Natural Language Research Articles

CitizenConnectBot(CCB): The interactive guide to government schemes

In the ever-evolving landscape of digital governance, the Generative CitizenConnectBot (CCB) emerges as a transformative tool, seamlessly bridging the gap between citizens and government schemes.The absence of real-time updates results in outdated information, undermining the effectiveness of the service. Moreover, the current translation feature limits the accessibility of scheme details for a diverse audience, creating barriers to understanding and participation. Additionally, the lack of a chatbot on schemes dissemination in the available websites complicates the process for citizens to obtain necessary information and determine eligibility, impeding their ability to benefit from available support.The CitizenConnectBot(CCB), integrated into a user-friendly website, acts as a comprehensive information hub, offering citizens tailored insights into various government initiatives.The bot's adaptability ensures that information is generated according to the unique requirements of each user.This paper employs natural language processing (NLP) techniques, including text classification with SVM, TF-IDF vectorization, regular expressions for pattern matching, and session management for maintaining conversation context.

The nexus of minds unveiling the significance of AI in mental health and viable remedie

The world is evolving at a cutthroat rate, and the integration of mental well-being and technology can yield promising results. For this reason, I have created The Nexus of Minds, which focuses on exploring the role of AI in changing the dynamics and thought processes of how we address and approach mental health issues. I am focusing on the transformative potential of Artificial Intelligence as a force for accessible, personalized, and effective mental healthcare. Mental healthcare has a huge spectrum, and I am exploring the multifaceted applications of AI, which have the potential to change the landscape. Machine Learning, Data Analytics, and Natural Language Processing have shown immense advancements, which is empowering AI to redefine how mental health issues are diagnosed and treated. Ranging from earlier intervention through continuous monitoring to all-time support, this book focuses on different ways in which AI solutions are improving the quality of mental healthcare and clinical results. It focuses on how AI is empowering people on their mental health journey. In addition to the technological innovation, I will navigate through the ethical considerations that need to be focused on, including but not limited to privacy, algorithmic bias, data privacy and security, and the role of human connection and empathy in mental healthcare. By understanding these essentials, I am offering insights into the responsible, ethical, and balanced integration of AI into mental health support. Moreover, this book focuses on apps and chatbots that are already providing help to people struggling with mental health illnesses and how they are benefiting from AI-centric mental health services. As a result, the readers will be able to gain an understanding of how AI will improve the quality of mental healthcare and destigmatize mental health conditions. With "The Nexus of Minds," I envision a future where AI won't be merely a tool but an absolute force in mental healthcare by breaking down the stigma and societal barriers to give hope to struggling people. This book will become a guiding beacon, providing information about the seamless coexistence of ethical and compassionate mental healthcare and tech advancement. I have taken an interdisciplinary approach, making it a promising resource for healthcare professionals, policymakers, researchers, and common people who want to see a bright future in the mental health industry.

The evolution of AI: What does the future hold in the next two years

Artificial intelligence (AI) has evolved rapidly in recent years, transforming several industries and reshaping cultural norms. This article examines the current state of AI technology, focusing on significant trends and developments that have accelerated its growth. This paper sheds light on the evolving environment of AI innovation by examining current breakthroughs in machine learning, deep learning, and ethical concerns surrounding AI deployment. The following two years should see substantial advances in natural language processing, computer vision, and autonomous systems. Despite the excitement, difficulties such as ethical considerations, regulatory frameworks, and societal repercussions loom large, needing careful study and decisive action. This study aims to inform stakeholders about the transformative potential of AI and the methods required to properly navigate its growing landscape by conducting a balanced examination of opportunities and challenges.

FarmTechBot (FTB): Empowering farmers for better harvest

In an evolving world, the FarmTechBot (FTB) emerges as a pioneering digital assistant, empowering farmers with essential insights and real-time information crucial for optimizing agricultural practices. The presence of chatbot functionality capable of addressing queries related to soil, pest, and market linkage associates to overcome hurdles for users seeking real-time information. Furthermore, the lack of voice recognition capabilities limits accessibility, especially for users who prefer or require voice-based interaction. Additionally, the manual process of pest identification from uploaded crop images hampers efficiency, prolonging the time required for pest management. The FTB represents a significant advancement in agricultural technology, addressing the existing system's limitations and empowering farmers with real-time information, enhanced accessibility, and streamlined pest management capabilities. The FTB utilizes a combination of techniques including Histogram of Oriented Gradients (HOG) for image feature extraction and classification using Support Vector Classifier (SVC). In Natural Language Processing (NLP), it employs tokenization and TF-IDF vectorization for text preprocessing and representation, alongside Linear Support Vector Classifier (LinearSVC) for intent classification.

Correlation Between News and Stock Price Based on Stock Market Indices: Can News Classification Be Used as a Tool to Make Better Decisions?

The stock market is influenced by various factors, including news events, economic indicators, and investor sentiment. Understanding the correlation between news and stock price movements interests market participants and researchers. In this paper, we explore the relationship between news sentiment and stock market trends using stock market indices. We employ natural language processing (NLP) techniques to classify news articles and analyze their impact on stock market indices, focusing on the S&P 500 and the Dow Jones Industrial Average. We utilize sentiment analysis and machine learning algorithms, including Random Forest, Loughran-McDonald (2014) Financial Sentiment Word Lists (Extended), and AFINN Lexicon, to predict stock market trends based on news sentiment. Our findings demonstrate that positive news sentiment has a more significant impact on stock prices than negative sentiment. The Random Forest model achieves the highest accuracy, while domain-specific lexicons provide valuable insights into financial news sentiment. However, predicting negative trends remains a challenge across all methods. Our research contributes to the growing knowledge of the relationship between news and stock prices and provides valuable insights for market participants and researchers.

Distinct CT imaging features of new liver metastases from primary genitourinary cancers.

To apply natural language processing (NLP) to a large volume of structured radiology reports in the investigation of CT imaging features of new liver metastases from primary genitourinary cancers. In this retrospective study, a previously reported NLP model was applied to consecutive structured CT reports from 2016 to 2022 to predict those patients with primary genitourinary cancer who developed liver metastasis. Pathology or imaging follow-up served as the reference standard for validating NLP predictions. Subsequently, diagnostic CTs of the identified patients were qualitatively assessed by two radiologists, whereby several imaging features of new liver metastasis were assessed. Proportions of the assessed imaging features were compared between primary genitourinary cancers using the Chi-square or Fisher's exact test. In 112 patients (mean age = 72years; 83 males), the majority of new liver metastases were hypovascular (73.2%), well defined (76.6%), homogenous (66.9%), and without necrotic/cystic component (73.2%). There was a higher proportion of iso- to hyperdense liver metastases for primary kidney cancer vs other primary genitourinary cancers (42.5% in kidney cancer; 2.3% in ureter/bladder cancer, 8% in prostate cancer, and 0% in testicular cancer; p < 0.05) and a higher proportion of new liver metastases with ill-defined margin for primary prostate cancer vs other primary genitourinary cancers (44.0% in prostate cancer, 15.0% in kidney cancer, 18.6% in ureter/bladder cancer, and 25.0% in testicular cancer; p < 0.05). New liver metastases from primary genitourinary cancers tend to be hypovascular and show several distinct imaging features between different primary genitourinary cancers.

Mapping the foundations and evolution of career aspiration research: a bibliometric analysis

PurposeThis bibliometric literature review aims to uncover the (1) intellectual foundations and (2) topical evolution in the field of career aspiration research.Design/methodology/approachWe employed the systematic literature review methodology following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. According to the predetermined criteria, 286 articles were included from the Web of Science database. Then, we employed performance analysis, science mapping and natural language processing to address our research objectives.FindingsCo-citation analysis uncovered four foundational themes: (1) gender and leadership aspiration, (2) Social Cognitive Career Theory, (3) process of career development and (4) adolescents’ career aspiration. Moreover, co-word analysis showed that scholarly foci have shifted from adolescents’ career development to diverse streams. This shift was indicated by the exploration of additional research samples, such as university students, and topics related to specific aspirations (e.g. entrepreneurial and managerial aspirations) and gender issues (e.g. women leadership and gender stereotypes).Research limitations/implicationsAlthough this review has limitations related to data selection, it presents implications for practice, theory and future research on career aspirations.Originality/valueThe study illuminated the past and development of a research domain, thus advancing the understanding of career aspirations and inspiring future research.

The language of nature in Murray Bowen's writings: how connection to nature informs Bowen theory and is essential to human survival

AbstractThe human is a product of nature, and the same emotional process at work in families is at work in the relationship of the human to nature. Current global concerns and environmental changes highlight the need for humans to revisit their relationships with the natural world. The purpose of this study was to survey Murray Bowen's professional letters to reify the constructs of Bowen theory. Qualitative methodology with a narrative research design was employed. A sample (N = 250) of Dr Bowen's professional letters from the archives dating from 1965 to 1990 was analysed. Results from this analysis reveal that Bowen's writings are grounded in the language of nature and that Bowen's thinking about the human species and the family offers ways in which to see the family and humans as part of a larger system. Relating more maturely to the natural world is essential for human survival.

Semantically Interoperable Census Data: Unlocking the Semantics of Census Data Using Ontologies and Linked Data

The Canadian Census of Population is a survey that collects statistical information on the Canadian population. These censuses contain valuable socioeconomic data that is often used by both the public and private sectors for project planning and decision-making. However, there are a few issues that may arise when using census data. Firstly, data wrangling, which is often a time-consuming process, needs to be conducted in order to clean and prepare the data for integration and use. Secondly, different datasets across different census years may be using different terms to describe the same concept/entity, hence creating a problem of referential equivalence (i.e., how do we know whether two different datasets are referring to the same concepts/entities?). Lastly, the data found in a census is often described using natural language that isn't easily interpreted by machines and can be difficult to break down or deconstruct. In this paper, we develop and propose the use of an ontology for representing the data from the Canadian Census of Population as linked data in order to address the aforementioned issues, evaluate the ontology using competency questions based on real world use cases, and discuss the advantages of census linked data for integration and visualisation uses.

Perception of temporal structure in speech is influenced by body movement and individual beat perception ability.

The subjective experience of time flow in speech deviates from the sound acoustics in substantial ways. The present study focuses on the perceptual tendency to regularize time intervals found in speech but not in other types of sounds with a similar temporal structure. We investigate to what extent individual beat perception ability is responsible for perceptual regularization and if the effect can be eliminated through the involvement of body movement during listening. Participants performed a musical beat perception task and compared spoken sentences to their drumbeat-based versions either after passive listening or after listening and moving along with the beat of the sentences. The results show that the interval regularization prevails in listeners with a low beat perception ability performing a passive listening task and is eliminated in an active listening task involving body movement. Body movement also helped to promote a veridical percept of temporal structure in speech at the group level. We suggest that body movement engages an internal timekeeping mechanism, promoting the fidelity of auditory encoding even in sounds of high temporal complexity and irregularity such as natural speech.

Typical artificial intelligence algorithms and real-world applications related to handwritten number classifier

Artificial Intelligence is a science that aiming to creating systems to imitate human wisdom, and perform tasks such as learning, reasoning, problem-solving, perception, natural language understanding, and some forms of creativity. This paper is about handwritten number classifier, a specific field within artificial intelligence, and its use across various industries. The basic concepts about artificial intelligence will be given at the first and the definition and structure of three representative artificial intelligence algorithms, convolutional neural networks, recurrent neural networks, and long short-term memory neural networks will be exemplified to better illustrate the concepts. Furthermore, the applications of handwritten number classifier will be analysed, especially in the areas of large-scale data statistics or survey, finance and taxation, and mail sorting. Eventually, a conclusion encompassing the challenges that the artificial intelligence systems are faced and reasoning regarding the significant role that artificial intelligence plays in the urban areas of the world is given for further discussion.

Hybrid Natural Language Processing Model for Sentiment Analysis during Natural Crisis

This paper introduces a novel natural language processing (NLP) model as an original approach to sentiment analysis, with a focus on understanding emotional responses during major disasters or conflicts. The model was created specifically for Croatian and is based on unigrams, but it can be used with any language that supports the n-gram model and expanded to multiple word sequences. The presented model generates a sentiment score aligned with discrete and dimensional emotion models, reliability metrics, and individual word scores using affective datasets Extended ANEW and NRC WordEmotion Association Lexicon. The sentiment analysis model incorporates different methodologies, including lexicon-based, machine learning, and hybrid approaches. The process of preprocessing includes translation, lemmatization, and data refinement, utilized automated translation services as well as the CLARIN Knowledge Centre for South Slavic languages (CLASSLA) library, with a particular emphasis on diacritical mark correction and tokenization. The presented model was experimentally evaluated on three simultaneous major natural crises that recently affected Croatia. The study’s findings reveal a significant shift in emotional dimensions during the COVID-19 pandemic, particularly a decrease in valence, arousal, and dominance, which corresponded with the two-month recovery period. Furthermore, the 2020 Croatian earthquakes elicited a wide range of negative discrete emotions, including anger, fear, and sadness, with the recuperation period much longer than in the case of COVID-19. This study represents an advancement in sentiment analysis, particularly in linguistically specific contexts, and provides insights into the emotional landscape shaped by major societal events.

Text and image generation from intracranial electroencephalography using an embedding space for text and images

Objective. Invasive brain–computer interfaces (BCIs) are promising communication devices for severely paralyzed patients. Recent advances in intracranial electroencephalography (iEEG) coupled with natural language processing have enhanced communication speed and accuracy. It should be noted that such a speech BCI uses signals from the motor cortex. However, BCIs based on motor cortical activities may experience signal deterioration in users with motor cortical degenerative diseases such as amyotrophic lateral sclerosis. An alternative approach to using iEEG of the motor cortex is necessary to support patients with such conditions. Approach. In this study, a multimodal embedding of text and images was used to decode visual semantic information from iEEG signals of the visual cortex to generate text and images. We used contrastive language-image pretraining (CLIP) embedding to represent images presented to 17 patients implanted with electrodes in the occipital and temporal cortices. A CLIP image vector was inferred from the high-γ power of the iEEG signals recorded while viewing the images. Main results. Text was generated by CLIPCAP from the inferred CLIP vector with better-than-chance accuracy. Then, an image was created from the generated text using StableDiffusion with significant accuracy. Significance. The text and images generated from iEEG through the CLIP embedding vector can be used for improved communication.

Analysis of addiction craving onset through natural language processing of the online forum Reddit.

Alcohol cravings are considered a major factor in relapse among individuals with alcohol use disorder (AUD). This study aims to investigate the frequency and triggers of cravings in the daily lives of people with alcohol-related issues. Large amounts of data are analyzed with Artificial Intelligence (AI) methods to identify possible groupings and patterns. For the analysis, posts from the online forum "stopdrinking" on the Reddit platform were used as the dataset from April 2017 to April 2022. The posts were filtered for craving content and processed using the word2vec method to map them into a multi-dimensional vector space. Statistical analyses were conducted to calculate the nature and frequency of craving contexts and triggers (location, time, social environment, and emotions) using word similarity scores. Additionally, the themes of the craving-related posts were semantically grouped using a Latent Dirichlet Allocation (LDA) topic model. The accuracy of the results was evaluated using two manually created test datasets. Approximately 16% of the forum posts discuss cravings. The number of craving-related posts decreases exponentially with the number of days since the author's last alcoholic drink. The topic model confirms that the majority of posts involve individual factors and triggers of cravings. The context analysis aligns with previous craving trigger findings related to the social environment, locations and emotions. Strong semantic craving similarities were found for the emotions boredom, stress and the location airport. The results for each method were successfully validated on test datasets. This exploratory approach is the first to analyze alcohol cravings in the daily lives of over 24,000 individuals, providing a foundation for further AI-based craving analyses. The analysis confirms commonly known craving triggers and even discovers new important craving contexts.

TFormer: A time–frequency Transformer with batch normalization for driver fatigue recognition

Within the framework of the advanced human-cybernetic interfaces (HCI), Cross-subject electroencephalogram (EEG)-based driver fatigue recognition is emerging as a pivotal application in the paradigm of Industry 5.0. Recognizing the importance of ensuring driver safety through proactive monitoring, it is essential to offer a general EEG decoding system to improve road safety. This work investigated the use of Transformers for the challenging cross-subject EEG decoding task due to the great success the Transformers have achieved in various applications. Previous research focused on using Transformers to capture global temporal information, but less work targeted global frequency-domain patterns. Furthermore, in order to leverage a standard Transformer architecture grounded in natural language processing for EEG decoding, it is imperative to account for inherent characteristics in EEG and make pertinent adjustments accordingly. In this work, we proposed a time–frequency Transformer (TFormer) that can automatically learn the global time–frequency patterns from raw EEG data. TFormer consisted of three components: convolutional stems for input embedding, time–frequency multi-head cross-attention (TF-MCA) for integrating time-domain patterns into frequency points, and self-attention to further learn global time-frequency patterns. Moreover, we analyzed TFormer’s internal settings and found batch normalization (BN) more suitable for cross-subject EEG decoding than layer normalization (LN). The experiment results demonstrated the superiority of the proposed model compared to existing methods. Overall, our work contributes to the development of Transformer models in EEG decoding and illustrates a different way to leverage Transformers for decoding raw EEG data.

Blending Strategic Expertise and Technology: A Case Study for Practice Analysis

AbstractThere is a growing integration of technology in the workplace (World Economic Forum), and with it, organizations are increasingly relying on advanced technological approaches for improving their human capital processes to stay relevant and competitive in complex environments. All professions must keep up with this transition and begin integrating technology into their tools and processes. This paper centers on how advanced technological approaches (such as natural language processing (NLP) and data mining) have complemented a traditional practice analysis of the accounting profession. We also discuss strategic selection and use of subject‐matter experts (SMEs) for more efficient practice analysis. The authors have adopted a triangulation process—gathering information from traditional practice analysis, using selected SMEs, and confirming findings with a novel NLP‐based approach. These methods collectively contributed to the revision of the Uniform CPA Exam blueprint and in understanding accounting trends.

Innovations in Risk Measurement and Management for Strategic Financing Decisions

This study aims to explore the integration of innovative quantitative models, behavioral finance insights, and advanced analytics techniques in contemporary risk management practices to bolster organizations' resilience and informed decision-making within dynamic financial landscapes. Through a systematic literature review spanning disciplines such as finance, psychology, and technology, this research synthesizes diverse insights to comprehensively understand the evolving nature of risk management strategies. The findings elucidate that incorporating advanced quantitative models like Value at Risk (VaR) and Conditional Value at Risk (CVaR) empowers organizations to meticulously assess and quantify financial risks, thereby enhancing risk management accuracy and granularity. Furthermore, the assimilation of behavioral finance insights illuminates the psychological determinants shaping risk perception and decision-making behaviors, leading to the development of more robust risk management frameworks that accommodate both rational and irrational factors. Moreover, leveraging advanced analytics techniques, notably natural language processing (NLP) algorithms, enables proactive risk management by analyzing textual data and market sentiments with heightened precision. This study underscores the imperative of perpetual innovation and adaptability in risk management practices to adeptly navigate the intricacies of modern financial landscapes. By harnessing emerging technologies, behavioral insights, and interdisciplinary methodologies, organizations can fortify their risk management capabilities, mitigate threats, and seize opportunities in today's globally interconnected economy. Addressing the challenges associated with implementing innovative risk management practices necessitates proactive leadership, resource allocation, and organizational culture transformation. Ultimately, this research contributes to advancing the understanding and application of innovative risk measurement and management approaches, fostering sustainable growth and success for organizations.

THÌ and LÀ Constructions in Vietnamese: A Study on Argument Structures

This study aims to shed light on the argument structure of the Vietnamese particles THÌ and LÀ, which function as markers of focused constituents and topic-comment structures, respectively. The research objectives are to identify the syntactic and semantic properties of constructions involving THÌ and LÀ, and to analyze their argument structures within the theoretical frameworks of construction grammar. Specifically, the constructional approach (Goldberg 1995, 2006) and radical construction grammar (Croft 2001) will be employed. The methodology involves collecting a corpus of naturally occurring data from various written sources. The data will undergo qualitative analysis to identify argument structure patterns associated with THÌ and LÀ constructions, as well as quantitative analysis of frequency and distribution. The expected findings will provide insights into the argument structures of these Vietnamese constructions, elucidating how focused constituents and topic-comment structures are marked. The study will evaluate the applicability of the chosen theoretical frameworks in accounting for the observed patterns. The findings have potential applications in natural language processing tasks requiring accurate recognition and interpretation of focused constituents and topic-comment structures, such as information extraction, machine translation, and language generation. Furthermore, the study may inform language teaching and acquisition by shedding light on the usage patterns of THÌ and LÀ in Vietnamese. The analysis is expected to contribute to a deeper understanding of argument structures in Vietnamese and potentially motivate extensions or modifications to the theoretical frameworks.

Multi-step Transfer Learning in Natural Language Processing for the Health Domain

The restricted access to data in healthcare facilities due to patient privacy and confidentiality policies has led to the application of general natural language processing (NLP) techniques advancing relatively slowly in the health domain. Additionally, because clinical data is unique to various institutions and laboratories, there are not enough standards and conventions for data annotation. In places without robust death registration systems, the cause of death (COD) is determined through a verbal autopsy (VA) report. A non-clinician field agent completes a VA report using a set of standardized questions as guide to identify the symptoms of a COD. The narrative text of the VA report is used as a case study to examine the difficulties of applying NLP techniques to the healthcare domain. This paper presents a framework that leverages knowledge across multiple domains via two domain adaptation techniques: feature extraction and fine-tuning. These techniques aim to improve VA text representations for COD classification tasks in the health domain. The framework is motivated by multi-step learning, where a final learning task is realized via a sequence of intermediate learning tasks. The framework builds upon the strengths of the Bidirectional Encoder Representations from Transformers (BERT) and Embeddings from Language Models (ELMo) models pretrained on the general English and biomedical domains. These models are employed to extract features from the VA narratives. Our results demonstrate improved performance when initializing the learning of BERT embeddings with ELMo embeddings. The benefit of incorporating character-level information for learning word embeddings in the English domain, coupled with word-level information for learning word embeddings in the biomedical domain, is also evident.

Enhancing Durrotalk Chatbot Accuracy Utilizing a Hybrid Model Based on Recurrent Neural Network (RNN) Algorithm and Decision Tree

DurroTalk, a chatbot for new student admissions at Pondok Pesantren Durrotu Ahlissunnah Waljamaah, Semarang, integrates a hybrid model with Recurrent Neural Network (RNN) and Decision Tree. RNN, the base model, employs Natural Language Processing (NLP) to understand sentence structure and context, overcoming vanishing gradient through LSTM layers. The Decision Tree normalizes words, addressing slang and synonyms. The hybrid model boosts chatbot accuracy by 9%, reaching 77% from the initial 68%. This research signifies progress in integrating artificial intelligence into traditional education, showcasing a chatbot adept at handling non-standard language. Decision Tree integration enhances overall performance, making the chatbot proficient in understanding user inputs and generating contextually relevant responses. This study exemplifies the potential of AI, particularly chatbot technology, in modernizing educational processes at traditional institutions.