Role Of Natural Language Processing Research Articles

AI-powered language learning: The role of NLP in grammar, spelling, and pronunciation feedback

Abstract. The introduction of artificial intelligence (AI) and natural language processing (NLP) in language learning has transformed the practices of education. This paper analyzes the role of AI-powered tools in English language learning with specific focus on automated essay scoring (AES), speech recognition, and advanced grammar and spelling checkers. By examining the accuracy of the most effective AES systems, it is evident that automated feedback has increased the quality of writing by providing detailed, consistent, and objective feedback on submitted essays. Speech recognition is another tool that is being used to assess pronunciation and fluency of learners. The software is constantly improving, with advances in NLP and machine learning. The technology for fluency tests is becoming more accurate, and is helping to improve learner outcomes significantly. Lastly, we examine advanced grammar and spelling checkers, which are increasingly used for correcting the contextual errors of individual learners. The AI systems adapt to the user and their needs, which make the corrections more effective. This paper offers an overview of how AI is leading to personalized and effective language learning, ultimately enhancing the educational experience of students and embracing future possibilities in education technology.

NLP Applications in Vocabulary Acquisition

This paper examines the transformative role of Natural Language Processing (NLP) in vocabulary acquisition for English as a Second Language (ESL) learners. With advancements in NLP technologies, innovative applications have emerged that foster effective vocabulary learning through personalized and context-rich experiences. The study explores various NLP-driven tools, such as context-aware learning systems, personalized vocabulary lists, and interactive games, that enhance engagement and retention. By analyzing the effectiveness of these tools, this research underscores the limitations of traditional vocabulary teaching methods and illustrates how NLP can provide dynamic, tailored support to meet individual learners’ needs. Furthermore, the paper highlights the significance of real-time feedback mechanisms, such as speech recognition, in promoting accurate pronunciation. Ultimately, this investigation aims to demonstrate that NLP technologies offer a more engaging, efficient, and effective approach to vocabulary acquisition, significantly improving ESL learners’ language proficiency and overall communicative competence.

A comprehensive review of the application of NLP technology in language learning

The rapid development of Natural Language Processing (NLP) technology has provided new perspectives and tools for the acquisition of second languages. As our world becomes increasingly interconnected, the role of NLP in facilitating language learning has become more prominent. This paper reviews the multifaceted applications of NLP technology in language learning, including auxiliary teaching functions such as reading assistance, writing feedback, oral interaction, and personalized learning. These applications have significantly enhanced language learners' abilities in vocabulary acquisition, grammatical application, pronunciation accuracy, and reading comprehension through real-time feedback, enhanced interactivity, and promotion of cultural understanding. Despite the immense potential of NLP technology in second language learning, challenges such as technical accuracy, cultural adaptability, and data privacy exist. This paper proposes strategies, such as ensuring technical accuracy, curating diverse datasets and safeguarding data privacy to address these challenges. Looking forward, the future development of NLP technology in second language education holds great promise. As NLP continues to evolve, it is expected to contribute to more personalized, effective, and accessible language learning solutions. This paper aims to provide valuable references and insights for educators, technology developers, and policymakers, enabling them to harness the transformative potential of NLP in language education and to navigate the future of educational technology with confidence and foresight.

Natural language processing in finance: A survey

This survey presents an in-depth review of the transformative role of Natural Language Processing (NLP) in finance, highlighting its impact on ten major financial applications: (1) financial sentiment analysis, (2) financial narrative processing, (3) financial forecasting, (4) portfolio management, (5) question answering, virtual assistant and chatbot, (6) risk management, (7) regulatory compliance monitoring, (8) ESG (Environmental, Social, Governance) and sustainable finance, (9) explainable artificial intelligence (XAI) in finance and (10) NLP for digital assets. With the integration of vast amounts of unstructured financial data and advanced NLP techniques, the study explores how NLP enables data-driven decision-making and innovation in the financial sector, alongside the limitations and challenges. By providing a comprehensive analysis of NLP applications combining both academic and industrial perspectives, this study postulates the future trends and evolution of financial services. It introduces a unique review framework to understand the interaction of financial data and NLP technologies systematically and outlines the key drivers, transformations, and emerging areas in this field. This survey targets researchers, practitioners, and professionals, aiming to close their knowledge gap by highlighting the significance and future direction of NLP in enhancing financial services.

Role of Natural Language Processing in Document Understanding and Semantic Analysis: A Chinese Perspective

With the recent advancements in Natural Language Processing (NLP), there is a growing need to enhance the effectiveness and accuracy of document understanding and sentiment analysis particularly for Chinese text as it presents unique challenges of linguistics. To conduct this study, a hybrid approach was implemented which combined CNN and BiLSTM models with an ensemble voting mechanism for sentiment analysis on Chinese text. This method attained an accuracy of 97% and outperformed other techniques such as Text_CNN and AttentionBiLSTM with significant improvements in F1 score and recall. Results demonstrated a superior performance achieving 97% accuracy, along with a 95% F1 score and 97% recall. The present study extends the growing body of literature by underscoring the effectiveness of integrating BiLSTM and CNN models in sentiment analysis within the context of Chinese linguistics. It showcased enhanced document comprehension and capabilities of semantic analysis. Practically, this study provides a robust framework for leveraging BiLSTM and CNN models in the sentiment analysis of real-world. It offers significant boost in adequacy and reliability for processing Chinese text. The research limitations and future research indications have also been addressed in the study

PROCESSAMENTO DE LINGUAGEM NATURAL PARA RECONHECIMENTO DE ENTIDADES NOMEADAS EXTRAÍDAS DE CORPUS

This article presents a code implementation to automate the reading, analysis and generation of summaries of unstructured web texts, highlighting the crucial role of Natural Language Processing (NLP). The process was divided into two stages: data collection and pre-processing. In the first stage, a scientific article was selected and relevant data was extracted via web scraping, organized into an HTML page hosted online. In pre-processing, tokenization, normalization, stopword removal and word counting were performed. Using NLTK, the most important sentences were identified and ranked based on keyword frequency, allowing the selection of the most relevant sentences from the introduction, methodology, results/dicussion and conclusion sections. Discrepancies such as links and figure descriptions have been removed to improve the clarity of the abstract. The “most_common()” method was used to select the most relevant words in each section. After additional processing to delete unnecessary words, the final summary was clear and understandable, despite minor flaws. This automated approach provides an efficient way to synthesize information from scientific articles, optimizing the textual analysis process.

The role of artificial intelligence in auditing and fraud detection in accounting information systems: moderating role of natural language processing

Purpose This study aims to investigate the moderating role of natural language processing natural language processing (NLP) on the relationship between AI-empowered AIS (data gathering, data analysis, risk assessment, detection, prevention and Investigation) and auditing and fraud detection. Design/methodology/approach Quantitative methodology was adapted through a questionnaire. In total, 221 individuals represented the population of the study, and SPSS was used to screen primary data. The study indicated the acceptance of the hypothesis that “Artificial Intelligence in AIS has a statistically significant influence on auditing and fraud detection,” showing a strong correlation between auditing and fraud detection. The study concluded that NLP moderates the relationship between AI in AIS and auditing and fraud detection. Findings The study’s implications lie in its contribution to the development of theoretical models that explore the complementary attributes of AI and NLP in detecting financial fraud. Research limitations/implications A cross-sectional design is a limitation. Practical implications NLP is a useful tool for developing more efficient methods for detecting fraudulent activities and audit risks. Originality/value The study’s originality stems from its focus on the use of AI-empowered AIS, a relatively new technology that has the potential to significantly impact auditing and fraud detection processes within the accounting field.

Implementation and evaluation of an additional GPT-4-based reviewer in PRISMA-based medical systematic literature reviews

BackgroundPRISMA-based literature reviews require meticulous scrutiny of extensive textual data by multiple reviewers, which is associated with considerable human effort. ObjectiveTo evaluate feasibility and reliability of using GPT-4 API as a complementary reviewer in systematic literature reviews based on the PRISMA framework. MethodologyA systematic literature review on the role of natural language processing and Large Language Models (LLMs) in automatic patient-trial matching was conducted using human reviewers and an AI-based reviewer (GPT-4 API). A RAG methodology with LangChain integration was used to process full-text articles. Agreement levels between two human reviewers and GPT-4 API for abstract screening and between a single reviewer and GPT-4 API for full-text parameter extraction were evaluated. ResultsAn almost perfect GPT–human reviewer agreement in the abstract screening process (Cohen’s kappa > 0.9) and a lower agreement in the full-text parameter extraction were observed. ConclusionAs GPT-4 has performed on a par with human reviewers in abstract screening, we conclude that GPT-4 has an exciting potential of being used as a main screening tool for systematic literature reviews, replacing at least one of the human reviewers.

Revolutionizing Healthcare: NLP, Deep Learning, and WSN Solutions for Managing the COVID-19 Crisis

The COVID-19 outbreak in 2020 catalyzed a global socio-economic upheaval, compelling nations to embrace digital technologies as a means of countering economic downturns and ensuring efficient communication systems. This paper delves into the role of Natural Language Processing (NLP) in harnessing wireless connectivity during the pandemic. The examination assesses how wireless networks have affected various facets of crisis management, including virus tracking, optimizing healthcare, facilitating remote education, and enabling unified communications. Additionally, the article underscores the importance of digital inclusion in mitigating disease outbreaks and reconnecting marginalized communities. To address these challenges, a Dual CNN-based BERT model is proposed. BERT model is used to extract the text features, the internal layers of BERT excel at capturing intricate contextual details concerning words and phrases, rendering them highly valuable as features for a wide array of text analysis tasks. The significance of dual CNN is capturing the unique capability to seamlessly integrate both character-level and word-level information. This fusion of insights from different levels of textual analysis proves especially valuable in handling text data that is noisy, complex, or presents challenges related to misspellings and domain-specific terminology. The proposed model is evaluated using the simulated WSN-based text data for crisis management.

Demystifying the Role of Natural Language Processing (NLP) in Smart City Applications: Background, Motivation, Recent Advances, and Future Research Directions.

Smart cities provide an efficient infrastructure for the enhancement of the quality of life of the people by aiding in fast urbanization and resource management through sustainable and scalable innovative solutions. The penetration of Information and Communication Technology (ICT) in smart cities has been a major contributor to keeping up with the agility and pace of their development. In this paper, we have explored Natural Language Processing (NLP) which is one such technical discipline that has great potential in optimizing ICT processes and has so far been kept away from the limelight. Through this study, we have established the various roles that NLP plays in building smart cities after thoroughly analyzing its architecture, background, and scope. Subsequently, we present a detailed description of NLP's recent applications in the domain of smart healthcare, smart business, and industry, smart community, smart media, smart research, and development as well as smart education accompanied by NLP's open challenges at the very end. This work aims to throw light on the potential of NLP as one of the pillars in assisting the technical advancement and realization of smart cities.

Muhasebe ve Denetimde Doğal Dil İşlemenin (NLP) Yeri (The Role of Natural Language Processing (NLP) in Accounting and Auditing)

Bu çalışma, yapay zekâteknolojisi tekniklerinden biri olan doğal dil işlemeninmuhasebe ve denetimde kullanımının mümkün olup-olamayacağı noktasından hareketle ortaya çıkmıştır. Bu doğrultuda çalışmada doğal dil işleme süreci ayrıntılarıyla açıklanmıştır. Doğal dil işlemenin kullanılmış olduğu uygulama örnekleri ve konuyla ilgili literatür kapsamlı bir şekilde incelenmiş ve sunulmuştur. Araştırma sonuçlarından ve doğal dil işlemenin uygulanabilme kapasitesinden yola çıkarak muhasebe ve denetim alanına yönelik öneriler sunulmuştur. Çalışma, yazılımcılar için yeni bir sektör alternatifi olmasının yanında muhasebe uygulayıcılarına, denetim firmalarınave işletmelere de farklı bir bakış açısı sunmaktadır.

The Role of Natural Language Processing during the COVID-19 Pandemic: Health Applications, Opportunities, and Challenges.

The COVID-19 pandemic is the most devastating public health crisis in at least a century and has affected the lives of billions of people worldwide in unprecedented ways. Compared to pandemics of this scale in the past, societies are now equipped with advanced technologies that can mitigate the impacts of pandemics if utilized appropriately. However, opportunities are currently not fully utilized, particularly at the intersection of data science and health. Health-related big data and technological advances have the potential to significantly aid the fight against such pandemics, including the current pandemic's ongoing and long-term impacts. Specifically, the field of natural language processing (NLP) has enormous potential at a time when vast amounts of text-based data are continuously generated from a multitude of sources, such as health/hospital systems, published medical literature, and social media. Effectively mitigating the impacts of the pandemic requires tackling challenges associated with the application and deployment of NLP systems. In this paper, we review the applications of NLP to address diverse aspects of the COVID-19 pandemic. We outline key NLP-related advances on a chosen set of topics reported in the literature and discuss the opportunities and challenges associated with applying NLP during the current pandemic and future ones. These opportunities and challenges can guide future research aimed at improving the current health and social response systems and pandemic preparedness.

Natural Language Processing: from Bedside to Everywhere.

Owing to the rapid progress of natural language processing (NLP), the role of NLP in the medical field has radically gained considerable attention from both NLP and medical informatics. Although numerous medical NLP papers are published annually, there is still a gap between basic NLP research and practical product development. This gap raises questions, such as what has medical NLP achieved in each medical field, and what is the burden for the practical use of NLP? This paper aims to clarify the above questions. We explore the literature on potential NLP products/services applied to various medical/clinical/healthcare areas. This paper introduces clinical applications (bedside applications), in which we introduce the use of NLP for each clinical department, internal medicine, pre-surgery, post-surgery, oncology, radiology, pathology, psychiatry, rehabilitation, obstetrics, and gynecology. Also, we clarify technical problems to be addressed for encouraging bedside applications based on NLP. These results contribute to discussions regarding potentially feasible NLP applications and highlight research gaps for future studies.

Utilizing Natural Language Processing (NLP) to identify breast cancer associated-lung metastases from pathology reports to delineate characteristics and challenges of this common site of breast cancer recurrence.

e13592 Background: NLP (artificial intelligence) can automate the identification of records in large datasets. The purpose of this study was to evaluate the feasibility of NLP to identify breast cancer-associated lung metastases to understand clinical characteristics and challenges posed by this common site of breast cancer recurrence. Methods: Patients with pathologically confirmed breast cancer associated-lung metastases seen at a large academic center between 3/2012-5/2019 were identified using NLP of institutional pathology reports, with an IRB approved protocol. Chart review was performed to confirm breast cancer associated-lung metastases and determine clinical and pathological features. Results: Using NLP, 32 patients with pathology reports denoting breast cancer associated-lung metastases were identified, with pathologic confirmation of lung biopsy tissue in the majority of cases (24), and pleural fluid specimens (8) on the remainder. Ten of 32 (31%) were HR+/HER2-, 3/32 (9.3%) HER2+, and 19/32 (59%) TNBC. The majority were invasive ductal carcinoma (21/26) with the remainder invasive lobular carcinoma (2/26) or mixed histology (3/26). Median age at lung metastasis diagnosis was 62 years (range 31-88). The median time to development of lung metastasis following primary breast cancer was 5.6 years (range 0-24.8 years). Fifty six percent of lung metastases were detected on imaging and 44% by symptoms including dyspnea, cough, or pain. Tumor tissue genotyping results on the lung metastases were available for 8 patients showing PI3KCA (5), TP53 (3), SMARCA4 (2), ERBB2 (1), FGFR3 (1), ATM (1), CDK4 (1), MYC (1), and ESR1 (1). Treatment after diagnosis of lung metastases included hormone therapy (61%), chemotherapy (84%), lung irradiation (26%), and surgical resection of lung metastases (6%). Lung metastases were associated with considerable morbidity including pleural effusion (15%), dyspnea (6%), pneumothorax (3%), hemothorax (3%), and atelectasis (3%). Patients diagnosed with lung metastases had brain (32%), bone (35%), renal (6%), skin (3%) and adrenal (3%) metastases during disease course. Conclusions: NLP can help identify organ specific metastases from pathology reports, such as breast cancer associated-lung metastases, which can then facilitate observational, translational, and clinical research to characterize and address challenges posed by this common site of breast cancer recurrence. This cohort of patients highlights the morbidity of breast cancer associated-lung metastases and potential role of NLP for disease characterization and clinical research. (Support from ASCO Medical Student Rotation for Underrepresented Populations Award.)

WITHDRAWN: A study on the role of natural language processing in the healthcare sector

Abstract During the COVID-19 pandemic, Natural Language Processing (NLP) plays a vital role in detecting the disease, stabilizing the intensive care, discovering the drugs and controlling the spreading of infections. It energizes chat programs to assist in the reduction of outbreaks during the initial stages of coronavirus infection. NLP technologies have reached new heights in terms of utility, and are at the heart of the success of a multilingual conversation system, Chatbots, and Deep learning language models. It supports a total of 13 languages around the world. NLP powered AI such as Health map and Cobweb platforms track patient requests and perform incident detections. This chapter looks at the role of NLP and its technologies, challenges, and future possibilities using AI and machine learning for crisis mitigation and easier EHRs in the health care industry.

Identify Patients with Congestive Heart Failure through Analyzing Free-Text Clinical Notes

IntroductionA number of challenges exist in analyzing unstructured free text data in electronic medical records (EMRs). EMR text are difficult to represent and model due to their high dimensionality, heterogeneity, sparsity, incompleteness, random errors and the presence of noise.
 Objectives and ApproachStandard Natural Language Processing (NLP) tools make errors when applied to clinical notes due to physician use of unconventional language, involving polysemy, abbreviations, ambiguity, misspelling, variations, and negation.
 This paper presents a novel NLP framework, “Clinical Learning On Natural Expression” (CLONE), to automatically learn from a large primary care EMR database, analyzing free text clinical notes from primary care practices. CLONE’s predictive clinical models using text mining and neural network approach to extract features to identify patterns. To demonstrate effectiveness, we evaluate CLONE’s ability in a case study to identify patients with a specific chronic condition: congestive heart failure (CHF).
 ResultsA random selected sample of 7500 patients from Electronic Medical Record Administrative data Linked Database (EMRALD) is used. In this dataset, each patient’s medical chart includes a reference standard, manually reviewed by medical practitioners. Prevalence of CHF is approximately 2%. The low prevalence leads to another challenging problem in machine learning: imbalanced datasets. After pre-processing, we build deep learning models to represent and extract important medical information from free text to identify CHF patients through analyzing patient charts. We evaluated the effectiveness of CLONE by comparing the predicted labels with the standard references on a holdout test dataset. Comparing it with a number of alternative algorithms, we improve the overall accuracy to over 90% on a test dataset.
 Conclusion/ImplicationsAs the role of NLP in EMR data expands, the CLONE natural language processing framework can lead to substantial reduction in manual processing, while improving predictive accuracy.

Role of Natural Language Processing in Social Media

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

Role of NLP in Indian Regional Languages

The NLP is closer for interfacing among the peoples knowing different languages. If we consider an example of India there are various peoples talking in various languages. Huge literature is available in different local languages which is not understandable to others in India itself. So we can use Information technology for Natural Language Processing. Natural language processing (NLP) is a field of computer science and linguistics concerned with the interactions between computers and human (natural) languages; it began as a branch of artificial intelligence. In theory, natural language processing is a very attractive method of human-computer interaction. Natural language understanding is sometimes referred to as an AI-complete problem because it seems to require extensive knowledge about the outside world and the ability to manipulate it. Modern NLP algorithms are grounded in machine learning, especially statistical machine learning. Research into modern statistical NLP algorithms requires an understanding of a number of disparate fields, including linguistics, computer science, and statistics. In this paper we want to study on Role of NLP for Indian Language conversions, like Marathi to Hindi, Hindi to Gujarati etc. If we observe the different languages in India they look similar in different aspects like Grammar, Words, and Alphabets. This paper will discuss the solutions available, problems and challenges in Indian Language conversions.

Differentiation of ileostomy from colostomy procedures: Assessing the accuracy of current procedural terminology codes and the utility of natural language processing

Large databases provide a wealth of information for researchers, but identifying patient cohorts often relies on the use of current procedural terminology (CPT) codes. In particular, studies of stoma surgery have been limited by the accuracy of CPT codes in identifying and differentiating ileostomy procedures from colostomy procedures. It is important to make this distinction because the prevalence of complications associated with stoma formation and reversal differ dramatically between types of stoma. Natural language processing (NLP) is a process that allows text-based searching. The Automated Retrieval Console is an NLP-based software that allows investigators to design and perform NLP-assisted document classification. In this study, we evaluated the role of CPT codes and NLP in differentiating ileostomy from colostomy procedures. Using CPT codes, we conducted a retrospective study that identified all patients undergoing a stoma-related procedure at a single institution between January 2005 and December 2011. All operative reports during this time were reviewed manually to abstract the following variables: formation or reversal and ileostomy or colostomy. Sensitivity and specificity for validation of the CPT codes against the mastery surgery schedule were calculated. Operative reports were evaluated by use of NLP to differentiate ileostomy- from colostomy-related procedures. Sensitivity and specificity for identifying patients with ileostomy or colostomy procedures were calculated for CPT codes and NLP for the entire cohort. CPT codes performed well in identifying stoma procedures (sensitivity 87.4%, specificity 97.5%). A total of 664 stoma procedures were identified by CPT codes between 2005 and 2011. The CPT codes were adequate in identifying stoma formation (sensitivity 97.7%, specificity 72.4%) and stoma reversal (sensitivity 74.1%, specificity 98.7%), but they were inadequate in identifying ileostomy (sensitivity 35.0%, specificity 88.1%) and colostomy (75.2% and 80.9%). NLP performed with greater sensitivity, specificity, and accuracy than CPT codes in identifying stoma procedures and stoma types. Major differences where NLP outperformed CPT included identifying ileostomy (specificity 95.8%, sensitivity 88.3%, and accuracy 91.5%) and colostomy (97.6%, 90.5%, and 92.8%, respectively). CPT codes can identify effectively patients who have had stoma procedures and are adequate in distinguishing between formation and reversal; however, CPT codes cannot differentiate ileostomy from colostomy. NLP can be used to differentiate between ileostomy- and colostomy-related procedures. The role of NLP in conjunction with electronic medical records in data retrieval warrants further investigation.

Realizing the full potential of electronic health records: the role of natural language processing

Meaningful use of electronic health records (EHRs) for patient care or for research requires data to be comparable. Many portions of EHRs continue to be unstructured, presenting significant challenges for biomedical informatics. This issue of the journal displays several solutions to this problem that are based on natural language processing (NLP) techniques. A high-level review by Nadkarni ( see page 544 ) is intended to introduce the main components of NLP for the novice, and to briefly describe machine learning methods that are successfully being employed in the field. It includes a discussion on Watson, a contestant on ‘Jeopardy!,’ a popular question-and-answer TV show, and the ensuing speculations about its potential extensions to medical NLP. However, despite some notable examples of successful NLP applications in clinical care, progress in the field has been relatively slow. Chapman and colleagues ( see page 540 ) discuss the need to steer current NLP research efforts so that new developments can be accelerated, and research products can become readily usable in healthcare applications. The authors advocate for a concerted, collaborative effort to develop open-source software components and infrastructure to share annotated data and tools. The guest editorial raises important questions …