NLP Applications Research Articles

Enhancing English education with Natural Language Processing: Research and development of automated grammar checking, scoring systems, and dialogue systems

Abstract. In this paper, we investigate the scope for transformational change that these technologies offer to English education. We examine how NLP can support English education through automated grammar checking having students text checked for grammatical errors in real time, automated scoring systems which evaluate written and verbal English against predefined criteria, and dialogue systems which interacts with learners in English to help develop their speaking and listening skills in an engaging and non-judgmental environment. We document the current status of these NLP applications, examine their educational benefits, highlight some of the challenges faced by the technologies and finally discuss the way forward for large-scale adoption of such technologies, bringing the research to a logical conclusion. We hope that this study will provide a comprehensive understanding of how NLP can be employed to transform education in English and also highlight some of the associated challenges and ethical dilemmas.

Qualitative Health-Related Quality of Life and Natural Language Processing: Characteristics, Implications, and Challenges.

This article focuses on describing the main characteristics of the application of NLP in the qualitative assessment of quality of life, as well as its implications and challenges. The qualitative methodology allows analysing patient comments in unstructured free text and obtaining valuable information through manual analysis of these data. However, large amounts of data are a healthcare challenge since it would require a high number of staff and time resources that are not available in most healthcare organizations. One potential solution to mitigate the resource constraints of qualitative analysis is the use of machine learning and artificial intelligence, specifically methodologies based on natural language processing.

ChatGPT-4 extraction of heart failure symptoms and signs from electronic health records

BackgroundNatural language processing (NLP) can facilitate research utilizing data from electronic health records (EHRs). Large language models can potentially improve NLP applications leveraging EHR notes. The objective of this study was to assess the performance of zero-shot learning using Chat Generative Pre-trained Transformer 4 (ChatGPT-4) for extraction of symptoms and signs, and compare its performance to baseline machine learning and rule-based methods developed using annotated data. Methods and resultsFrom unstructured clinical notes of the national EHR data on the Veterans healthcare system, we extracted 1999 text snippets containing relevant keywords for heart failure symptoms and signs, which were then annotated by two clinicians. We also created 102 synthetic snippets that were semantically similar to snippets randomly selected from the original 1999 snippets. The authors applied zero-shot learning, using two different forms of prompt engineering in a symptom and sign extraction task with ChatGPT-4, utilizing the synthetic snippets. For comparison, baseline models using machine learning and rule-based methods were trained using the original 1999 annotated text snippets, and then used to classify the 102 synthetic snippets.The best zero-shot learning application achieved 90.6 % precision, 100 % recall, and 95 % F1 score, outperforming the best baseline method, which achieved 54.9 % precision, 82.4 % recall, and 65.5 % F1 score. Prompt style and temperature settings influenced zero-shot learning performance. ConclusionsZero-shot learning utilizing ChatGPT-4 significantly outperformed traditional machine learning and rule-based NLP. Prompt type and temperature settings affected zero-shot learning performance. These findings suggest a more efficient means of symptoms and signs extraction than traditional machine learning and rule-based methods.

A comparative study of AI code bots: Efficiency, features, and use cases

This article thus provides a comparative study of AI code bots, in terms of their performances, characteristics and applications. It looks at how via application of artificial learning and NLP for software development, these bots help in coding, debugging, and optimizing. The work also analyses AI code bots including GitHub Copilot, Tabnine, Replit Ghostwriter, Amazon CodeWhisperer, and others as well as strengths, weaknesses, and versatility of such software depending on the programming language and the development environment used. Although these tools work well in improving productivity, reducing on-task redundancies and forming a more accurate code, they do not contain self-authorization, especially for intricate occasions. The study shows that the employment of AI code bots has advantages and drawbacks that should be investigated further in the future emphasizing on the possibilities of enhancing the filters on errors, the degree of personalization and considering the ethical aspect of employing AI code generation.

Annotation of epilepsy clinic letters for natural language processing

BackgroundNatural language processing (NLP) is increasingly being used to extract structured information from unstructured text to assist clinical decision-making and aid healthcare research. The availability of expert-annotated documents for the development and validation of NLP applications is limited. We created synthetic clinical documents to address this, and to validate the Extraction of Epilepsy Clinical Text version 2 (ExECTv2) NLP pipeline.MethodsWe created 200 synthetic clinic letters based on hospital outpatient consultations with epilepsy specialists. The letters were double annotated by trained clinicians and researchers according to agreed guidelines. We used the annotation tool, Markup, with an epilepsy concept list based on the Unified Medical Language System ontology. All annotations were reviewed, and a gold standard set of annotations was agreed and used to validate the performance of ExECTv2.ResultsThe overall inter-annotator agreement (IAA) between the two sets of annotations produced a per item F1 score of 0.73. Validating ExECTv2 using the gold standard gave an overall F1 score of 0.87 per item, and 0.90 per letter.ConclusionThe synthetic letters, annotations, and annotation guidelines have been made freely available. To our knowledge, this is the first publicly available set of annotated epilepsy clinic letters and guidelines that can be used for NLP researchers with minimum epilepsy knowledge. The IAA results show that clinical text annotation tasks are difficult and require a gold standard to be arranged by researcher consensus. The results for ExECTv2, our automated epilepsy NLP pipeline, extracted detailed epilepsy information from unstructured epilepsy letters with more accuracy than human annotators, further confirming the utility of NLP for clinical and research applications.

LAAP: Learning the Argument of An Entity with Event Prompts for document-level event extraction

Document-level Event Extraction (DEE) aims to identify event types within a document and extract their corresponding arguments, which is essential for structured information provision in various NLP applications. Unlike sentence-level extraction, DEE requires handling events and arguments scattered across a document. Existing methods often focus on intricate feature interactions, neglecting explicit argument-entity relationships. We introduce a novel method, Learning the Argument of an Entity with Event Prompts (LAAP), which constructs event prompts for type detection, incorporating sentence placeholders to elicit event-specific information. Additionally, we propose an entity argument learning strategy that narrows down entity types to find the most suitable one. Experiments on the ChFinAnn and three other public datasets show that our method surpasses state-of-the-art approaches in accuracy and effectiveness.

Applications of NLP to Human Resource Management: From Word Dictionaries to Large Language Models

Applications of NLP to Human Resource Management: From Word Dictionaries to Large Language Models

Generative artificial intelligence in topic-sentiment classification for Arabic text: a comparative study with possible future directions

Social media platforms have become essential for disseminating news and expressing individual sentiments on various life topics. Arabic, widely used in the Middle East, presents unique challenges for sentiment analysis due to its complexity and multiple dialects. Motivated by the need to address these challenges, this article develops methods to overcome the lack of topic-based labeling techniques, compares different approaches for preparing extensive, annotated datasets, and analyzes the efficacy of machine learning (ML), deep learning (DL), and large language models (LLMs) in classifying Arabic textual data. Our research utilizes the topic-modeling technique to build a topic-based sentiment dataset of Arabic texts aimed at enhancing our understanding and processing capabilities. We present a comprehensive evaluation of dataset balancing techniques, including under-sampling, over-sampling, and using imbalanced datasets, providing insights into how these approaches impact classification outcomes. Additionally, we explore the influence of dataset sizes on the performance of various ML models, highlighting the importance of dataset scale in developing effective Arabic NLP applications. A further focus of our study is the comparative analysis of generative artificial intelligence (AI) models, including the emerging ChatGPT LLM, assessing their effectiveness in managing the complexities of Arabic language classification tasks. Our results show that support vector machines (SVM) achieved the highest performance, with F1-scores of 0.97 and 0.96 in classifying sentiment and topic, respectively, in Arabic tweets. This research not only benchmarks existing methodologies but also paves the way for more nuanced and robust models in the future, enhancing the application of generative AI in Arabic topic-based sentiment analysis.

PENERAPAN NEURO-LINGUISTIC PROGRAMMING BERBASIS PENDIDIKAN AKHLAK BAGI ANAK SUKU PENESAK PASCA KASUS KONFLIK SOSIAL AGRARIA

The application of NLP Based on Moral Education aims to address the psychological burden on Penesak Tribe children after agrarian social conflicts, which harbor potential emotional outbursts. The Participatory Action Research (PAR) method is used to provide significant benefits to the community and accelerate positive social transformation. This intervention strategy involves motivational training in NLP Based on Moral Education, targeting children as subjects for mental and spiritual recovery. As a result, children affected by social conflicts can practice contemplation techniques, transform themselves, and become important parts of the change process without being trapped in prolonged spiritual mental weakness. They strive to rebuild their region with the moral values embraced by the Ogan Ilir community. The Penesak Tribe children are now able to understand themselves and apply self-control techniques independently, in line with the teachings of NLP Based on Moral Education, which are highly beneficial for their mental and spiritual recovery.

A Named Entity Recognition System for the Marathi Language

Named entity recognition is a complex task in developing many NLP applications. This is one of the essential requirements of language modeling in NLP; without it, it is not possible to proceed further and achieve better results. In this proposed task, we have designed a hybrid technique that is a combination of machine learning and a rule-based approach. This system is to identify such named entities that belong under a specific class, creating a special identification and importance in the meaning generation as well as understanding of the language. This is concerned with the input text. Named entity recognition is important for different group items, such as a person’s name, location or place, animals, organization, time or date, etc. Named entities are informative and good representatives of knowledge. NE also explores the knowledge of artificial intelligence-based systems or expert systems. Using the proposed hybrid model, we have achieved 59.40% performance in identifying named entities and properly labeling for the Marathi

DL-TBAM: Deep Learning Transformer based Architecture Model for Sentiment Analysis on Tamil-English Dataset

NLP, or natural language processing, is a subfield of AI that aims to equip computers with the ability to understand and analyze human language. Sentiment analysis is a widely used application of NLP, particularly for examining attitudes expressed in online conversations. Nevertheless, many social media comments are written in languages that are not native to the authors, making sentiment analysis more difficult, especially for languages with limited resources, such as Tamil. To tackle this issue, a code-mixed and sentiment-annotated corpus in Tamil and English was created. This article will explain how the corpus was established, including the process of data collection and the assignment of polarities. The article will also explore the agreement between annotators and the results of sentiment analysis performed on the corpus. This work signifies various performance metrics such as precision, recall, support, and F1-score for the transformer-based model such as BERT, RoBerta, and XLM-RoBerta. Among the various models, XLM-Robert shows slightly significant positive results on the code-mixed corpus when compared to the state of art models.

Applications of Natural Language Processing for Automated Clinical Data Analysis in Orthopaedics.

Natural language processing is an exciting and emerging field in health care that can transform the field of orthopaedics. It can aid in the process of automated clinical data analysis, changing the way we extract data for various purposes including research and registry formation, diagnosis, and medical billing. This scoping review will look at the various applications of NLP in orthopaedics. Specific examples of NLP applications include identification of essential data elements from surgical and imaging reports, patient feedback analysis, and use of AI conversational agents for patient engagement. We will demonstrate how NLP has proven itself to be a powerful and valuable tool. Despite these potential advantages, there are drawbacks we must consider. Concerns with data quality, bias, privacy, and accessibility may stand as barriers in the way of widespread implementation of NLP technology. As natural language processing technology continues to develop, it has the potential to revolutionize orthopaedic research and clinical practices and enhance patient outcomes.

Generative adversarial networks (GANs): Introduction, Taxonomy, Variants, Limitations, and Applications

AbstractThe growing demand for applications based on Generative Adversarial Networks (GANs) has prompted substantial study and analysis in a variety of fields. GAN models have applications in NLP, architectural design, text-to-image, image-to-image, 3D object production, audio-to-image, and prediction. This technique is an important tool for both production and prediction, notably in identifying falsely created pictures, particularly in the context of face forgeries, to ensure visual integrity and security. GANs are critical in determining visual credibility in social media by identifying and assessing forgeries. As the field progresses, a variety of GAN variations arise, along with the development of diverse assessment techniques for assessing model efficacy and scope. The article provides a complete and exhaustive overview of the most recent advances in GAN model designs, the efficacy and breadth of GAN variations, GAN limits and potential solutions, and the blooming ecosystem of upcoming GAN tool domains. Additionally, it investigates key measures like as Inception Score (IS) and Fréchet Inception Distance (FID) as critical benchmarks for improving GAN performance in contrast to existing approaches.

HOP to the Next Tasks and Domains for Continual Learning in NLP

Continual Learning (CL) aims to learn a sequence of problems (i.e., tasks and domains) by transferring knowledge acquired on previous problems, whilst avoiding forgetting of past ones. Different from previous approaches which focused on CL for one NLP task or domain in a specific use-case, in this paper, we address a more general CL setting to learn from a sequence of problems in a unique framework. Our method, HOP, permits to hop across tasks and domains by addressing the CL problem along three directions: (i) we employ a set of adapters to generalize a large pre-trained model to unseen problems, (ii) we compute high-order moments over the distribution of embedded representations to distinguish independent and correlated statistics across different tasks and domains, (iii) we process this enriched information with auxiliary heads specialized for each end problem. Extensive experimental campaign on 4 NLP applications, 5 benchmarks and 2 CL setups demonstrates the effectiveness of our HOP.

Detecting Anomalies and Intrusions in Unstructured Cybersecurity Data Using Natural Language Processing

Abstract: Due to the growing volume and variety in data generated by cybersecurity systems, it is crucial that unstructured text be used for detecting anomalies. Natural language processing is a powerful tool for analyzing unstructured information and identifying threats. This paper presents a comprehensive review of NLP applications for cybersecurity. We first present the motivations for and challenges associated with using NLP to improve cybersecurity. Then, we provide background information on unstructured data that is relevant to cybersecurity, and discuss NLP techniques such as named entity recognition (NEAR), sentiment analysis, topic modelling, and document classifying. This paper focuses on how these techniques are used to detect anomalies and intrusions. We present a taxonomy for NLP-driven approaches, and we conduct a literature review that is categorized according to this taxonomy. We examine critically the strengths and weaknesses of current techniques. We highlight research gaps based on this analysis and propose a research agenda to advance NLP research in cybersecurity applications. This paper summarizes previous research and lays the foundation for using NLP to tackle cybersecurity challenges that involve unstructured data.

Advancing Legal Citation Text Classification A Conv1D-Based Approach for Multi-Class Classification

The escalating volume and intricacy of legal documents necessitate advanced techniques for automated text classification in the legal domain. Our proposed approach leverages Convolutional Neural Networks (Conv1D), a neural network architecture adept at capturing hierarchical features in sequential data. The incorporation of max-pooling facilitates the extraction of salient features, while softmax activation enables the model to handle the multi-class nature of legal citation categorization. By addressing the limitations identified in previous studies, our model aims to advance the state-of-the-art in legal citation text classification, offering a robust and efficient solution for automated categorization in the legal domain. Our research contributes to the ongoing evolution of NLP applications in the legal field, promising enhanced accuracy and adaptability in the automated analysis of legal texts.

Korean Syntactic Complexity Analyzer (KOSCA): An NLP application for the analysis of syntactic complexity in second language production

Given the lack of computational tools available for assessing second language (L2) production in Korean, this study introduces a novel automated tool called the Korean Syntactic Complexity Analyzer (KOSCA) for measuring syntactic complexity in L2 Korean production. As an open-source graphic user interface (GUI) developed in Python, KOSCA provides seven indices of syntactic complexity, including traditional and Korean-specific ones. Its validity was tested by investigating whether the syntactic complexity indices measured by it in L2 written and spoken production could explain the variability of L2 Korean learners’ proficiency. The results of mixed-effects regression analyses showed that all seven indices significantly accounted for learner proficiency in Korean. Subsequent stepwise multiple regression analyses revealed that the syntactic complexity indices explained 56.0% of the total variance in proficiency for the written data and 54.4% for the spoken data. These findings underscore the validity of the syntactic complexity indices measured by KOSCA as reliable indicators of L2 Korean proficiency, which can serve as a valuable resource for researchers and educators in the field of L2 Korean learning and assessment.

Influence of Context in Transformer-Based Medication Relation Extraction.

The extraction of medication information from unstructured clinical documents has been a major application of clinical NLP in the past decade as evidenced by the conduct of two shared tasks under the I2B2 and N2C2 umbrella. We here propose a new methodological approach which has already shown a tremendous potential for increasing system performance for general NLP tasks, but has so far not been applied to medication extraction from EHR data, namely deep learning based on transformer models. We ran experiments on established clinical data sets for English (exploiting I2B2 and N2C2 corpora) and German (based on the 3000PA corpus, a German reference data set). Our results reveal that transformer models are on a par with current state-of-the-art results for English, but yield new ones for German data. We further address the influence of context on the overall performance of transformer-based medication relation extraction.

Automation of text document classification in the budgeting phase of the Construction process: a Systematic Literature Review

PurposeThis study aims to review recent advances towards the implementation of ANN and NLP applications during the budgeting phase of the construction process. During this phase, construction companies must assess the scope of each task and map the client’s expectations to an internal database of tasks, resources and costs. Quantity surveyors carry out this assessment manually with little to no computer aid, within very austere time constraints, even though these results determine the company’s bid quality and are contractually binding.Design/methodology/approachThis paper seeks to compile applications of machine learning (ML) and natural language processing in the architectural engineering and construction sector to find which methodologies can assist this assessment. The paper carries out a systematic literature review, following the preferred reporting items for systematic reviews and meta-analyses guidelines, to survey the main scientific contributions within the topic of text classification (TC) for budgeting in construction.FindingsThis work concludes that it is necessary to develop data sets that represent the variety of tasks in construction, achieve higher accuracy algorithms, widen the scope of their application and reduce the need for expert validation of the results. Although full automation is not within reach in the short term, TC algorithms can provide helpful support tools.Originality/valueGiven the increasing interest in ML for construction and recent developments, the findings disclosed in this paper contribute to the body of knowledge, provide a more automated perspective on budgeting in construction and break ground for further implementation of text-based ML in budgeting for construction.

Development and Evaluation of Gold Standard Dataset for Sentiment Analysis of Tweets

Pre-labeled data is typically required for supervised machine learning. A limited number of object classes in the majority of open access and pre-annotated datasets make them unsuitable for certain tasks, even though they are readily available for training machine learning algorithms. For custom models, previously available pre-annotated data is typically insufficient, so gathering and preparing training data is necessary for the majority of real-world applications. The quantity and quality of annotations clearly trade-off with one another. Either more annotated data can be produced or better data quality can be guaranteed by allocating time and resources. Development of the gold standard by annotating textual information is an essential part of the “Text Analytics” domain in the field of “Natural Language Processing-NLP”. In “Text Analytics”, annotation can be done by adopting a manual, semi-automatic or automatic approach. In the case of the manual approach, annotators often work with partial parts of the corpus, and the results are generalized by automated text classification which may affect the final classification results. Annotations reliability and suitability of assigned labels are particularly important in the NLP applications related to opinion mining or sentiment analysis. In this research study, we have evaluated the significance of the annotation process on a novel dataset that contained multiple languages (English, Roman Urdu), a free text dataset that was extracted from Twitter. This unique dataset contained multiple languages which makes this annotation process essential for researching this data. Using this multi-language dataset, we examine the inter-annotator agreement in multiclass and multi-label sentiment annotation. To scrutinize the reliability of this research work, several annotation agreement metrics, statistical analysis, and Machine Learning methods have been considered to evaluate the accuracy of resulting annotations. It was observed that the annotation process is significant and a complex step that is essential for the proper implementation of Natural Language processing tasks for text analytics in machine learning. During this research, different gaps were identified and resolved which can impact the overall reliability of the annotation process which are reported in this paper. We conclude that while inaccurate annotations worsen the results, the impact is minimal, at least when using text data. The advantages of the larger annotated data set (obtained by employing subpar auto-annotation techniques) surpass the degradation resulting from the use of annotated data.