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Aspirin-exacerbated respiratory disease (AERD) is an acquired inflammatory condition characterized by the presence of asthma, chronic rhinosinusitis with nasal polyposis, and respiratory hypersensitivity reactions on ingestion of aspirin or other nonsteroidal anti-inflammatory drugs (NSAIDs). Despite AERD having a classic constellation of symptoms, the diagnosis is often overlooked, with an average of greater than 10 years between the onset of symptoms and diagnosis of AERD. Without a diagnosis, individuals will lack opportunities to receive effective treatments, such as aspirin desensitization or biologic medications. Our aim was to develop a combined algorithm that integrates both natural language processing (NLP) and machine learning (ML) techniques to identify patients with AERD from an electronic health record (EHR). A rule-based decision tree algorithm incorporating NLP-based features was developed using clinical documents from the EHR at Mayo Clinic. From clinical notes, using NLP techniques, 7 features were extracted that included the following: AERD, asthma, NSAID allergy, nasal polyps, chronic sinusitis, elevated urine leukotriene E4 level, and documented no-NSAID allergy. MedTagger was used to extract these 7 features from the unstructured clinical text given a set of keywords and patterns based on the chart review of 2 allergy and immunology experts for AERD. The status of each extracted feature was quantified by assigning the frequency of its occurrence in clinical documents per subject. We optimized the decision tree classifier's hyperparameters cutoff threshold on the training set to determine the representative feature combination to discriminate AERD. We then evaluated the resulting model on the test set. The AERD algorithm, which combines NLP and ML techniques, achieved an area under the receiver operating characteristic curve score, sensitivity, and specificity of 0.86 (95% CI 0.78-0.94), 80.00 (95% CI 70.82-87.33), and 88.00 (95% CI 79.98-93.64) for the test set, respectively. We developed a promising AERD algorithm that needs further refinement to improve AERD diagnosis. Continued development of NLP and ML technologies has the potential to reduce diagnostic delays for AERD and improve the health of our patients.

1 IntroductionOrganizations focus on automate their processes in order to improve efficiency, reduce costs, and/or reduce human beings' errors in an easy and rapid manner. Business process management (BPM) methods provide a solution to this issue. In this context, information systems like CRM, ERP, SCM, etc. have known an increasing demand. The main problem consists of the length of the business process specifications. If new regulations appear, these specifications must be adapted. Manually extraction of visual models is time consuming. During time, a series of solutions were proposed. The literature shows a crowd of approaches that extracts data models [1], [2], [3], and process models [4], [5] from Natural Language (NL) text. This paper aims to analyze the Natural Language Processing (NLP) techniques and tools used in order to provide different types of visual representations.On the last few years, approaches based on NLP have been developed in order to automate process conversion from NL text. NLP plays an important role in NL text analysis as NLP tries to understand speech and text as humans beings would do. Colloquialism, abbreviations or typos make this task a challenging one. NLP has the origins in 1950s when Alan Turing proposed the Turing test [6], by introducing the imitation game. Since then, the literature shows a plethora of NLP tools [7], [8], [9], [10] using several machine learning techniques, our focus being on those applied on data models and process models discovery from NL text, starting with the first language parser [11] to the actual ones like NLTk [7], [8], ANTLR1, etc.Linguistic analysis is closely tied to NLP. Liddy [12] highlights 7 levels of linguistic analysis: a) Phonetic or Phonological level: how words are pronounced, b) Morphological level: prefixes, suffixes and roots analysis, c) Lexical level: word level analysis including lexical meaning and Part-Of-Speech (POS) analysis, d) Syntactic level: grammatical analysis of words in a sentence, e) Semantic level: determining the possible meanings of sentences, f) Discourse level: interpreting structure and meaning for texts larger than a sentence, g) Pragmatic level: understanding the purpose of a language.Some of the problems approached by NLP are: POS tagging, parsing, Named Entity Recognition (NER), chunking, Semantic Role Labeling (SRL). Anaphora resolution [13] refers to the interpretation of the link between the anaphor and its antecedents.The remainder of the paper is organized as follows: Section 2 briefly outlines the NLP domain, describing the levels of linguistic analysis and the main NLP approaches of analyzing NL requirements. Section 3 focuses on data and process models extraction from NL text. This section makes an introduction to Object Oriented Analysis and Business Process Modeling and analyses the existing tools that discover data and process models from text. Subsequently, Section 4 summarizes the results of this work and the conclusions are drawn in Section 5.2NLPA detailed review on NLP is given in [14] and in [15]. Jones [14] divides the history of NLP into four phases: the first starts at the beginning of the 1940s and lasts to the late 1960s, the second begins from the end of 60s and lasts to the end of 70s, the third is represented by late 80s, where the fourth phase starts in the late of 90s. Next, we will detail each phase as they were defined in [14] and [15]. First phase treated machine translation issues, while the second focused on artificial intelligence. The third phase can be called grammatico-logical phase, which is followed by the lexical phase. A fifth phase is proposed in [16] where formal theories and statistical data are combined. Since this study was published first in 1994 and then it was re-organized in 2001 we can add the sixth phase: from 2000 until present where NLP techniques are combined in order to contribute to visual models extraction.Software requirements are usually written in NL which is asymmetric and irregular [17]. …

Task-oriented dialogue systems employ third-party APIs to serve end-users via natural language interactions. while existing advances in Natural Language Processing (NLP) and Machine Learning (ML) techniques have produced promising and useful results to recognize user intents, the synthesis of API calls to support a broad range of potentially complex user intents is still largely a manual and costly process. In this paper, we propose a new approach to recognize and realize complex user intents. Our approach relies on a new rule-based technique that leverages both (i) natural language features extracted using existing NLP and ML techniques and (ii) contextual knowledge to capture the different classes of complex intents. We devise a context knowledge service to capture the requisite contextual knowledge.KeywordsTask-oriented conversational botsComplex intent recognitionContext knowledgeSlot value inference

Abstract: Artificial intelligence (AI), which has sped up the creation of intelligent conversational systems, or chatbots, has totally changed how people interact with computers. This investigation looks at how AI-powered chatbots are developed, designed, and used in a variety of sectors, such as customer service, healthcare, education, and e-commerce. It highlights how natural language processing (NLP), machine learning (ML), and deep learning techniques are combined to assist chatbots in understanding, interpreting, and effectively responding to consumer requests. The research examines typical frameworks and platforms for chatbot development in addition to language comprehension, emotional intelligence, privacy, and user engagement challenges. Furthermore, the effectiveness and adaptability of chatbots have significantly increased due to recent advancements in generative AI models, such as GPT and BERT. The final section of this paper examines future research directions, emphasizing AI chatbots' potential to create conversational agents that are more human-like, contextually aware, and emotionally intelligent. With the creation of intelligent chatbots that can comprehend and react to natural language, artificial intelligence (AI) has completely changed human–computer interaction. The design and deployment of an AI-based chatbot that mimics human speech in order to deliver prompt, precise, and context-aware responses is the main emphasis of this study. The chatbot interprets user inquiries, extracts pertinent data, and provides insightful responses in real time by utilizing Natural Language Processing (NLP) and Machine Learning (ML) techniques. The solution improves user engagement and conversational accuracy by incorporating deep learning models for entity recognition and intent detection. Additionally, the chatbot's design includes modules for response generation, dialogue management, and data preparation to guarantee dependability and flexibility across a range of disciplines, especially in healthcare applications..

Skills are the common ground between employers, job seekers and educational institutions which can be analyzed with the help of natural language processing (NLP) techniques. In this paper we explore a state-of-the-art pipeline that extracts, vectorizes, clusters, and compares skills to provide recommendations for all three parties—thereby bridging the gap between employers, job seekers and educational institutions. Our best system combines Sentence-BERT [1], UMAP [2], DBSCAN [3], and K-means clustering [4].KeywordsAI in educationRecommender systemRecommendation systemUp-skillingNatural language processing

The banking industry's growing need for efficient and personalized customer service has driven the development of Chatbot technology. Our project proposes the creation of an intelligent Bank Chatbot, leveraging Natural Language Processing (NLP) and Machine Learning (ML) techniques to offer tailored customer interactions. This Chatbot will adeptly handle customer inquiries, including account balance, transaction history, and account details, in a conversational and user-friendly manner. We will source data from open platforms like GitHub or Kaggle to train the Chatbot. Utilizing cutting-edge NLP for text processing and ML models like Support Vector Machine (SVM) and Naive Bayes (NB) Classifier, the Chatbot aims to deliver optimal results. The preliminary findings indicate that Naive Bayes, with an impressive accuracy of 90.6%, outperforms SVM, which has an accuracy of 76.2%. This showcases the promising potential of integrating chatbots into the banking sector, enhancing the system's ability to comprehend and efficiently address customer queries. In the subsequent phases, we will connect this system to a server, creating a user-friendly front-end application that empowers both customers and banks with convenient interactions.

Recruitment fraud has emerged as a serious global concern with the rapid growth of online job portals, social media hiring, and remote recruitment practices. Fraudulent recruiters exploit job seekers through fake job offers, phishing emails, counterfeit interviews, and financial scams, causing financial loss and psychological distress. Traditional rule-based and manual verification mechanisms are insufficient to detect evolving fraud patterns in real time. This paper presents HireForensics, an intelligent recruitment fraud detection system that leverages machine learning and natural language processing (NLP) techniques to identify deceptive patterns in recruitment ecosystems. The proposed system analyzes job descriptions, recruiter messages, communication behavior, and metadata to classify recruitment activities as legitimate or fraudulent. Multiple machine learning models including Logistic Regression, Random Forest, Support Vector Machines, and ensemble classifiers are implemented and evaluated. Experimental results demonstrate that the proposed model achieves high accuracy and robustness, making HireSafe AI a scalable, cost-effective, and reliable solution for safeguarding job seekers and recruitment platforms. Keywords Natural Language Processing (NLP), Random Forest, Ensemble Learning, Heuristic Analysis, TF-IDF, Logistic Regression

Mental health disorders represent a significant global health burden, often characterized by late diagnoses and inconsistent treatment outcomes. The integration of artificial intelligence (AI) with electronic health records (EHRs) offers a transformative approach to early diagnosis and intervention. This study presents the development of an AI-powered predictive model designed to enhance the diagnosis of mental health disorders by leveraging structured and unstructured data from EHRs. The model integrates natural language processing (NLP), machine learning (ML), and deep learning techniques to analyze clinical notes, patient histories, demographic data, and behavioral indicators. Our methodology involves preprocessing EHR datasets, extracting relevant features, and applying advanced algorithms such as random forests, support vector machines (SVM), and neural networks to build robust predictive models. Additionally, NLP techniques are used to process narrative text data, identifying critical indicators of mental health conditions such as depression, anxiety, bipolar disorder, and schizophrenia. The model is trained and validated using a large, anonymized EHR dataset, ensuring a high level of accuracy, precision, and recall in identifying at-risk individuals. Preliminary results demonstrate the model’s ability to outperform traditional diagnostic methods by identifying subtle patterns and risk factors often overlooked in standard clinical evaluations. Moreover, the integration of interpretability tools such as SHAP (SHapley Additive exPlanations) enables clinicians to understand the rationale behind each prediction, promoting trust and clinical applicability. This research underscores the potential of AI-driven tools in revolutionizing mental healthcare by enabling timely diagnosis, personalized treatment planning, and improved patient outcomes. However, the study also highlights challenges such as data quality, privacy concerns, algorithmic bias, and the need for interdisciplinary collaboration in deploying these systems responsibly. In conclusion, the proposed AI-powered predictive model demonstrates promise in augmenting mental health diagnostics using EHR data, paving the way for scalable and proactive mental healthcare delivery.

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Context:</i> User stories have been widely accepted as artifacts to capture the user requirements in agile software development. They are short pieces of texts in a semi-structured format that express requirements. Natural language processing (NLP) techniques offer a potential advantage in user story applications. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective:</i> Conduct a systematic literature review to capture the current state-of-the-art of NLP research on user stories. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Method:</i> The search strategy is used to obtain relevant papers from SCOPUS, ScienceDirect, IEEE Xplore, ACM Digital Library, SpringerLink, and Google Scholar. Inclusion and exclusion criteria are applied to filter the search results. We also use the forward and backward snowballing techniques to obtain more comprehensive results. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results:</i> The search results identified 718 papers published between January 2009 to December 2020. After applying the inclusion/exclusion criteria and the snowballing technique, we identified 38 primary studies that discuss NLP techniques in user stories. Most studies used NLP techniques to extract aspects of who, what, and why from user stories. The purpose of NLP studies in user stories is broad, ranging from discovering defects, generating software artifacts, identifying the key abstraction of user stories, and tracing links between model and user stories. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Conclusion:</i> NLP can help system analysts manage user stories. Implementing NLP in user stories has many opportunities and challenges. Considering the exploration of NLP techniques and rigorous evaluation methods is required to obtain quality research. As with NLP research in general, the ability to understand a sentence’s context continues to be a challenge.

The development of computational data science techniques in natural language processing (NLP) and machine learning (ML) algorithms to analyze large and complex textual information opens new avenues to study intricate policy processes at a scale unimaginable even a few years ago. We apply these scalable NLP and ML techniques to analyze the United States Government’s regulation of the banking and financial services sector. First, we employ NLP techniques to convert the text of financial regulation laws into feature vectors and infer representative “topics” across all the laws. Second, we apply ML algorithms to the feature vectors to predict various attributes of each law, focusing on the amount of authority delegated to regulators. Lastly, we compare the power of alternative models in predicting regulators’ discretion to oversee financial markets. These methods allow us to efficiently process large amounts of documents and represent the text of the laws in feature vectors, taking into account words, phrases, syntax, and semantics. The vectors can be paired with predefined policy features, thereby enabling us to build better predictive measures of financial sector regulation. The analysis offers policymakers and the business community alike a tool to automatically score policy features of financial regulation laws to and measure their impact on market performance.

The inspection of documents written in natural language with computers has become feasible thanks to the advances in Natural Language Processing (NLP) techniques. However, certain applications require a deeper semantic analysis of the text to produce good results. In this article, we present an exploratory study of semantic-aware NLP techniques for discovering latent concerns in use case specifications. For this purpose, we propose two NLP techniques, namely: semantic clustering and semantically-enriched rules. After evaluating these two techniques and comparing them with a technique developed by other researchers, results have showed that semantic NLP techniques hold great potential for detecting candidate concerns. Particularly, if these techniques are properly configured, they can help to reduce the efforts of requirement analysts and promote better quality in software development.

In the era of innovation and entrepreneurship, evaluating the feasibility, risk, and market potential of a business idea before investment has become a critical necessity. The “Business Idea Analyzer using NLP and Clustering” is an AI-based web application designed to assist entrepreneurs, students, and startups in assessing the viability of their business ideas using Natural Language Processing (NLP) and Machine Learning (ML) techniques. The system is implemented using the Flask framework in Python, providing a user-friendly interface for idea submission and result visualization.This project aims to enhance startup idea validation by leveraging two prominent artificial intelligence techniques: Natural Language Processing (NLP) and clustering algorithms. Business planning is a critical phase in entrepreneurship that requires data-driven validation to ensure effective decision-making. By applying these techniques to a comprehensive set of user inputs and real-time data, the project seeks to assess the feasibility of a startup idea and suggest improvements or alternatives. Additionally, the project utilizes the Geoapify API to perform competitive market analysis by locating nearby competitors based on the user’s selected location and business type. This real-time competitor mapping feature empowers entrepreneurs with data-driven decision-making to choose the right market area for launching their products or services

While radiologists regularly issue follow-up recommendations, our preliminary research has shown that anywhere from 35 to 50% of patients who receive follow-up recommendations for findings of possible cancer on abdominopelvic imaging do not return for follow-up. As such, they remain at risk for adverse outcomes related to missed or delayed cancer diagnosis. In this study, we develop an algorithm to automatically detect free text radiology reports that have a follow-up recommendation using natural language processing (NLP) techniques and machine learning models. The data set used in this study consists of 6000 free text reports from the author's institution. NLP techniques are used to engineer 1500 features, which include the most informative unigrams, bigrams, and trigrams in the training corpus after performing tokenization and Porter stemming. On this data set, we train naive Bayes, decision tree, and maximum entropy models. The decision tree model, with an F1 score of 0.458 and accuracy of 0.862, outperforms both the naive Bayes (F1 score of 0.381) and maximum entropy (F1 score of 0.387) models. The models were analyzed to determine predictive features, with term frequency of n-grams such as "renal neoplasm" and "evalu with enhanc" being most predictive of a follow-up recommendation. Key to maximizing performance was feature engineering that extracts predictive information and appropriate selection of machine learning algorithms based on the feature set.

In the past few years there has been a growing interest in the employment of verbal productions as digital biomarkers, namely objective, quantifiable behavioural data that can be collected and measured by means of digital devices, allowing for a low-cost pathology detection, classification and monitoring. Numerous research papers have been published on the automatic detection of subtle verbal alteration, starting from written texts, raw speech recordings and transcripts, and such linguistic analysis has been singled out as a cost-effective method for diagnosing dementia and other medical conditions common among elderly patients (e.g., cognitive dysfunctions associated with metabolic disorders, dysarthria). To provide a critical appraisal and synthesis of evidence concerning the application of natural language processing (NLP) techniques for clinical purposes in the geriatric population. In particular, we discuss the state of the art on studying language in healthy and pathological ageing, focusing on the latest research efforts to build non-intrusive language-based tools for the early identification of cognitive frailty due to dementia. We also discuss some challenges and open problems raised by this approach. We performed a scoping review to examine emerging evidence about this novel domain. Potentially relevant studies published up to November 2021 were identified from the databases of MEDLINE, Cochrane and Web of Science. We also browsed the proceedings of leading international conferences (e.g., ACL, COLING, Interspeech, LREC) from 2017 to 2021, and checked the reference lists of relevant studies and reviews. The paper provides an introductory, but complete, overview of the application of NLP techniques for studying language disruption due to dementia. We also suggest that this technique can be fruitfully applied to other medical conditions (e.g., cognitive dysfunctions associated with dysarthria, cerebrovascular disease and mood disorders). Despite several critical points need to be addressed by the scientific community, a growing body of empirical evidence shows that NLP techniques can represent a promising tool for studying language changes in pathological aging, with a high potential to lead a significant shift in clinical practice. What is already known on this subject Speech and languages abilities change due to non-pathological neurocognitive ageing and neurodegenerative processes. These subtle verbal modifications can be measured through NLP techniques and used as biomarkers for screening/diagnostic purposes in the geriatric population (i.e., digital linguistic biomarkers-DLBs). What this paper adds to existing knowledge The review shows that DLBs can represent a promising clinical tool, with a high potential to spark a major shift to dementia assessment in the elderly. Some challenges and open problems are also discussed. What are the potential or actual clinical implications of this work? This methodological review represents a starting point for clinicians approaching the DLB research field for studying language in healthy and pathological ageing. It summarizes the state of the art and future research directions of this novel approach.

The “Skill Gap Analysis Using Machine Learning” project aims to bridge the gap between user skillsets and industry requirements.It examines user resumes, finds skill gaps, and gives a path for skill improvement using Natural Language Processing (NLP) and machine learning techniques. Additionally, the system suggests resources for interview preparation based on the professional domains that the user has chosen. This platform supports firms in workforce development while providing users with practical insights for career growth. KEYWORDS: Skill Gap Analysis, Machine Learning, Natural Language Processing (NLP), Resume Analysis, Skill Enhancement, Industry Requirements, Career Growth, Personalized Development Roadmap, Interview Preparation Resources, Career Domains, Workforce Development, Skill Recommendations, Employee Development, Data- Driven Insights, Job Market Alignment, User-Centric Design, Actionable Insights, Predictive Analytics, Skill Matching, Resource Optimization.