Natural Language Processing Methods Research Articles

PENERAPAN ALGORITMA TEXTRANK DALAM MERANGKUM TEKS WORD DAN PDF

This review discusses the implementation of the TextRank algorithm for summarizing Word and PDF texts. Current technology enables rapid information growth but also raises issues regarding the lack of time to delve deeper into information. The TextRank algorithm is a web-based natural language processing method that employs an unsupervised approach and can be used to generate automatic text summaries. The search method used includes text preprocessing and the utilization of the TextRank algorithm. This article also explains how to use Google Colab and Google Drive to execute the TextRank algorithm for summarization. Steps for connecting Google Colab to Google Drive, installing Python libraries, retrieving data from Google Drive, and using the TextRank algorithm are also outlined in this article

ИСПОЛЬЗОВАНИЕ АЛГОРИТМОВ ОБРАБОТКИ ЕСТЕСТВЕННОГО ЯЗЫКА И МАШИННОГО ОБУЧЕНИЯ В СОВЕРШЕНСТВОВАНИИ УПРАВЛЕНЧЕСКИХ РЕШЕНИЙ

In the current conditions of growth and development of digital technologies, the latest tools and methods of supporting management decision-making are becoming increasingly important. In this paper, we will consider natural language processing algorithms that help analyze large amounts of unstructured text data and machine learning algorithms that analyze data and, based on them, perform forecasting, optimization and automation tasks. The paper analyzes the main tasks that can be solved when using machine learning and natural language processing methods together, namely: achieving high forecasting accuracy, processing and analyzing big data, automating routine tasks, managing risks, optimizing resource allocation, improving customer interaction. We analyzed the main software tools for the implementation of these tasks, the features of their application, settings and work parameters. Built a communication diagram based on the flow of messages between these tasks. Separately, the main software tools that are used to solve natural language processing and machine learning problems are analyzed in detail, their strengths and the possibilities of mutual integration or complement are described. A diagram of the IT infrastructure deployment was built based on the main software tools for natural language processing and machine learning methods.

Повышение эффективности государственной политики в сфере кинематографа с помощью машинного обучения

A study was conducted on the distribution data of a range of Russian national films from 2004 to September 2023 using machine learning methods, with successful and unsuccessful films and patriotic projects considered separately. The study utilized the ensemble machine learning model HistGradientBoostingClassifier and a sequential fully connected three-layer neural network based on the TensorFlow library, along with basic methods of natural language processing. It was found that patriotic films exhibit distribution characteristics lower than the market average and significantly lag behind successful Russian cinema projects at the box office. The study demonstrated the possibility of accurately predicting film box office receipts, distribution characteristics, as well as selecting project parameters and the composition of its creative team to enhance distribution results and increase the reach of target audiences. The study also illustrated the use of language models, specifically through film annotations, to create effective film content. A conclusion was drawn regarding the need for a set of measures to implement state policy in the field of education, information technology, culture, and art, focusing on the creation of unified media franchises and umbrella brands encompassing films, TV series, computer and online games, music, and other products. The implementation of the research results will enhance the effectiveness of public policy and the return on public and private- public investments in the creative industries sector, positioning cinema and the creative industry as self-sustaining and income-generating sectors of the economy. Leveraging rental data from other markets will facilitate the creation of content with higher demand globally, serving as a soft power and cultural influence tool. The results of the study can aid in project selection and film project development by the Russian Cinema Fund, the Ministry of Culture of the Russian Federation, private Russian investors and film studios, as well as authorities and organizations responsible for distributing Russian film content abroad.

Applications of Pruning Methods in Natural Language Processing

Applications of Pruning Methods in Natural Language Processing

Spiritual versus Religious: A Natural Language Processing Perspective

The method of Natural Language Processing (NLP) is used to analyze the literature on spirituality and religion. Specifically, the corpus produced in the spirituality/religion related scholarly literatures are used to train unsupervised neural network models (Word2Vec) that learn the extent to which words associate syntactically and semantically with one another. These models provide insights into what scholars mean when they use such terms as spiritual, religious, and spiritual-but-not-religious. For instance, they reveal that in the scholarly literature the term spiritual is used more often in contexts that describe an individual's experiences, emotions, and feelings, whereas the term religious is used more often in contexts that highlight an individual's identity and affiliations. The results also suggest that NLP methods may help scholars to perform reasonably meaningful vector operations (e. g., spiritual minus religious) that can be used to explore quickly and efficiently the syntactic and semantic patterns in a large corpus.

APLICAÇÃO DE TÉCNICAS DE INTELIGÊNCIA ARTIFICIAL PARA CLASSIFICAÇÃO DE FUGA AO TEMA EM REDAÇÕES

ABSTRACT: The process of manual correction of essays causes some difficulties, among which we point out the time spent for correction and feedback to the student. For institutions such as elementary schools, universities, and the National High School Exam in Brazil (Enem), such activity demands time and cost for the evaluation of the texts produced. Going off-topic is one of the items evaluated in the Enem essay that can nullify the whole essay produced by the candidate. In this context, the automatic analysis of essays with the application of techniques and methods of Natural Language Processing, Text Mining, and other Artificial Intelligence techniques has shown to be promising in the process of automated evaluation of written language. The goal of this research is to compare different AI techniques for the classification of going off-topic in texts and identify the one with the best result to enable a smart correction system for essays. Therefore, computer experiments were carried out to classify these texts to normalize, identify patterns, and classify the essays in 1,320 Brazilian Portuguese essays on 119 different topics. The results indicate that the Convolutional Neural Network classifier obtained greater gain concerning the other classifiers analyzed, both in accuracy and about the results of false positives, the precision of metrics, Recall, and F1-Score. In conclusion, the solution validated in this research contributes to positively impacting the work of teachers and educational institutions, by reducing the time and costs associated with the essay evaluation process.

APTAnet: an atom-level peptide-TCR interaction affinity prediction model.

The prediction of affinity between TCRs and peptides is crucial for the further development of TIL (Tumor-Infiltrating Lymphocytes) immunotherapy. Inspired by the broader research of drug-protein interaction (DPI), we propose an atom-level peptide-TCR interaction (PTI) affinity prediction model APTAnet using natural language processing methods. APTAnet model achieved an average ROC-AUC and PR-AUC of 0.893 and 0.877, respectively, in ten-fold cross-validation on 25,675 pairs of PTI data. Furthermore, experimental results on an independent test set from the McPAS database showed that APTAnet outperformed the current mainstream models. Finally, through the validation on 11 cases of real tumor patient data, we found that the APTAnet model can effectively identify tumor peptides and screen tumor-specific TCRs.

Supporting the working life exposome: Annotating occupational exposure for enhanced literature search.

An individual's likelihood of developing non-communicable diseases is often influenced by the types, intensities and duration of exposures at work. Job exposure matrices provide exposure estimates associated with different occupations. However, due to their time-consuming expert curation process, job exposure matrices currently cover only a subset of possible workplace exposures and may not be regularly updated. Scientific literature articles describing exposure studies provide important supporting evidence for developing and updating job exposure matrices, since they report on exposures in a variety of occupational scenarios. However, the constant growth of scientific literature is increasing the challenges of efficiently identifying relevant articles and important content within them. Natural language processing methods emulate the human process of reading and understanding texts, but in a fraction of the time. Such methods can increase the efficiency of both finding relevant documents and pinpointing specific information within them, which could streamline the process of developing and updating job exposure matrices. Named entity recognition is a fundamental natural language processing method for language understanding, which automatically identifies mentions of domain-specific concepts (named entities) in documents, e.g., exposures, occupations and job tasks. State-of-the-art machine learning models typically use evidence from an annotated corpus, i.e., a set of documents in which named entities are manually marked up (annotated) by experts, to learn how to detect named entities automatically in new documents. We have developed a novel annotated corpus of scientific articles to support machine learning based named entity recognition relevant to occupational substance exposures. Through incremental refinements to the annotation process, we demonstrate that expert annotators can attain high levels of agreement, and that the corpus can be used to train high-performance named entity recognition models. The corpus thus constitutes an important foundation for the wider development of natural language processing tools to support the study of occupational exposures.

Cyberbullying Detection System on Social Media Using Supervised Machine Learning

The use of digital and social media is growing every day as technology advances. People in the twenty-first century are growing up in a social media and internet-enabled society. Digital media offers a lot of opportunities, but people frequently tend to misuse them. On social networking sites, people spread anger toward a person. People are affected by cyberbullying in various ways. It has an impact on more than just health; numerous other factors put life in danger. Cyberbullying is a widespread modern phenomenon that people cannot completely avoid but can prevent. The author proposes a system for automatic cyberbullying detection and prevention using supervised machine learning. The system considers key characteristics of cyberbullying, such as the intention to harm, repeated behavior, and the use of abusive language. Support vector machines and logistic regression are employed to identify cyberbullying and related themes/categories such as race, physical, sexuality, and politics.This proposed method offers a novel theory for the detection of cyberbullying: texting has evolved over time due to changes in context usage, and language. In the dataset that includes tweets, Support Vector Machine (SVM), Naïve Bayes, and Logistic Regression (LR) models were tested along with different Natural Language Processing methods. The accuracy of the system is improved by sentiment analysis, N-gram analysis, and other non-traditional feature extraction methods like Term Frequency-Inverse Document Frequency (TF-IDF) and profanity detection.

Enhancing extractive text summarization using natural language processing with an optimal deep learning model

<abstract> <p>Natural language processing (NLP) performs a vital function in text summarization, a task targeted at refining the crucial information from the massive quantity of textual data. NLP methods allow computers to comprehend and process human language, permitting the development of advanced summarization methods. Text summarization includes the automatic generation of a concise and coherent summary of a specified document or collection of documents. Extracting significant insights from text data is crucial as it provides advanced solutions to end-users and business organizations. Automatic text summarization (ATS) computerizes text summarization by decreasing the initial size of the text without the loss of main data features. Deep learning (DL) approaches exhibited significant performance in abstractive and extractive summarization tasks. This research designed an extractive text summarization using NLP with an optimal DL (ETS-NLPODL) model. The major goal of the ETS-NLPODL technique was to exploit feature selection with a hyperparameter-tuned DL model for summarizing the text. In the ETS-NLPODL technique, an initial step of data preprocessing was involved to convert the input text into a compatible format. Next, a feature extraction process was carried out and the optimal set of features was chosen by the hunger games search optimization (HGSO) algorithm. For text summarization, the ETS-NLPODL model used an attention-based convolutional neural network with a gated recurrent unit (ACNN-GRU) model. Finally, the mountain gazelle optimization (MGO) algorithm was employed for the optimal hyperparameter selection of the ACNN-GRU model. The experimental results of the ETS-NLPODL system were examined under the benchmark dataset. The experimentation outcomes pointed out that the ETS-NLPODL technique gained better performance over other methods concerning diverse performance measures.</p> </abstract>

The Early Detection and Diagnosis of Mental Health Status Employing NLP-Based Methods With ML Classifiers

People now communicate on a variety of internet channels on a daily basis. Natural language processing techniques can be used to deduce users' mental states based on textual or spoken information they post on these sites. Using SMS to predict mental health issues is a proactive step toward better treatment. NLP is transforming the way that professionals in the field of mental health assess patients' freedom of expression in order to identify and diagnose mental illnesses. In addition to offering new avenues for research into human attitudes and behaviors, machine learning techniques can be used to recognize the telltale indications and symptoms of mental illness. In this study, we investigate various supervised classifier methods in depth and use natural language processing (NLP) to identify the mental health state from a text message. People experience suffering from several mental diseases, but the most common ones include PTSD, bipolar disorder, panic disorder, depression, stress, and anxiety. We used Decision Trees, Random Forest, K-Nearest Neighbors, BernoulliNB, and Logistic Regression to classify the data for this investigation. In comparison to the other four classifiers, Logistic Regression performs the best in our suggested strategy. The experimental result confirms that more accurate patient data classification can be achieved with the suggested methodology. With a 93 percent accuracy rate, the suggested model was demonstrated to be efficient.

Advanced Cyber Attack Detection Using Generative Adversarial Networks and NLP

A key difficulty in the ever-changing cybersecurity scene is the detection of sophisticated cyber-attacks. Because new threats are so much more sophisticated and difficult to detect, traditional tactics typically fail. A new technique to improving cyber-attack detection skills is explored in this study. It uses Generative Adversarial Networks (GANs) and Natural Language Processing (NLP). Using GANs' realistic data generation capabilities, possible attack paths are simulated, creating a strong dataset for training detection systems. At the same time, natural language processing (NLP) methods are used to decipher the mountain of textual information produced by cyberspace, including incident reports, communication patterns, and logs. Our approach is based on building a fake dataset using GANs that mimics the features of advanced cyberattacks. A detection model is then trained using this dataset. Simultaneously, we improve the detection model's capacity to spot intricate and nuanced assault patterns by processing and analysing text-based data using natural language processing approaches. We use a benchmark cybersecurity dataset to test the integrated method. The experimental findings show that our GAN-NLP based detection system outperforms existing systems, which have an average accuracy of 85.3%, by a wide margin. It achieves a recall of 93.2%, precision of 92.5%, and accuracy of 94.7%. These findings prove that GANs and NLP work well together to identify complex cyberattacks. Finally, GANs and NLP together provide a potent instrument for better cyber-attack detection. A scalable solution that can adapt to the ever-changing nature of cyber threats is offered by this integrated approach, which also increases detection accuracy and efficiency. Improving the models and investigating their use in a real-world cybersecurity setting will be the primary goals of future research.

Progressive Detection of Uncertainty in Natural Language Processing Using a Labeled Variable Dimension Kalman Filter

Owing to rapid advancements in information and communication technology, natural language processing (NLP) methods have attracted considerable attention. Estimating uncertainty is a well‐established problem in NLP. Developing systems capable of automatically detecting uncertain terms within a natural language is critical for developing effective text‐analysis algorithms. Consequently, several NLP tools have been developed for this purpose. However, there are several obstacles to the design of fast and effective NLP technologies that effectively analyze ambiguous terms in natural languages. This work focuses on a particular method for detecting uncertainty, termed progressive detection, which can be accomplished using a dynamic probabilistic graphical model to solve these challenges. We iteratively upgraded the Kalman Filter to Labeled Variable Dimension Kalman Filter (LVDKF) based on observations from real datasets. The LVDKF learns two basic features from positive and negative words. The formula was then derived using a dynamic detection system, designed based on conditional probability. Finally, we simulated progressive detection experimental conditions and assessed the LVDKF on two publicly available datasets. It performs better than the baseline methods in these tests, which shows that it is effective at detecting changes over time.

APPLICATION OF NATURAL LANGUAGE PROCESSING AND FUZZY LOGIC TO DISINFORMATION DETECTION

Natural language processing (NLP) is a field of computer science that is concerned with processing, collection and analysis of data encoded in natural language, such as speech, written text, online posts, etc. This paper explores the integration of Natural Language Processing (NLP) methods, specifically TF-IDF and n-gram analysis, with fuzzy logic rules employing Gaussian membership functions to detect disinformation in text data. The approach emphasizes reducing false positives by assessing the probability of disinformation rather than binary decisions, enhancing the accuracy and reliability of text analysis under informational uncertainty.

Automated formation of university R&D teams based on the competence selection algorithm

The paper discusses the possibility of increasing the effectiveness of strategic management of research and development activities in higher education institutions. To achieve this goal, the authors proposed an algorithm for selecting competencies, which allows for the distribution of university employees and students among research and development teams in accordance with their individual competencies for carrying out research and development work. The paper covers the problem statement of competence selection, an overview of natural language processing methods for algorithm development, problematic areas of the algorithm, and prospects for its implementation in university management.

Use of Natural Language Processing Methods in Teaching Turkish Proverbs and Idioms

Use of Natural Language Processing Methods in Teaching Turkish Proverbs and Idioms

The ENACT network is acting on housing instability and the unhoused using the open health natural language processing toolkit

Abstract Housing is an environmental social determinant of health that is linked to mortality and clinical outcomes. We developed a lexicon of housing-related concepts and rule-based natural language processing methods for identifying these housing-related concepts within clinical text. We piloted our methods on several test cohorts: a synthetic cohort generated by ChatGPT for initial infrastructure testing, a cohort with substance use disorders (SUD), and a cohort diagnosed with problems related to housing and economic circumstances (HEC). Our methods successfully identified housing concepts in our ChatGPT notes (recall = 1.0, precision = 1.0), our SUD population (recall = 0.9798, precision = 0.9898), and our HEC population (recall = N/A, precision = 0.9160).

Applied Linguistics Driven Deceptive Content Recognition using Single Valued Trapezoidal Neutrosophic Number with Natural Language Processing

Single valued neutrosophic number is a special case of single valued neutrosophic set and are of importance for neutrosophic multi-attribute decision making problem. A single valued neutrosophic number seems to define an ill-known quantity as a generalization of intuitionistic number. Applied linguistics in the context of Natural Language Processing (NLP) comprises the practical applications of linguistic approaches for addressing real time language processing issues. Social media become indispensable components in many people’s lives and have been growing rapidly. In the meantime, social networking media have become a widespread source of identity deception. Several social media identity deception cases have appeared presently. The research was performed to detect and prevent deception. Identifying deceptive content in natural language is significant to combat misrepresentation. Leveraging forward-thinking NLP methods, our model contextual cues analyze linguistic patterns, and semantic inconsistencies to flag possibly deceptive contents. By assimilating complex procedures for parameter optimization, feature extraction, and classification, the NLP focused on precisely recognizing deceptive content through different digital platforms, which contributes to the preservation of data integrity and the promotion of digital literacy. This study presents a Single Valued Trapezoidal Neutrosophic Number with Natural Language Processing for Deceptive Content Recognition (STVNNLP-DCR) technique on Social Media. The presented technique includes four important elements: preprocessing, GloVe word embedding, STVN classification, and Chicken Swarm Optimization (CSO) for parameter tuning. The preprocessing stage includes tokenization and text normalization, preparing text information for succeeding analysis. Then, GloVe word embedding represents the word in a continuous vector space, which captures contextual relationships and semantic similarities. The STVN classifier deploys the embedding to discern deceptive patterns within the text, leveraging its capability to effectively manage high-dimensional and sparse datasets. Moreover, the CSO technique enhances the hyperparameter of the STVN classifier, improving its generalization capabilities and performance. Empirical analysis implemented on varied datasets validates the efficacy of the presented technique in precisely recognizing deceptive content. Comparative studies with advanced approaches demonstrate high efficiency. The presented technique shows robustness against different forms of deceptive content, such as clickbait, misinformation, and propaganda

Performance Evaluation of Deep Learning Techniques in The Detection of IOT Malware

Internet of Things (IoT) equipment is rapidly being used in a variety of businesses and for a variety of reasons (for example, sensing and collecting data from the environment in both public and military settings). Because of their expanding involvement in a wide range of applications and their rising computational and processing capabilities, they are a viable attack target for malware tailored to infect specific IoT devices. This study investigates the potential of detecting IoT malware using different deep learning techniques: the classic feedforward neural network (FNN), convolutional neural networks (CNN), long short-term memory (LSTM), and recurrent neural networks (RNN). The proposed method analyses the execution operation codes of IOT app sequences using modern NLP (natural language processing) methods. The current work utilized an IoT application dataset with 500 malware (collected from the IOTPOT dataset) and 500 goodware samples to train the proposed algorithms. The trained model is tested against 2971 fresh IoT malware and goodware samples. The samples were input into deep learning models, and performance metrics were obtained. The results demonstrate that the RNN model had the best accuracy (99.19%) in detecting fresh malware samples. On the other hand, the results were compared by the time required for training; the CNN model shows that it could achieve high accuracy (98.05%) with less training time. A comparison with various deep learning classifiers demonstrates that the RNN and CNN techniques produce the best results.

DETECTION OF DEPRESSION AMONG SOCIAL NETWORK USERS USING MACHINE LEARNING METHODS

Statistical data provided by the FSBI “NMITSPN named after V.P. Serbsky” of the Ministry of Health of Russia indicate that depression, as a psychoemotional state, is the main cause of concern around the world, which in most cases leads to suicide, if not detected, and to a threat to others. Studies show that depression tends to have an impact on writing style and appropriate language use. The main purpose of the proposed study is to study user messages on the VKontakte social network and identify attributes that may indicate depressive symptoms of users. The article uses machine learning approaches (logistic regression, random forest, support vector machine, XGBoost) and natural language processing methods (removal of stop words, character deletion, tokenization, lemmatization) to prepare data and evaluate their effectiveness. The work demonstrated that the ability to search for depressed users with an accuracy of 77% using the XGBoost classifier. This method is combined with other linguistic functions (N-gram + TF-IDF) and LDA to achieve higher accuracy. In conclusion, the main conclusions of the study are formulated.