-CRF)model is used to annotate access control attributes within these extracted statements,including subject attributes,object attributes,and action attributes,thus reassembling new access control policies.Experimental results show that our method,compared to baseline methods,achieved the highest F1 score of 0.961,confirming the model’s effectiveness and accuracy.展开更多
The exponential growth of literature is constraining researchers’access to comprehensive information in related fields.While natural language processing(NLP)may offer an effective solution to literature classificatio...The exponential growth of literature is constraining researchers’access to comprehensive information in related fields.While natural language processing(NLP)may offer an effective solution to literature classification,it remains hindered by the lack of labelled dataset.In this article,we introduce a novel method for generating literature classification models through semi-supervised learning,which can generate labelled dataset iteratively with limited human input.We apply this method to train NLP models for classifying literatures related to several research directions,i.e.,battery,superconductor,topological material,and artificial intelligence(AI)in materials science.The trained NLP‘battery’model applied on a larger dataset different from the training and testing dataset can achieve F1 score of 0.738,which indicates the accuracy and reliability of this scheme.Furthermore,our approach demonstrates that even with insufficient data,the not-well-trained model in the first few cycles can identify the relationships among different research fields and facilitate the discovery and understanding of interdisciplinary directions.展开更多
This work is about the progress of previous related work based on an experiment to improve the intelligence of robotic systems,with the aim of achieving more linguistic communication capabilities between humans and ro...This work is about the progress of previous related work based on an experiment to improve the intelligence of robotic systems,with the aim of achieving more linguistic communication capabilities between humans and robots.In this paper,the authors attempt an algorithmic approach to natural language generation through hole semantics and by applying the OMAS-III computational model as a grammatical formalism.In the original work,a technical language is used,while in the later works,this has been replaced by a limited Greek natural language dictionary.This particular effort was made to give the evolving system the ability to ask questions,as well as the authors developed an initial dialogue system using these techniques.The results show that the use of these techniques the authors apply can give us a more sophisticated dialogue system in the future.展开更多
Sentiment analysis(SA)is the procedure of recognizing the emotions related to the data that exist in social networking.The existence of sarcasm in tex-tual data is a major challenge in the efficiency of the SA.Earlier...Sentiment analysis(SA)is the procedure of recognizing the emotions related to the data that exist in social networking.The existence of sarcasm in tex-tual data is a major challenge in the efficiency of the SA.Earlier works on sarcasm detection on text utilize lexical as well as pragmatic cues namely interjection,punctuations,and sentiment shift that are vital indicators of sarcasm.With the advent of deep-learning,recent works,leveraging neural networks in learning lexical and contextual features,removing the need for handcrafted feature.In this aspect,this study designs a deep learning with natural language processing enabled SA(DLNLP-SA)technique for sarcasm classification.The proposed DLNLP-SA technique aims to detect and classify the occurrence of sarcasm in the input data.Besides,the DLNLP-SA technique holds various sub-processes namely preprocessing,feature vector conversion,and classification.Initially,the pre-processing is performed in diverse ways such as single character removal,multi-spaces removal,URL removal,stopword removal,and tokenization.Secondly,the transformation of feature vectors takes place using the N-gram feature vector technique.Finally,mayfly optimization(MFO)with multi-head self-attention based gated recurrent unit(MHSA-GRU)model is employed for the detection and classification of sarcasm.To verify the enhanced outcomes of the DLNLP-SA model,a comprehensive experimental investigation is performed on the News Headlines Dataset from Kaggle Repository and the results signified the supremacy over the existing approaches.展开更多
A variety of neural networks have been presented to deal with issues in deep learning in the last decades.Despite the prominent success achieved by the neural network,it still lacks theoretical guidance to design an e...A variety of neural networks have been presented to deal with issues in deep learning in the last decades.Despite the prominent success achieved by the neural network,it still lacks theoretical guidance to design an efficient neural network model,and verifying the performance of a model needs excessive resources.Previous research studies have demonstrated that many existing models can be regarded as different numerical discretizations of differential equations.This connection sheds light on designing an effective recurrent neural network(RNN)by resorting to numerical analysis.Simple RNN is regarded as a discretisation of the forward Euler scheme.Considering the limited solution accuracy of the forward Euler methods,a Taylor‐type discrete scheme is presented with lower truncation error and a Taylor‐type RNN(T‐RNN)is designed with its guidance.Extensive experiments are conducted to evaluate its performance on statistical language models and emotion analysis tasks.The noticeable gains obtained by T‐RNN present its superiority and the feasibility of designing the neural network model using numerical methods.展开更多
One of the critical hurdles, and breakthroughs, in the field of Natural Language Processing (NLP) in the last two decades has been the development of techniques for text representation that solves the so-called curse ...One of the critical hurdles, and breakthroughs, in the field of Natural Language Processing (NLP) in the last two decades has been the development of techniques for text representation that solves the so-called curse of dimensionality, a problem which plagues NLP in general given that the feature set for learning starts as a function of the size of the language in question, upwards of hundreds of thousands of terms typically. As such, much of the research and development in NLP in the last two decades has been in finding and optimizing solutions to this problem, to feature selection in NLP effectively. This paper looks at the development of these various techniques, leveraging a variety of statistical methods which rest on linguistic theories that were advanced in the middle of the last century, namely the distributional hypothesis which suggests that words that are found in similar contexts generally have similar meanings. In this survey paper we look at the development of some of the most popular of these techniques from a mathematical as well as data structure perspective, from Latent Semantic Analysis to Vector Space Models to their more modern variants which are typically referred to as word embeddings. In this review of algoriths such as Word2Vec, GloVe, ELMo and BERT, we explore the idea of semantic spaces more generally beyond applicability to NLP.展开更多
The recent developments in Multimedia Internet of Things(MIoT)devices,empowered with Natural Language Processing(NLP)model,seem to be a promising future of smart devices.It plays an important role in industrial models...The recent developments in Multimedia Internet of Things(MIoT)devices,empowered with Natural Language Processing(NLP)model,seem to be a promising future of smart devices.It plays an important role in industrial models such as speech understanding,emotion detection,home automation,and so on.If an image needs to be captioned,then the objects in that image,its actions and connections,and any silent feature that remains under-projected or missing from the images should be identified.The aim of the image captioning process is to generate a caption for image.In next step,the image should be provided with one of the most significant and detailed descriptions that is syntactically as well as semantically correct.In this scenario,computer vision model is used to identify the objects and NLP approaches are followed to describe the image.The current study develops aNatural Language Processing with Optimal Deep Learning Enabled Intelligent Image Captioning System(NLPODL-IICS).The aim of the presented NLPODL-IICS model is to produce a proper description for input image.To attain this,the proposed NLPODL-IICS follows two stages such as encoding and decoding processes.Initially,at the encoding side,the proposed NLPODL-IICS model makes use of Hunger Games Search(HGS)with Neural Search Architecture Network(NASNet)model.This model represents the input data appropriately by inserting it into a predefined length vector.Besides,during decoding phase,Chimp Optimization Algorithm(COA)with deeper Long Short Term Memory(LSTM)approach is followed to concatenate the description sentences 4436 CMC,2023,vol.74,no.2 produced by the method.The application of HGS and COA algorithms helps in accomplishing proper parameter tuning for NASNet and LSTM models respectively.The proposed NLPODL-IICS model was experimentally validated with the help of two benchmark datasets.Awidespread comparative analysis confirmed the superior performance of NLPODL-IICS model over other models.展开更多
This paper attempts to approach the interface of a robot from the perspective of virtual assistants.Virtual assistants can also be characterized as the mind of a robot,since they manage communication and action with t...This paper attempts to approach the interface of a robot from the perspective of virtual assistants.Virtual assistants can also be characterized as the mind of a robot,since they manage communication and action with the rest of the world they exist in.Therefore,virtual assistants can also be described as the brain of a robot and they include a Natural Language Processing(NLP)module for conducting communication in their human-robot interface.This work is focused on inquiring and enhancing the capabilities of this module.The problem is that nothing much is revealed about the nature of the human-robot interface of commercial virtual assistants.Therefore,any new attempt of developing such a capability has to start from scratch.Accordingly,to include corresponding capabilities to a developing NLP system of a virtual assistant,a method of systemic semantic modelling is proposed and applied.For this purpose,the paper briefly reviews the evolution of virtual assistants from the first assistant,in the form of a game,to the latest assistant that has significantly elevated their standards.Then there is a reference to the evolution of their services and their continued offerings,as well as future expectations.The paper presents their structure and the technologies used,according to the data provided by the development companies to the public,while an attempt is made to classify virtual assistants,based on their characteristics and capabilities.Consequently,a robotic NLP interface is being developed,based on the communicative power of a proposed systemic conceptual model that may enhance the NLP capabilities of virtual assistants,being tested through a small natural language dictionary in Greek.展开更多
Purpose:This work aims to normalize the NLPCONTRIBUTIONS scheme(henceforward,NLPCONTRIBUTIONGRAPH)to structure,directly from article sentences,the contributions information in Natural Language Processing(NLP)scholarly...Purpose:This work aims to normalize the NLPCONTRIBUTIONS scheme(henceforward,NLPCONTRIBUTIONGRAPH)to structure,directly from article sentences,the contributions information in Natural Language Processing(NLP)scholarly articles via a two-stage annotation methodology:1)pilot stage-to define the scheme(described in prior work);and 2)adjudication stage-to normalize the graphing model(the focus of this paper).Design/methodology/approach:We re-annotate,a second time,the contributions-pertinent information across 50 prior-annotated NLP scholarly articles in terms of a data pipeline comprising:contribution-centered sentences,phrases,and triple statements.To this end,specifically,care was taken in the adjudication annotation stage to reduce annotation noise while formulating the guidelines for our proposed novel NLP contributions structuring and graphing scheme.Findings:The application of NLPCONTRIBUTIONGRAPH on the 50 articles resulted finally in a dataset of 900 contribution-focused sentences,4,702 contribution-information-centered phrases,and 2,980 surface-structured triples.The intra-annotation agreement between the first and second stages,in terms of F1-score,was 67.92%for sentences,41.82%for phrases,and 22.31%for triple statements indicating that with increased granularity of the information,the annotation decision variance is greater.Research limitations:NLPCONTRIBUTIONGRAPH has limited scope for structuring scholarly contributions compared with STEM(Science,Technology,Engineering,and Medicine)scholarly knowledge at large.Further,the annotation scheme in this work is designed by only an intra-annotator consensus-a single annotator first annotated the data to propose the initial scheme,following which,the same annotator reannotated the data to normalize the annotations in an adjudication stage.However,the expected goal of this work is to achieve a standardized retrospective model of capturing NLP contributions from scholarly articles.This would entail a larger initiative of enlisting multiple annotators to accommodate different worldviews into a“single”set of structures and relationships as the final scheme.Given that the initial scheme is first proposed and the complexity of the annotation task in the realistic timeframe,our intraannotation procedure is well-suited.Nevertheless,the model proposed in this work is presently limited since it does not incorporate multiple annotator worldviews.This is planned as future work to produce a robust model.Practical implications:We demonstrate NLPCONTRIBUTIONGRAPH data integrated into the Open Research Knowledge Graph(ORKG),a next-generation KG-based digital library with intelligent computations enabled over structured scholarly knowledge,as a viable aid to assist researchers in their day-to-day tasks.Originality/value:NLPCONTRIBUTIONGRAPH is a novel scheme to annotate research contributions from NLP articles and integrate them in a knowledge graph,which to the best of our knowledge does not exist in the community.Furthermore,our quantitative evaluations over the two-stage annotation tasks offer insights into task difficulty.展开更多
A support vector rule based method is investigated for the construction of motion controllers via natural language training. It is a two-phase process including motion control information collection from natural langu...A support vector rule based method is investigated for the construction of motion controllers via natural language training. It is a two-phase process including motion control information collection from natural language instructions, and motion information condensation with the aid of support vector machine （SVM） theory. Self-organizing fuzzy neural networks are utilized for the collection of control rules, from which support vector rules are extracted to form a final controller to achieve any given control accuracy. In this way, the number of control rules is reduced, and the structure of the controller tidied, making a controller constructed using natural language training more appropriate in practice, and providing a fundamental rule base for high-level robot behavior control. Simulations and experiments on a wheeled robot are carried out to illustrate the effectiveness of the method.展开更多
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
Numeral systems in natural languages show astonishing variety,though with very strong unifying tendencies that are increasing as many indigenous numeral systems disappear through language contact and globalization.Mos...Numeral systems in natural languages show astonishing variety,though with very strong unifying tendencies that are increasing as many indigenous numeral systems disappear through language contact and globalization.Most numeral systems make use of a base,typically 10,less commonly 20,followed by a wide range of other possibilities.Higher numerals are formed from primitive lower numerals by applying the processes of addition and multiplication,in many languages also exponentiation;sometimes,however,numerals are formed from a higher numeral,using subtraction or division.Numerous complexities and idiosyncrasies are discussed,as are numeral systems that fall outside this general characterization,such as restricted numeral systems with no internal arithmetic structure,and some New Guinea extended body-part counting systems.展开更多
In this research paper, we research on the automatic pattern abstraction and recognition method for large-scale database system based on natural language processing. In distributed database, through the network connec...In this research paper, we research on the automatic pattern abstraction and recognition method for large-scale database system based on natural language processing. In distributed database, through the network connection between nodes, data across different nodes and even regional distribution are well recognized. In order to reduce data redundancy and model design of the database will usually contain a lot of forms we combine the NLP theory to optimize the traditional method. The experimental analysis and simulation proves the correctness of our method.展开更多
The expert system is an important field of the artificial intelligence. The traditional interface of the expert system is the command, menu and window at present. It limits the application of the expert system and emb...The expert system is an important field of the artificial intelligence. The traditional interface of the expert system is the command, menu and window at present. It limits the application of the expert system and embarrasses the enthusiasm of using expert system. Combining with the study on the expert system of network fault diagnosis, the natural language interface of the expert system has been discussed in this article. This interface can understand and generate Chinese sentences. Using this interface, the user and field experts can use the expert system to diagnose the fault of network conveniently. In the article, first, the extended production rule has been proposed. Then the methods of Chinese sentence generation from conceptual graphs and the model of expert system are introduced in detail. Using this model, the network fault diagnosis expert system and its natural language interface have been developed with Prolog.展开更多
The recent advancements made in World Wide Web and social networking have eased the spread of fake news among people at a faster rate.At most of the times,the intention of fake news is to misinform the people and make...The recent advancements made in World Wide Web and social networking have eased the spread of fake news among people at a faster rate.At most of the times,the intention of fake news is to misinform the people and make manipulated societal insights.The spread of low-quality news in social networking sites has a negative influence upon people as well as the society.In order to overcome the ever-increasing dissemination of fake news,automated detection models are developed using Artificial Intelligence(AI)and Machine Learning(ML)methods.The latest advancements in Deep Learning(DL)models and complex Natural Language Processing(NLP)tasks make the former,a significant solution to achieve Fake News Detection(FND).In this background,the current study focuses on design and development of Natural Language Processing with Sea Turtle Foraging Optimizationbased Deep Learning Technique for Fake News Detection and Classification(STODL-FNDC)model.The aim of the proposed STODL-FNDC model is to discriminate fake news from legitimate news in an effectual manner.In the proposed STODL-FNDC model,the input data primarily undergoes pre-processing and Glove-based word embedding.Besides,STODL-FNDC model employs Deep Belief Network(DBN)approach for detection as well as classification of fake news.Finally,STO algorithm is utilized after adjusting the hyperparameters involved in DBN model,in an optimal manner.The novelty of the study lies in the design of STO algorithm with DBN model for FND.In order to improve the detection performance of STODL-FNDC technique,a series of simulations was carried out on benchmark datasets.The experimental outcomes established the better performance of STODL-FNDC approach over other methods with a maximum accuracy of 95.50%.展开更多
Student mobility or academic mobility involves students moving between institutions during their post-secondary education,and one of the challenging tasks in this process is to assess the transfer credits to be offere...Student mobility or academic mobility involves students moving between institutions during their post-secondary education,and one of the challenging tasks in this process is to assess the transfer credits to be offered to the incoming student.In general,this process involves domain experts comparing the learning outcomes of the courses,to decide on offering transfer credits to the incoming students.This manual implementation is not only labor-intensive but also influenced by undue bias and administrative complexity.The proposed research article focuses on identifying a model that exploits the advancements in the field of Natural Language Processing(NLP)to effectively automate this process.Given the unique structure,domain specificity,and complexity of learning outcomes(LOs),a need for designing a tailor-made model arises.The proposed model uses a clustering-inspired methodology based on knowledge-based semantic similarity measures to assess the taxonomic similarity of LOs and a transformer-based semantic similarity model to assess the semantic similarity of the LOs.The similarity between LOs is further aggregated to form course to course similarity.Due to the lack of quality benchmark datasets,a new benchmark dataset containing seven course-to-course similarity measures is proposed.Understanding the inherent need for flexibility in the decision-making process the aggregation part of the model offers tunable parameters to accommodate different levels of leniency.While providing an efficient model to assess the similarity between courses with existing resources,this research work also steers future research attempts to apply NLP in the field of articulation in an ideal direction by highlighting the persisting research gaps.展开更多
Most software systems have different stakeholders with a variety of concerns.The process of collecting requirements from a large number of stakeholders is vital but challenging.We propose an efficient,automatic approa...Most software systems have different stakeholders with a variety of concerns.The process of collecting requirements from a large number of stakeholders is vital but challenging.We propose an efficient,automatic approach to collecting requirements from different stakeholders’responses to a specific question.We use natural language processing techniques to get the stakeholder response that represents most other stakeholders’responses.This study improves existing practices in three ways:Firstly,it reduces the human effort needed to collect the requirements;secondly,it reduces the time required to carry out this task with a large number of stakeholders;thirdly,it underlines the importance of using of data mining techniques in various software engineering steps.Our approach uses tokenization,stop word removal,and word lemmatization to create a list of frequently accruing words.It then creates a similarity matrix to calculate the score value for each response and selects the answer with the highest score.Our experiments show that using this approach significantly reduces the time and effort needed to collect requirements and does so with a sufficient degree of accuracy.展开更多
Natural language semantic construction improves natural language comprehension ability and analytical skills of the machine.It is the basis for realizing the information exchange in the intelligent cloud-computing env...Natural language semantic construction improves natural language comprehension ability and analytical skills of the machine.It is the basis for realizing the information exchange in the intelligent cloud-computing environment.This paper proposes a natural language semantic construction method based on cloud database,mainly including two parts:natural language cloud database construction and natural language semantic construction.Natural Language cloud database is established on the CloudStack cloud-computing environment,which is composed by corpus,thesaurus,word vector library and ontology knowledge base.In this section,we concentrate on the pretreatment of corpus and the presentation of background knowledge ontology,and then put forward a TF-IDF and word vector distance based algorithm for duplicated webpages(TWDW).It raises the recognition efficiency of repeated web pages.The part of natural language semantic construction mainly introduces the dynamic process of semantic construction and proposes a mapping algorithm based on semantic similarity(MBSS),which is a bridge between Predicate-Argument(PA)structure and background knowledge ontology.Experiments show that compared with the relevant algorithms,the precision and recall of both algorithms we propose have been significantly improved.The work in this paper improves the understanding of natural language semantics,and provides effective data support for the natural language interaction function of the cloud service.展开更多
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This Research is funded by Researchers Supporting Project Number(RSPD2024R947),King Saud University,Riyadh,Saudi Arabia.
文摘Software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty requirements.Researchers are exploring machine learning to predict software bugs,but a more precise and general approach is needed.Accurate bug prediction is crucial for software evolution and user training,prompting an investigation into deep and ensemble learning methods.However,these studies are not generalized and efficient when extended to other datasets.Therefore,this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification problems.The methods involved feature selection,which is used to reduce the dimensionality and redundancy of features and select only the relevant ones;transfer learning is used to train and test the model on different datasets to analyze how much of the learning is passed to other datasets,and ensemble method is utilized to explore the increase in performance upon combining multiple classifiers in a model.Four National Aeronautics and Space Administration(NASA)and four Promise datasets are used in the study,showing an increase in the model’s performance by providing better Area Under the Receiver Operating Characteristic Curve(AUC-ROC)values when different classifiers were combined.It reveals that using an amalgam of techniques such as those used in this study,feature selection,transfer learning,and ensemble methods prove helpful in optimizing the software bug prediction models and providing high-performing,useful end mode.
基金supported by the National Natural Science Foundation of China Project(No.62302540),please visit their website at https://www.nsfc.gov.cn/(accessed on 18 June 2024)The Open Foundation of Henan Key Laboratory of Cyberspace Situation Awareness(No.HNTS2022020),Further details can be found at http://xt.hnkjt.gov.cn/data/pingtai/(accessed on 18 June 2024)Natural Science Foundation of Henan Province Youth Science Fund Project(No.232300420422),you can visit https://kjt.henan.gov.cn/2022/09-02/2599082.html(accessed on 18 June 2024).
文摘In response to the challenges of generating Attribute-Based Access Control(ABAC)policies,this paper proposes a deep learning-based method to automatically generate ABAC policies from natural language documents.This method is aimed at organizations such as companies and schools that are transitioning from traditional access control models to the ABAC model.The manual retrieval and analysis involved in this transition are inefficient,prone to errors,and costly.Most organizations have high-level specifications defined for security policies that include a set of access control policies,which often exist in the form of natural language documents.Utilizing this rich source of information,our method effectively identifies and extracts the necessary attributes and rules for access control from natural language documents,thereby constructing and optimizing access control policies.This work transforms the problem of policy automation generation into two tasks:extraction of access control statements andmining of access control attributes.First,the Chat General Language Model(ChatGLM)isemployed to extract access control-related statements from a wide range of natural language documents by constructing unique prompts and leveraging the model’s In-Context Learning to contextualize the statements.Then,the Iterated Dilated-Convolutions-Conditional Random Field(ID-CNN-CRF)model is used to annotate access control attributes within these extracted statements,including subject attributes,object attributes,and action attributes,thus reassembling new access control policies.Experimental results show that our method,compared to baseline methods,achieved the highest F1 score of 0.961,confirming the model’s effectiveness and accuracy.
基金funded by the Informatization Plan of Chinese Academy of Sciences(Grant No.CASWX2021SF-0102)the National Key R&D Program of China(Grant Nos.2022YFA1603903,2022YFA1403800,and 2021YFA0718700)+1 种基金the National Natural Science Foundation of China(Grant Nos.11925408,11921004,and 12188101)the Chinese Academy of Sciences(Grant No.XDB33000000)。
文摘The exponential growth of literature is constraining researchers’access to comprehensive information in related fields.While natural language processing(NLP)may offer an effective solution to literature classification,it remains hindered by the lack of labelled dataset.In this article,we introduce a novel method for generating literature classification models through semi-supervised learning,which can generate labelled dataset iteratively with limited human input.We apply this method to train NLP models for classifying literatures related to several research directions,i.e.,battery,superconductor,topological material,and artificial intelligence(AI)in materials science.The trained NLP‘battery’model applied on a larger dataset different from the training and testing dataset can achieve F1 score of 0.738,which indicates the accuracy and reliability of this scheme.Furthermore,our approach demonstrates that even with insufficient data,the not-well-trained model in the first few cycles can identify the relationships among different research fields and facilitate the discovery and understanding of interdisciplinary directions.
文摘This work is about the progress of previous related work based on an experiment to improve the intelligence of robotic systems,with the aim of achieving more linguistic communication capabilities between humans and robots.In this paper,the authors attempt an algorithmic approach to natural language generation through hole semantics and by applying the OMAS-III computational model as a grammatical formalism.In the original work,a technical language is used,while in the later works,this has been replaced by a limited Greek natural language dictionary.This particular effort was made to give the evolving system the ability to ask questions,as well as the authors developed an initial dialogue system using these techniques.The results show that the use of these techniques the authors apply can give us a more sophisticated dialogue system in the future.
基金supported through the Annual Funding track by the Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia[Project No.AN000685].
文摘Sentiment analysis(SA)is the procedure of recognizing the emotions related to the data that exist in social networking.The existence of sarcasm in tex-tual data is a major challenge in the efficiency of the SA.Earlier works on sarcasm detection on text utilize lexical as well as pragmatic cues namely interjection,punctuations,and sentiment shift that are vital indicators of sarcasm.With the advent of deep-learning,recent works,leveraging neural networks in learning lexical and contextual features,removing the need for handcrafted feature.In this aspect,this study designs a deep learning with natural language processing enabled SA(DLNLP-SA)technique for sarcasm classification.The proposed DLNLP-SA technique aims to detect and classify the occurrence of sarcasm in the input data.Besides,the DLNLP-SA technique holds various sub-processes namely preprocessing,feature vector conversion,and classification.Initially,the pre-processing is performed in diverse ways such as single character removal,multi-spaces removal,URL removal,stopword removal,and tokenization.Secondly,the transformation of feature vectors takes place using the N-gram feature vector technique.Finally,mayfly optimization(MFO)with multi-head self-attention based gated recurrent unit(MHSA-GRU)model is employed for the detection and classification of sarcasm.To verify the enhanced outcomes of the DLNLP-SA model,a comprehensive experimental investigation is performed on the News Headlines Dataset from Kaggle Repository and the results signified the supremacy over the existing approaches.
基金supported in part by the National Natural Science Foundation of China under Grant 62176109in part by the Tibetan Information Processing and Machine Translation Key Laboratory of Qinghai Province under Grant 2021‐Z‐003+3 种基金in part by the Natural Science Foundation of Gansu Province under Grant 21JR7RA531 and Grant 22JR5RA487in part by the Fundamental Research Funds for the Central Universities under Grant lzujbky‐2022‐23in part by the CAAI‐Huawei MindSpore Open Fund under Grant CAAIXSJLJJ‐2022‐020Ain part by the Supercomputing Center of Lanzhou University,in part by Sichuan Science and Technology Program No.2022nsfsc0916.
文摘A variety of neural networks have been presented to deal with issues in deep learning in the last decades.Despite the prominent success achieved by the neural network,it still lacks theoretical guidance to design an efficient neural network model,and verifying the performance of a model needs excessive resources.Previous research studies have demonstrated that many existing models can be regarded as different numerical discretizations of differential equations.This connection sheds light on designing an effective recurrent neural network(RNN)by resorting to numerical analysis.Simple RNN is regarded as a discretisation of the forward Euler scheme.Considering the limited solution accuracy of the forward Euler methods,a Taylor‐type discrete scheme is presented with lower truncation error and a Taylor‐type RNN(T‐RNN)is designed with its guidance.Extensive experiments are conducted to evaluate its performance on statistical language models and emotion analysis tasks.The noticeable gains obtained by T‐RNN present its superiority and the feasibility of designing the neural network model using numerical methods.
文摘One of the critical hurdles, and breakthroughs, in the field of Natural Language Processing (NLP) in the last two decades has been the development of techniques for text representation that solves the so-called curse of dimensionality, a problem which plagues NLP in general given that the feature set for learning starts as a function of the size of the language in question, upwards of hundreds of thousands of terms typically. As such, much of the research and development in NLP in the last two decades has been in finding and optimizing solutions to this problem, to feature selection in NLP effectively. This paper looks at the development of these various techniques, leveraging a variety of statistical methods which rest on linguistic theories that were advanced in the middle of the last century, namely the distributional hypothesis which suggests that words that are found in similar contexts generally have similar meanings. In this survey paper we look at the development of some of the most popular of these techniques from a mathematical as well as data structure perspective, from Latent Semantic Analysis to Vector Space Models to their more modern variants which are typically referred to as word embeddings. In this review of algoriths such as Word2Vec, GloVe, ELMo and BERT, we explore the idea of semantic spaces more generally beyond applicability to NLP.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R161)PrincessNourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the|Deanship of Scientific Research at Umm Al-Qura University|for supporting this work by Grant Code:(22UQU4310373DSR33).
文摘The recent developments in Multimedia Internet of Things(MIoT)devices,empowered with Natural Language Processing(NLP)model,seem to be a promising future of smart devices.It plays an important role in industrial models such as speech understanding,emotion detection,home automation,and so on.If an image needs to be captioned,then the objects in that image,its actions and connections,and any silent feature that remains under-projected or missing from the images should be identified.The aim of the image captioning process is to generate a caption for image.In next step,the image should be provided with one of the most significant and detailed descriptions that is syntactically as well as semantically correct.In this scenario,computer vision model is used to identify the objects and NLP approaches are followed to describe the image.The current study develops aNatural Language Processing with Optimal Deep Learning Enabled Intelligent Image Captioning System(NLPODL-IICS).The aim of the presented NLPODL-IICS model is to produce a proper description for input image.To attain this,the proposed NLPODL-IICS follows two stages such as encoding and decoding processes.Initially,at the encoding side,the proposed NLPODL-IICS model makes use of Hunger Games Search(HGS)with Neural Search Architecture Network(NASNet)model.This model represents the input data appropriately by inserting it into a predefined length vector.Besides,during decoding phase,Chimp Optimization Algorithm(COA)with deeper Long Short Term Memory(LSTM)approach is followed to concatenate the description sentences 4436 CMC,2023,vol.74,no.2 produced by the method.The application of HGS and COA algorithms helps in accomplishing proper parameter tuning for NASNet and LSTM models respectively.The proposed NLPODL-IICS model was experimentally validated with the help of two benchmark datasets.Awidespread comparative analysis confirmed the superior performance of NLPODL-IICS model over other models.
文摘This paper attempts to approach the interface of a robot from the perspective of virtual assistants.Virtual assistants can also be characterized as the mind of a robot,since they manage communication and action with the rest of the world they exist in.Therefore,virtual assistants can also be described as the brain of a robot and they include a Natural Language Processing(NLP)module for conducting communication in their human-robot interface.This work is focused on inquiring and enhancing the capabilities of this module.The problem is that nothing much is revealed about the nature of the human-robot interface of commercial virtual assistants.Therefore,any new attempt of developing such a capability has to start from scratch.Accordingly,to include corresponding capabilities to a developing NLP system of a virtual assistant,a method of systemic semantic modelling is proposed and applied.For this purpose,the paper briefly reviews the evolution of virtual assistants from the first assistant,in the form of a game,to the latest assistant that has significantly elevated their standards.Then there is a reference to the evolution of their services and their continued offerings,as well as future expectations.The paper presents their structure and the technologies used,according to the data provided by the development companies to the public,while an attempt is made to classify virtual assistants,based on their characteristics and capabilities.Consequently,a robotic NLP interface is being developed,based on the communicative power of a proposed systemic conceptual model that may enhance the NLP capabilities of virtual assistants,being tested through a small natural language dictionary in Greek.
基金This work was co-funded by the European Research Council for the project ScienceGRAPH(Grant agreement ID:819536)by the TIB Leibniz Information Centre for Science and Technology.
文摘Purpose:This work aims to normalize the NLPCONTRIBUTIONS scheme(henceforward,NLPCONTRIBUTIONGRAPH)to structure,directly from article sentences,the contributions information in Natural Language Processing(NLP)scholarly articles via a two-stage annotation methodology:1)pilot stage-to define the scheme(described in prior work);and 2)adjudication stage-to normalize the graphing model(the focus of this paper).Design/methodology/approach:We re-annotate,a second time,the contributions-pertinent information across 50 prior-annotated NLP scholarly articles in terms of a data pipeline comprising:contribution-centered sentences,phrases,and triple statements.To this end,specifically,care was taken in the adjudication annotation stage to reduce annotation noise while formulating the guidelines for our proposed novel NLP contributions structuring and graphing scheme.Findings:The application of NLPCONTRIBUTIONGRAPH on the 50 articles resulted finally in a dataset of 900 contribution-focused sentences,4,702 contribution-information-centered phrases,and 2,980 surface-structured triples.The intra-annotation agreement between the first and second stages,in terms of F1-score,was 67.92%for sentences,41.82%for phrases,and 22.31%for triple statements indicating that with increased granularity of the information,the annotation decision variance is greater.Research limitations:NLPCONTRIBUTIONGRAPH has limited scope for structuring scholarly contributions compared with STEM(Science,Technology,Engineering,and Medicine)scholarly knowledge at large.Further,the annotation scheme in this work is designed by only an intra-annotator consensus-a single annotator first annotated the data to propose the initial scheme,following which,the same annotator reannotated the data to normalize the annotations in an adjudication stage.However,the expected goal of this work is to achieve a standardized retrospective model of capturing NLP contributions from scholarly articles.This would entail a larger initiative of enlisting multiple annotators to accommodate different worldviews into a“single”set of structures and relationships as the final scheme.Given that the initial scheme is first proposed and the complexity of the annotation task in the realistic timeframe,our intraannotation procedure is well-suited.Nevertheless,the model proposed in this work is presently limited since it does not incorporate multiple annotator worldviews.This is planned as future work to produce a robust model.Practical implications:We demonstrate NLPCONTRIBUTIONGRAPH data integrated into the Open Research Knowledge Graph(ORKG),a next-generation KG-based digital library with intelligent computations enabled over structured scholarly knowledge,as a viable aid to assist researchers in their day-to-day tasks.Originality/value:NLPCONTRIBUTIONGRAPH is a novel scheme to annotate research contributions from NLP articles and integrate them in a knowledge graph,which to the best of our knowledge does not exist in the community.Furthermore,our quantitative evaluations over the two-stage annotation tasks offer insights into task difficulty.
基金This work was partially supported by the Royal Society of UK and the National Natural Science Foundation of PRC (No. 60175028).
文摘A support vector rule based method is investigated for the construction of motion controllers via natural language training. It is a two-phase process including motion control information collection from natural language instructions, and motion information condensation with the aid of support vector machine （SVM） theory. Self-organizing fuzzy neural networks are utilized for the collection of control rules, from which support vector rules are extracted to form a final controller to achieve any given control accuracy. In this way, the number of control rules is reduced, and the structure of the controller tidied, making a controller constructed using natural language training more appropriate in practice, and providing a fundamental rule base for high-level robot behavior control. Simulations and experiments on a wheeled robot are carried out to illustrate the effectiveness of the method.
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Numeral systems in natural languages show astonishing variety,though with very strong unifying tendencies that are increasing as many indigenous numeral systems disappear through language contact and globalization.Most numeral systems make use of a base,typically 10,less commonly 20,followed by a wide range of other possibilities.Higher numerals are formed from primitive lower numerals by applying the processes of addition and multiplication,in many languages also exponentiation;sometimes,however,numerals are formed from a higher numeral,using subtraction or division.Numerous complexities and idiosyncrasies are discussed,as are numeral systems that fall outside this general characterization,such as restricted numeral systems with no internal arithmetic structure,and some New Guinea extended body-part counting systems.
文摘In this research paper, we research on the automatic pattern abstraction and recognition method for large-scale database system based on natural language processing. In distributed database, through the network connection between nodes, data across different nodes and even regional distribution are well recognized. In order to reduce data redundancy and model design of the database will usually contain a lot of forms we combine the NLP theory to optimize the traditional method. The experimental analysis and simulation proves the correctness of our method.
基金This work was supported by the National Natural Science Foundation of China (No.60173066) .
文摘The expert system is an important field of the artificial intelligence. The traditional interface of the expert system is the command, menu and window at present. It limits the application of the expert system and embarrasses the enthusiasm of using expert system. Combining with the study on the expert system of network fault diagnosis, the natural language interface of the expert system has been discussed in this article. This interface can understand and generate Chinese sentences. Using this interface, the user and field experts can use the expert system to diagnose the fault of network conveniently. In the article, first, the extended production rule has been proposed. Then the methods of Chinese sentence generation from conceptual graphs and the model of expert system are introduced in detail. Using this model, the network fault diagnosis expert system and its natural language interface have been developed with Prolog.
文摘The recent advancements made in World Wide Web and social networking have eased the spread of fake news among people at a faster rate.At most of the times,the intention of fake news is to misinform the people and make manipulated societal insights.The spread of low-quality news in social networking sites has a negative influence upon people as well as the society.In order to overcome the ever-increasing dissemination of fake news,automated detection models are developed using Artificial Intelligence(AI)and Machine Learning(ML)methods.The latest advancements in Deep Learning(DL)models and complex Natural Language Processing(NLP)tasks make the former,a significant solution to achieve Fake News Detection(FND).In this background,the current study focuses on design and development of Natural Language Processing with Sea Turtle Foraging Optimizationbased Deep Learning Technique for Fake News Detection and Classification(STODL-FNDC)model.The aim of the proposed STODL-FNDC model is to discriminate fake news from legitimate news in an effectual manner.In the proposed STODL-FNDC model,the input data primarily undergoes pre-processing and Glove-based word embedding.Besides,STODL-FNDC model employs Deep Belief Network(DBN)approach for detection as well as classification of fake news.Finally,STO algorithm is utilized after adjusting the hyperparameters involved in DBN model,in an optimal manner.The novelty of the study lies in the design of STO algorithm with DBN model for FND.In order to improve the detection performance of STODL-FNDC technique,a series of simulations was carried out on benchmark datasets.The experimental outcomes established the better performance of STODL-FNDC approach over other methods with a maximum accuracy of 95.50%.
文摘Student mobility or academic mobility involves students moving between institutions during their post-secondary education,and one of the challenging tasks in this process is to assess the transfer credits to be offered to the incoming student.In general,this process involves domain experts comparing the learning outcomes of the courses,to decide on offering transfer credits to the incoming students.This manual implementation is not only labor-intensive but also influenced by undue bias and administrative complexity.The proposed research article focuses on identifying a model that exploits the advancements in the field of Natural Language Processing(NLP)to effectively automate this process.Given the unique structure,domain specificity,and complexity of learning outcomes(LOs),a need for designing a tailor-made model arises.The proposed model uses a clustering-inspired methodology based on knowledge-based semantic similarity measures to assess the taxonomic similarity of LOs and a transformer-based semantic similarity model to assess the semantic similarity of the LOs.The similarity between LOs is further aggregated to form course to course similarity.Due to the lack of quality benchmark datasets,a new benchmark dataset containing seven course-to-course similarity measures is proposed.Understanding the inherent need for flexibility in the decision-making process the aggregation part of the model offers tunable parameters to accommodate different levels of leniency.While providing an efficient model to assess the similarity between courses with existing resources,this research work also steers future research attempts to apply NLP in the field of articulation in an ideal direction by highlighting the persisting research gaps.
文摘Most software systems have different stakeholders with a variety of concerns.The process of collecting requirements from a large number of stakeholders is vital but challenging.We propose an efficient,automatic approach to collecting requirements from different stakeholders’responses to a specific question.We use natural language processing techniques to get the stakeholder response that represents most other stakeholders’responses.This study improves existing practices in three ways:Firstly,it reduces the human effort needed to collect the requirements;secondly,it reduces the time required to carry out this task with a large number of stakeholders;thirdly,it underlines the importance of using of data mining techniques in various software engineering steps.Our approach uses tokenization,stop word removal,and word lemmatization to create a list of frequently accruing words.It then creates a similarity matrix to calculate the score value for each response and selects the answer with the highest score.Our experiments show that using this approach significantly reduces the time and effort needed to collect requirements and does so with a sufficient degree of accuracy.
基金This paper is partially supported by the Natural Science Foundation of Hebei Province(No.F2015207009)the Hebei higher education research project(No.BJ2016019,QN2016179)+5 种基金Research project of Hebei University of Economics and Business(No.2016KYZ05)Education technology research Foundation of the Ministry of Education(No.2017A01020)At the same time,the paper is also supported by the National Natural Science Foundation of China under grant No.61702305the China Postdoctoral Science Foundation under grant No.2017M622234the Qingdao city Postdoctoral Researchers Applied Research Projects,University Science and Technology Program of Shandong Province under the grant No.J16LN08the Shandong Province Key Laboratory of Wisdom Mine Information Technology foundation under the grant No.WMIT201601.
文摘Natural language semantic construction improves natural language comprehension ability and analytical skills of the machine.It is the basis for realizing the information exchange in the intelligent cloud-computing environment.This paper proposes a natural language semantic construction method based on cloud database,mainly including two parts:natural language cloud database construction and natural language semantic construction.Natural Language cloud database is established on the CloudStack cloud-computing environment,which is composed by corpus,thesaurus,word vector library and ontology knowledge base.In this section,we concentrate on the pretreatment of corpus and the presentation of background knowledge ontology,and then put forward a TF-IDF and word vector distance based algorithm for duplicated webpages(TWDW).It raises the recognition efficiency of repeated web pages.The part of natural language semantic construction mainly introduces the dynamic process of semantic construction and proposes a mapping algorithm based on semantic similarity(MBSS),which is a bridge between Predicate-Argument(PA)structure and background knowledge ontology.Experiments show that compared with the relevant algorithms,the precision and recall of both algorithms we propose have been significantly improved.The work in this paper improves the understanding of natural language semantics,and provides effective data support for the natural language interaction function of the cloud service.
