Natural language processing for cognitive therapy: Extracting schemas from thought records

Natural Language Processing (NLP) is an essential element of computational linguistics and artificial ‎intelligence, enabling fluid interactions between humans and machines. Social networking networks ‎produce substantial volumes of user-generated text daily, offering both opportunities and challenges for ‎NLP researchers. Social media discourse's informal, dynamic, and context-dependent characteristics ‎necessitate specific NLP techniques for precise processing and analysis. This study thoroughly examines NLP applications in social media, including essential tasks such as sentiment analysis, topic ‎modeling, misinformation detection, and hate speech identification. It examines the influence of machine ‎learning and deep learning methodologies, particularly transformer models, on the advancement of NLP ‎capabilities. This study also emphasizes the ethical issues related to NLP-driven social media apps, ‎including data privacy, algorithmic bias, and the regulation of misinformation. The paper continues by ‎discussing emerging research paths, highlighting the necessity for adaptable and ethical NLP solutions in ‎the changing social media environment‎.

Integrated Development Environments (IDEs) are pivotal in enhancing productivity with features like code completion in modern software development. Recent advancements in Natural Language Processing (NLP) have empowered neural language models for code completion. In this study, we present an extensive investigation of the impact of open and closed vocabulary systems on the task of code completion. Specifically, we compare open and closed vocabulary systems with various vocabulary sizes to observe their impact on code completion performance. We experiment with three different open vocabulary systems: byte pair encoding (BPE), WordPiece and Unigram to compare them with closed-vocabulary systems to analyze their modeling performance. We also conduct experiments with different context sizes to study their impact on code completion performance. We have experimented using various prominent language models, including one from recurrent neural networks and five from transformers. Our results indicate that vocabulary size significantly impacts modeling performance and can artificially boost the accuracy of code completion models, especially in the case of a closed-vocabulary system. Moreover, we find that different vocabulary systems have varying impacts on token coverage, whereas open-vocabulary systems exhibit better token coverage. Our findings offer valuable insights for building effective code completion models, aiding researchers and practitioners in this field.

The increasing demand for cross-lingual communication in a globally interconnected world has spurred significant advancements in Natural Language Processing (NLP), a branch of artificial intelligence aimed at enabling computers to comprehend, generate, and process human language. Over the past few years, NLP has transitioned from traditional rule-based and statistical approaches to deep learning-based methods, with pre-trained models like BERT and GPT demonstrating remarkable performance across a variety of tasks, including text classification and machine translation. However, the linguistic diversity of the world's languages presents ongoing challenges, as many NLP models are primarily developed for high-resource languages like English, leaving other languages underserved. This paper examines the linguistic features of three representative languagesChinese, Japanese, and Englishand explores NLP methodologies tailored to their distinct grammatical, lexical, and structural properties. Through comparative linguistic analysis, this study discusses the challenges of multilingual NLP and the potential for cross-lingual applications. The findings underscore the importance of adapting NLP models to different language characteristics to enhance the effectiveness and inclusivity of cross-lingual processing.

Research on lifestyle changes during the COVID-19 pandemic often relies on Likert-type scale question surveys. Survey participants respond to questions by selecting one of the numerically ordered choices, "Strongly Disagree"=1, "Disagree"=2, "Neutral"=3, "Agree"=4, and "Strongly Agree"=5. Analyzing Likert-type data requires statistical methods beyond approaches like linear regression. First, it is unclear if the distance between choices are truly equal. For example, are "Agree" and "Strongly Agree" more close than "Neutral" and "Agree? Second, summarizing results using traditional means makes little sense. For example, would a mean of 4.5 imply "Agree and a half"? Finally, participants tend to select more central choices and less extremes.

Speech and language impairments, long recognized as early symptoms of Alzheimer's disease (AD), can now be quantified with unprecedented precision due to recent advances in natural language processing (NLP) and artificial intelligence (AI). Despite growing interest in AI-enabled speech biomarkers, few studies have linked spontaneous speech to biologically verified AD, and most have focused on English-language data or acoustic features with limited linguistic interpretability. Here, we present the first end-to-end machine-learning framework for automatic AD detection from German speech, using clinical-biological criteria validated by cerebrospinal fluid (CSF) biomarkers. 44 participants were included: 22 biomarker-defined AD cases from a prospective observational study (German Clinical Trials Register, DRKS00030633) and 22 socio-demographically matched cognitively healthy controls (CHC). Connected speech was elicited using the standardized Cookie Theft picture description task. Recordings were transcribed with a state-of-the-art automatic speech recognition (ASR) system. From these transcripts, 32 theory-driven linguistic biomarkers were computed with an advanced NLP tool, falling into three categories: information-theoretic, lexical richness, and syntactic. AD-versus-CHC classification used five supervised models (logistic regression, support vector machine with a radial basis function kernel, random forest, gradient boosting, XGBoost) under stratified five-fold cross-validation, with stability-based recursive feature elimination performed within training folds. Model interpretability was assessed using SHapley Additive exPlanations (SHAP). Recursive feature elimination retained seven of 32 candidate speech biomarkers as consistently informative across folds. Trained on this subset, all classifiers showed strong discrimination between biomarker-defined AD and CHC. Logistic regression, SVM, random forest, and gradient boosting achieved ~91% mean accuracy with F1 ≈ 0.90 and sensitivity ≈ 0.90, while XGBoost was slightly lower (~89% accuracy). SHAP analyses indicated that model decisions were primarily driven by information-theoretic and structural markers: lower compressibility, reduced lexical density, shorter clauses and sentences, and weaker predictive sequencing indexed by higher-order n-gram statistics. Clinically meaningful linguistic biomarkers can be robustly derived from spontaneous speech, even in small, well-characterized clinical samples. Theory-driven features and stability-focused modeling show that information-theoretic and structural properties of connected speech capture core Alzheimer-related impairments with robust classification performance. These findings support AI-enabled speech analysis as a non-invasive, scalable complement to established biological biomarkers of Alzheimer's disease.

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.

Natural Language Processing (NLP) has emerged as a transformative technology within the domain of artificial intelligence, significantly advancing the ability of machines to comprehend and interact with human language. Recent advancements in machine learning algorithms have led to substantial improvements in a variety of NLP applications, including sentiment analysis, machine translation, automated summarization, and question answering. This study presents a novel approach to NLP, which integrates advanced machine learning techniques to enhance performance in these areas. The proposed method achieves a high level of accuracy, with an overall accuracy rate of 97.6%. Additionally, it demonstrates strong performance in error metrics, including a Mean Absolute Error (MAE) of 0.403 and a Root Mean Square Error (RMSE) of 0.203. These results indicate a significant advancement in the precision and effectiveness of language-based tasks. Despite these achievements, challenges such as the management of ambiguous language, the need for extensive annotated datasets, and the demand for real-time processing capabilities remain. This paper aims to explore the current state of NLP driven by machine learning, examining key techniques and methodologies that have propelled its development. The analysis will cover various machine learning approaches applied to NLP, evaluate their impact on different applications, and identify ongoing challenges and future research directions. By providing a comprehensive overview of the interplay between NLP and machine learning, this research seeks to enhance understanding of how these technologies can be leveraged to improve language processing and communication

Natural Language Processing (NLP) has witnessed remarkable advancements in recent years, revolutionizing various fields including voice assistants and chatbots. This paper explores the latest developments in NLP technologies and their applications in enhancing the functionality and intelligence of voice assistants and chatbots. Beginning with an overview of NLP and its core components, such as tokenization, syntactic analysis, semantic understanding, and machine learning techniques, this paper delves into the cutting-edge techniques driving the evolution of NLP. Furthermore, it discusses the challenges faced by NLP systems and the strategies employed to address them. Subsequently, the paper explores the utilization of NLP in voice assistants and chatbots, elucidating how these technologies are reshaping human-computer interaction, improving user experiences, and expanding the scope of applications in various domains. Additionally, it examines the impact of NLP advancements on the future trajectory of voice assistants and chatbots, highlighting potential opportunities and implications for research and industry

Studying human emotions and feelings is a crucial element in the field of psychology, having significant implications such as evaluating mental health and improving human-computer interactions. Recently, there has been a rise in interest in examining how psychological sentiment can be predicted through various mediums, including text, audio, video, and physiological signals. By utilizing advancements in Natural Language Processing (NLP) and analysing multimodal data, this research delves into incorporating emotion datasets into NLP frameworks to improve psychological sentiment prediction. This presents present an applicability of some of the NLP techniques to predicts the Psychological Sentiment such as Deep Generating adversarial networks (D-GANs), Long short-term memory (LSTM) and gated recurrent unit (GRU). The sentimental analysis performed by the considered algorithms are implemented in python and the parameters which are considered for the results evaluation are model loss, confusion matrix, accuracy, precision, recall and f1-score. Our goal with this analysis is to offer a deeper understanding of the existing methodologies and future scope of growth in psychological sentiment prediction of research in the field of NLP.

Natural Language Processing (NLP) plays a critical role in the development of chatbots, enabling them to understand and generate human-like language. This paper provides a comprehensive review of the applications, challenges, and future directions of NLP in chatbots. It discusses the fundamental principles of NLP, including tokenization, part-of-speech tagging, named entity recognition, and sentiment analysis, and examines how these techniques are used in chatbots. The paper also explores the challenges and limitations of NLP in chatbots, such as ambiguity in language, multilingual support, privacy concerns, and integration with existing systems. Additionally, it discusses recent advances in NLP, such as neural language models and transfer learning, and their potential impact on the future development of chatbots. Ethical considerations in NLP development are also addressed. Overall, the paper highlights the significant role of NLP in advancing chatbot technology and the challenges that must be overcome to realize its full potential.

Advances in natural language processing (NLP) and automatic speech recognition and processing technologies offer new opportunities for language testing. Despite their potential uses on a range of language test item types, relatively little work has been done in this area, and it is therefore not well understood by test developers, researchers or users in language assessment. This paper introduces NLP for language assessment as an area of inquiry and practice by describing the historical roots coming from computational linguistics, statistical NLP, speech recognition and processing technologies, language assessment, and computer-assisted language learning. It outlines uses of NLP and speech recognition and processing technologies in language assessment through illustrations of current testing projects, and identifies areas in need of further development.

Current research manifests the challenges and limitations of analyzing copious volumes of textual content while conducting literature reviews. Recent advances in natural language processing (NLP) and increased computational power have made it easier for researchers to review large volumes of literature more efficiently. In this study, we apply Leidner’s polylithic framework as a theoretical lens to examine areas where NLP may aid the literature review process. Specifically, we anatomize the use of NLP in published literature review studies in terms of capabilities, characteristics, and workflows. Through this analysis, we identify four meta-requirements that underpin the development of effective NLP-enhanced literature review solutions, forming the foundation of our proposed NLP-enhanced Literature Review (NLP-e-LR) framework. This framework provides structured guidance for applying suitable NLP techniques across various types of literature reviews based on research objectives and review focus. We present a mapping of NLP-e-LR process enhancements to the core literature review tasks (i.e., search, screening, and analysis), outlining a range of NLP capabilities aligned with each task and detailing their relevance across different literature review types. Through illustrative examples, we demonstrate how NLP-e-LR facilitates key literature review activities. Finally, we discuss the benefits and limitations of the framework and identify directions for future research.

Tobler's First Law of Geography (TFL) is one of the key reasons why is special. The law, which states that is related to everything else, but near things are more related than distant things, is central to the management, presentation, and analysis of geographic information. However, despite the importance of TFL, we have a limited general understanding of its domain-neutral properties. In this paper, we leverage recent advances in the natural language processing domain of semantic relatedness estimation to, for the first time, robustly evaluate the extent to which relatedness between spatial entities decreases over distance in a domain-neutral fashion. Our results reveal that, in general, TFL can indeed be considered a globally recognized domain-neutral property of geographic information but that there is a distance beyond which being nearer, on average, no longer means being more related.

Major Depression (MD) and General Anxiety Disorder (GAD) are the most common mental health disorders, which typically are assessed quantitatively by rating scales such as PHQ-9 and GAD-7. However, recent advances in natural language processing (NLP) and machine learning (ML) have opened up the possibility of question-based computational language assessment (QCLA). Here we investigate how accurate open-ended questions, using descriptive keywords or autobiographical narratives, can discriminate between participants that self-reported diagnosis of depression and anxiety, or health control. The results show that both language and rating scale measures can discriminate well, however, autobiographical narratives discriminate best between healthy and anxiety (ϕ = 1.58), as well as healthy and depression (ϕ = 1.38). Descriptive keywords, and to a certain extent autobiographical narratives, also discriminate better than summed scores of GAD-7 and PHQ-9 (ϕ=0.80 in discrimination between anxiety and depression), but not when individual items of these scales were analyzed by ML (ϕ=0.86 and ϕ=0.91 in item-level analysis of PHQ-9 and GAD-7, respectively). Combining the scales consistently elevated the discrimination even more (ϕ=1.39 in comparison between depression and anxiety), both in item-level and sum-scores analyses. These results indicate that QCLA measures often, but not in all cases, are better than standardized rating scales for assessment of depression and anxiety. Implication of these findings for mental health assessments are discussed.

Caption generation requires best of both Computer Vision and Natural Language Processing. Due to recent improvements in both of them many efficient models have been developed. Automatic Image Captioning can be utilized to provide descriptions of website content or to engender frame-by-frame descriptions of video for the vision-impaired and in many such applications. In this work, a model is described which is utilized to generate novel image captions for a previously unseen image by utilizing a multimodal architecture by amalgamation of a Recurrent Neural Network (RNN) and a Convolutional Neural Network (CNN). The model is trained on Microsoft Common Objects in Context (MSCOCO), an image captioning dataset that aligns captions and images in the same representation space, so that an image is close to its relevant captions in that space and far away from dissimilar captions and dissimilar images. ResNet-50 architecture is used for extracting features from the images and GloVe embeddings are used along with Gated Recurrent Unit (GRU) in Recurrent Neural Network (RNN) for text representation. MSCOCO evaluation server is used for evaluation of the machine generated caption for a given image.