Unlocking the Potential:A Comprehensive Systematic Review of ChatGPT in Natural Language Processing Tasks

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.

Comparison of R and Excel in the Field of Data Analysis

This research paper compares Excel and R language for data analysis and concludes that R language is more suitable for complex data analysis tasks.R language’s open-source nature makes it accessible to everyone,and its powerful data management and analysis tools make it suitable for handling complex data analysis tasks.It is also highly customizable,allowing users to create custom functions and packages to meet their specific needs.Additionally,R language provides high reproducibility,making it easy to replicate and verify research results,and it has excellent collaboration capabilities,enabling multiple users to work on the same project simultaneously.These advantages make R language a more suitable choice for complex data analysis tasks,particularly in scientific research and business applications.The findings of this study will help people understand that R is not just a language that can handle more data than Excel and demonstrate that r is essential to the field of data analysis.At the same time,it will also help users and organizations make informed decisions regarding their data analysis needs and software preferences.

A Brief Study of Second Language Learning Strategies From the Perspective of Error Analysis

Language teaching is not a one-way process.It interacts with language learning in an extremely intricate way.To improve language teaching,we need to take the process of language learning into account.This paper tries to explore and understand what strategies the second language learners consciously or subconsciously adopt during their language learning process through the analyses of the linguistic errors they commit,so as to provide some insights into language teaching practice.

Sentiment Analysis of Low-Resource Language Literature Using Data Processing and Deep Learning

Sentiment analysis, a crucial task in discerning emotional tones within the text, plays a pivotal role in understandingpublic opinion and user sentiment across diverse languages.While numerous scholars conduct sentiment analysisin widely spoken languages such as English, Chinese, Arabic, Roman Arabic, and more, we come to grapplingwith resource-poor languages like Urdu literature which becomes a challenge. Urdu is a uniquely crafted language,characterized by a script that amalgamates elements from diverse languages, including Arabic, Parsi, Pashtu,Turkish, Punjabi, Saraiki, and more. As Urdu literature, characterized by distinct character sets and linguisticfeatures, presents an additional hurdle due to the lack of accessible datasets, rendering sentiment analysis aformidable undertaking. The limited availability of resources has fueled increased interest among researchers,prompting a deeper exploration into Urdu sentiment analysis. This research is dedicated to Urdu languagesentiment analysis, employing sophisticated deep learning models on an extensive dataset categorized into fivelabels: Positive, Negative, Neutral, Mixed, and Ambiguous. The primary objective is to discern sentiments andemotions within the Urdu language, despite the absence of well-curated datasets. To tackle this challenge, theinitial step involves the creation of a comprehensive Urdu dataset by aggregating data from various sources such asnewspapers, articles, and socialmedia comments. Subsequent to this data collection, a thorough process of cleaningand preprocessing is implemented to ensure the quality of the data. The study leverages two well-known deeplearningmodels, namely Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), for bothtraining and evaluating sentiment analysis performance. Additionally, the study explores hyperparameter tuning tooptimize the models’ efficacy. Evaluation metrics such as precision, recall, and the F1-score are employed to assessthe effectiveness of the models. The research findings reveal that RNN surpasses CNN in Urdu sentiment analysis,gaining a significantly higher accuracy rate of 91%. This result accentuates the exceptional performance of RNN,solidifying its status as a compelling option for conducting sentiment analysis tasks in the Urdu language.

The Application and Effect Evaluation of Corpus Linguistics in English Teaching

This paper explores the application and effect evaluation of corpus linguistics in English teaching.It first introduces the basic concepts and development history of corpus linguistics,then analyzes its connection with language teaching theories,discussing the advantages and challenges of using corpora in language teaching.Next,it delves into the methods and technologies for building and utilizing corpora,as well as their role in developing teaching resources.Lastly,within the framework and methods of teaching effect evaluation,specific application cases of corpus linguistics in teaching design are analyzed,and their effects are assessed.Recommendations for teaching improvement and future development directions are also proposed.

Schisming as an Interactional Resource for Learning at the Frontiers of Work and Vocational Training-A Secondary Publication

This paper examines the sorts of interactional competencies and institutional demands required from students as they engage in complex forms of participation combining work and training purposes.It focuses on a series of empirical cases,recorded through video data and analyzed from a conversation analytic perspective,in which mentors make the decision to intervene during work sessions moderated by students.Such interventions do not interrupt the student’s activity and lead to the emergence of two distinct but not impermeable interactional spaces.This complex participation framework,known as“schisming,”contributes to overcoming practical issues within multiparty settings.Our study shows how schisming constitutes a particular sequential phenomenon where participants reorganize the interaction and co-construct a social and cognitive interactional space,thus enabling a shared understanding of the specific training context.Empirical data from the practical training of medical radiographers are used to illustrate how schisming may contribute to learning in the conditions of guided practice.

The Analysis of Lexical Approach in Language Teaching

I.IntroductionThe study of the.lexical approach focuses on the understanding ofa lexical-grammatical unit,which was called lexical phrase by Nattinger and Decarrieo and was called chunks by Michel Lewis.It is a multi-word unit of varying lengths,which has a fixed orrelatively fixed structure and expresses a certain meaning.It is prefabri-cated and frequently used.As a language teacher I think chunks arevery useful in language teaching and the lexical approach is a way of improving my teaching.They make sense in the classroom as they

A Study on the Focuses and Fronts of International Second Language Acquisition Researches -Statistical Analysis of the Articles Published in Language Learning(2012-2016)

Keywords word frequency and co-word analysis are adopted in an attempt to to investigate the focuses and fronts of inter-national second language acquisition(SLA) researches by analyzing the key words in articles published in the internationally re-nowned SLA academic journal Language Learning from 2012 to 2016. It is found that the best researched topics in SLA are vocabu-lary acquisition, explicit knowledge, form-focused teaching, language use, and so on, among which learner's language attracts themost attention. In terms of research methods, they become more diversified and interdisciplinary, as empirical studies take a domi-nant position and experiments still play a leading role, displaying an interdisciplinary feature.

Theoretical Analysis of Task-Based Language Teaching Pedagogy

Since the implementation of English class XinCheng, English teachers actively studying task-based language teaching approach, try to use task-based language teaching in the classroom teaching. This article will combine the implementation of task-based language teaching, and discussed the application of the task-based language teaching in English teaching.

Discourse Analysis of the Language Classroom in China

This study attempts to answer the following questions: What is the nature of the classroom discouse of the traditional secondary English class in China? How is it different from that in the intervention lessons when the text-based teaching method is applied? And what theories inform the current secondary English curriculum,textbook and teaching practices?

DeBERTa-GRU: Sentiment Analysis for Large Language Model

Modern technological advancements have made social media an essential component of daily life.Social media allow individuals to share thoughts,emotions,and ideas.Sentiment analysis plays the function of evaluating whether the sentiment of the text is positive,negative,neutral,or any other personal emotion to understand the sentiment context of the text.Sentiment analysis is essential in business and society because it impacts strategic decision-making.Sentiment analysis involves challenges due to lexical variation,an unlabeled dataset,and text distance correlations.The execution time increases due to the sequential processing of the sequence models.However,the calculation times for the Transformer models are reduced because of the parallel processing.This study uses a hybrid deep learning strategy to combine the strengths of the Transformer and Sequence models while ignoring their limitations.In particular,the proposed model integrates the Decoding-enhanced with Bidirectional Encoder Representations from Transformers(BERT)attention(DeBERTa)and the Gated Recurrent Unit(GRU)for sentiment analysis.Using the Decoding-enhanced BERT technique,the words are mapped into a compact,semantic word embedding space,and the Gated Recurrent Unit model can capture the distance contextual semantics correctly.The proposed hybrid model achieves F1-scores of 97%on the Twitter Large Language Model(LLM)dataset,which is much higher than the performance of new techniques.

Error Analysis of English Pronunciation Acquisition by Chinese Non-English Majors

Chinese non-English majors are a large group of English learners.In the process of English pronunciation acquisition,issues such as incomplete phonological knowledge,transfer of mother tongue,and overgeneralization,lead to confusion of phonemes and stress,misunderstanding of syllable structure,and errors of assimilation,drop,and epenthesis.The accuracy of English pronunciation can only be improved by knowing both English and Chinese phonological systems,strengthening the teaching of English phonological knowledge,and adopting various phonological training activities.

A Brief Analysis of Contrastive Analysis Hypothesis

This paper makes a brief analysis on the contrastive analysis hypothesis, and it is hoped to be helpful for the English teachers.

A Critical Discourse Analysis of Cyberbullying Language Based on Text-mining Techniques——A Case Study of Prince Harry and Meghan Markle

With the development of AI,a large amount of cyberbullying language flooded into the Internet.Cyberbullying language damages the online environment and causes mental harm to the victims of online bullying.Many researchers,therefore,have paid much attention to the problem.This study collected online comments targeted at Prince Harry and Meghan Markle as a corpus and then analyzed text data based on Critical Discourse Analysis by using text-mining tools to explore the factors that contribute to the social ideological effects of the cyberbullying language.The research results show that cultural differences,prejudice,or social exclusion due to race or gender form cyberbullying on social media.

The Effect of the Native Language and Culture on EFL Writing of Chinese Students

According to an error analysis on Chinese college students' writing,the native language and culture have great influence onsecond-language writing.On the basis of error analysis,this paper put forward some countermeasures to improve Chinese students' Eng-lish writing skill.

Groundwater quality assessment using multivariate analysis,geostatistical modeling, and water quality index(WQI): a case of study in the Boumerzoug-El Khroub valley of Northeast Algeria

In this study,the analytical data set of 26 groundwater samples from the alluvial aquifer of Boumerzoug-E1 khroub valley has been processed simultaneously with Multivariate analysis,geostatistical modeling,WQI,and geochemical modeling.Cluster analysis identified three main water types based on the major ion contents,where mineralization increased from group 1 to group 3.These groups were confirmed by FA/PCA,which demonstrated that groundwater quality is influenced by geochemical processes(water-rock interaction)and human practice(irrigation).The exponential semivariogram model WQI.Groundwater chemistry has a strong spatial structure for Mg,Na,Cl,and NO3,and a moderate spatial structure for EC,Ca,K,HCO3,and SO4.Water quality maps generated using ordinary Kriging are consistent with the HCA and PCA results.All water groups are supersaturated with respect to carbonate minerals,and dissolution of kaolinite and Ca-smectite is one of the processes responsible for hydrochemical evolution in the area.

Mother Tongue Influence on the Learning of a Foreign Language Positive and Negative Migration of Mother Tongue in the Learning Process of English

With economy increasing globalization, English as an international language of its important position can not be ignored. English teaching in China has been attached to great importance. Among the four basic skills of listening, speaking, reading and writing, writing is considered a high-level cognitive behavior, it reflects the author's way of thinking and cognitive characteristics. Chinese students learning English, will be more or less influenced by the mother tongue without doubt, this would be contrary to the requirements of Standard English. Language migration theory in the field of applied linguistics is a controversial topic. Language migration, that is, the impact of mother tongue on second language acquisition process, is a central issue in applied linguistics, language acquisition and language teaching. The research of Language transfer is an important component of the study of second language acquisition. The correct understanding to the language of the migration will help us deepen their understanding of second language acquisition. This article will focus on the phenomenon of migrations in the different languages, especially between the Chinese and English

Exploratory and Confirmatory Factor Analyses for Testing Validity and Reliability of the Malay Language Questionnaire for Urinary Incontinence Diagnosis (QUID)

This study examines the construct validity and reliability of the Malay language questionnaire for urinary incontinence diagnosis (QUID) in women. Study Design: Random sampling design was used in this cross-sectional survey. Materials and Methods: The Americanized English language questionnaire was translated to the Malay language and distributed to community-dwelling Malaysian women living in various locations in Selangor. The construct validity was tested using exploratory factor analysis (EFA) followed by confirmatory factor analysis (CFA). The reliability was determined using Cronbach’s α. Results: A total of 111 women completed the Malay language QUID in this pilot study. The Keiser-Meyer-Olkin (KMO) measure of sampling adequacy of 0.675 and Bartlett’s test of sphericity (χ2 = 284.633, df = 15, p = 0.001) indicated that the EFA was possible. The total variance and the scree plot identified two factors above the initial eigenvalue of 1 while a third factor was just below it (0.758). The CFA output showed a recursive model with the solution being not admissible because two unobserved and exogenous variables had negative variance estimates. The following values of absolute fit indices showed an acceptable level of fit: 1) Chi-square test with χ2 = 4.997, df = 5, p = 0.416, indicated a smaller difference between the expected and observed covariance matrices;2) GFI = 0.986, AGFI = 0.939, RMR = 0.021 and CMIN/DF = 1.0 indicated acceptable level of fit;3) The baseline comparison values of NFI = 0.983 and CFI = 1.0 also indicated a good fit to the data;4) RMSEA = 0.000 was considered a perfect fit indicating that the hypothesized model was a good fit to the observed data. Under the hypothesis of “close fit”, the probability of getting a sample RMSEA as large as 0.000 was 0.567. The Cronbach’s α coefficient of 0.823 indicated good reliability. Conclusion: The Malay language QUID is a valid and reliable instrument for diagnosing female urinary incontinence in the Malaysian population.