Natural Language Processing Concepts Research Articles

Using of NLP Methods in Intelligent Educational Systems

For the effective organisation of educational processes supported by relevant intelligent learning systems, it is important to choose the right technologies that would ensure individualisation of learning, adequate perception of learning content, and the so-called “understanding” of texts in Ukrainian provided by students (description of the solution to a task, answers provided in their own words, not selected from the test answer options, questions to the system, etc.), prototyping, constant iteration during natural language text recognition and processing, and maximum reliability and efficiency of learning processes. The purpose of the article is to study and analyse various methods of natural language processing, and the concept of NLP, and to consider common problems and prospects for developing a software product for processing Ukrainian-language text in online courses that support intelligent learning systems based on it. The research methods are the main methodological approaches and technological tools for analysing natural language texts in intelligent educational systems and developing a system for supporting NLP (Natural Language Processing) technology in the linguistic analysis of texts in Ukrainian. Such methods include, in particular: systemic and comparative analyses to identify the features of intelligence and information (with elements of intellectualisation) systems; the method of expert evaluation, which involves the study of literary sources and information resources, interviews and surveys of experts, as well as the processes of developing and testing intelligent and information systems. The novelty of the study is the analysis of modern technologies for the development of online educational process support systems through the organisation of processes of perception of information provided by students in natural language, the results of which can be used in the development of their software product to support the educational process in Ukrainian, ensuring the improvement of learning efficiency through the use of NLP technology in the process of studying the relevant academic content. Conclusions. The paper analyses modern NLP methods. The analysis has led to the selection of tokenisation, normalisation, stemming and lemmatisation methods for use in intelligent learning systems in the linguistic analysis of the so-called “free” communication in the natural (Ukrainian) language of students in the process of studying the educational content of online courses. During the tokenisation of Ukrainian-language texts, we solved such problems as eliminating so-called “merged” tokens, correcting spelling mistakes, identifying common prefixes in compound words and their impact on the semantics of the corresponding lexemes, identifying common prefixes in abbreviations, and bringing words to their normal form. Lemmatisation is especially important for the Ukrainian language (with its large number of cases of nouns, adjectives, word forms, etc.) and it requires the use of specially compiled dictionaries of the subject area under consideration. In these dictionaries, word forms are presented in the forms of lemmas (i.e., nouns are presented in the nominative case).

Study on Intelligent Scoring of English Composition Based on Machine Learning from the Perspective of Natural Language Processing

Knowledge management is crucial to the teaching and learning process in the current era of digitalization. The idea of "learning via working together" is making Natural Language Processing a popular tool to improve the learning process based on the intelligent system for evaluating the composition. English language learning is highly dependent on the composition written by the students under various topics. Teachers are facing huge difficulties in the evaluation of the composition as the level of writing by the students will vary for individual. In this research, Natural Language Processing concept is utilized for getting trained with the student's writing skills and Multiprocessor Learning Algorithm (MLA) combined with Convolutional Neural Network (CNN) (MLA-CNN) for evaluating the composition and declaring the scores for the students. The model's composition scoring rate is validated using a range of learning rate settings. Some theoretical notions for smart teaching are proposed, and it is hoped that this automatic composition scoring model would be used to grade student writing in English classes. When applied to the automatic scoring of students' English composition in schools, the suggested composition scoring system trained by the MLP-CNN has great performance and lays the groundwork for the educational applications of ML inside AI. The study results proved that the proposed model has provided an accuracy of 98%.

CHATBOT USING LARGE LANGUAGE MODEL

The concept of Natural Language Processing has seen a remarkable advancement in the recent years. This remarkable advancement was particularly with the development of Large Language Models (LLM). Large Language Models are used to develop a human like conversations. This LLM is a part of Natural Language Processing which focuses on enabling computers to understand, interpret, and generate human language. The existing system of chatbots does not generate human like responses. The proposed system of chatbots uses the power of Large Language Models to generate more human like responses, providing the conversation in a natural way. By genereating human like respones, it will be in a natural way for the user. To enhance user experience, the chatbot uses a dynamic learning mechanism, by which it continuously adapt to user preferences and evolving conversational patterns. This system uses feedbacks from the users to refine its responses everytime.Moreover, the chatbot is designed with a multi-turn conversational context awareness, allowing it to maintain coherence and relevance throughout extended dialogues.The effectiveness of the proposed chatbot is evaluated through user testing, comparing its performance against traditional rule-based chatbots and existing conversational agents. This report explains about the usage of Large Language Models in the design and implementation of conversational chatbots. The outcomes of this research contribute to the advancement of intelligent chatbot systems, demonstrating the potential of large language models to significantly enhance conversational AI applications.

Enhancing Cross-Linguistic Image Caption Generation with Indian Multilingual Voice Interfaces using Deep Learning Techniques

The Multilingual Voice-Based Image Caption Generator (MVBICG) is a versatile tool with numerous applications spanning communications, culture preservation, business, and technology, making it indispensable in the interconnected world. The task of image caption generation combines computer vision and NLP (natural language processing) concepts, enabling the system to understand the details or complexities of the image context and describe them in natural language. Image descriptions serve as an invaluable solution for visually impaired individuals. The MVBICG system is designed to provide real-time image descriptions in the form of voice in multiple languages as per user requirements. With the use of an MVBICG, the descriptions can be obtained as a voice output in different languages. Converting a voice into multiple languages with the help of the Google Translate API is often referred to as “multilingual voice conversion” or “multilingual speech synthesis." It leverages the latest advancements in deep learning, particularly convolutional neural networks (CNNs) for image feature extraction and recurrent neural networks (RNNs) with attention mechanisms for natural language generation. In the future, image processing is expected to take center stage as a critical research domain primarily dedicated to the preservation and protection of human lives. The MVBICG demonstrates remarkable performance with BLEU scores of 0.483601 for BLEU-1 and 0.320112 for BLEU-2, indicating its proficiency in generating precise and contextually relevant image captions. These scores further underscore its value in bridging language barriers and enhancing accessibility, highlighting its potential for broader societal impact. Additionally, the system's training progress is illustrated by a loss plot, showing the convergence of the model over time. As image processing continues to advance, the MVBICG emerges as a pivotal research domain, focusing on the preservation and safeguarding of human lives through advanced technologies.

Gesture Controlled Virtual Mouse with Voice Assistant

The usage of hand gestures to operate digital devices has grown in recent years, and developing accurate gesture recognition systems using computer vision technology is becoming easier. Voice assistants have also become more common, with developments in artificial intelligence (AI) and natural language processing (NLP) allowing for more accurate voice recognition. Our goal is to create a system which combines both gesture-controlled mouse functionality and voice assistant to execute things that a regular mouse and keyboard can. The GCVA (Gesture Controlled Voice Assistant) comes with a gesture-controlled mouse and a voice assistant. A gesture-controlled mouse uses OpenCV and Media-Pipe, whereas a voice assistant uses AI and NLP concepts to recognize the voice commands given by the user. The system should be used by performing hand gestures and executing the right click, left click, drag, drop, volume control and computer cursor functions, thereby omitting the need for a physical mouse. The success of this system is observed in terms of practicality, stability, and compatibility with physical mechanisms. The voice assistant works and responds to pre-defined commands present in the system. The GCVA system was tested by four different users in order to generate the accuracy scores of mouse gestures and response time graphs for voice assistant. The system yields an accuracy rate of 98%. The system's accuracy and usability showed that hand gesture detection and voice assistants have the potential to replace traditional input devices in the future.

An Enhanced Approach to Recommend Data Structures and Algorithms Problems Using Content-based Filtering

Data Structures and Algorithms (DSA) is a widely explored domain in the world of computer science. With it being a crucial topic during an interview for a software engineer, it is a topic not to take lightly. There are various platforms available to understand a particular DSA, several programming problems, and its implementation. Hacckerank, LeetCode, GeeksForGeeks (GFG), and Codeforces are popular platforms that offer a vast collection of programming problems to enhance skills. However, with the huge content of DSA available, it is challenging for users to identify which one among all to focus on after going through the required domain. This work aims to use a Content-based filtering (CBF) recommendation engine to suggest users programming-based questions related to different DSAs such as arrays, linked lists, trees, graphs, etc. The recommendations are generated using the concept of Natural Language Processing (NLP). The data set consists of approximately 500 problems. Each problem is represented by the features such as problem statement, related topics, level of difficulty, and platform link. Standard measures like cosine similarity, accuracy, precision, and F1-score are used to determine the proportion of correctly recommended problems. The percentages indicate how well the system performed regarding that evaluation. The result shows that CBF achieves an accuracy of 83 %, a precision of 83 %, a recall of 80%, and an F1-score of 80%. This recommendation system is deployed on a web application that provides a suitable user interface allowing the user to interact with other features. With this, a whole E-learning application is built to aid potential software engineers and computer science students. In the future, two more recommendation systems, Collaborative Filtering (CF) and Hybrid systems, can be implemented to make a comparison and decide which is most suitable for the given problem statement.

Improving the accuracy of automated gout flare ascertainment using natural language processing of electronic health records and linked Medicare claims data.

We aimed to determine whether integrating concepts from the notes from the electronic health record (EHR) data using natural language processing (NLP) could improve the identification of gout flares. Using Medicare claims linked with EHR, we selected gout patients who initiated the urate-lowering therapy (ULT). Patients' 12-month baseline period and on-treatment follow-up were segmented into 1-month units. We retrieved EHR notes for months with gout diagnosis codes and processed notes for NLP concepts. We selected a random sample of 500 patients and reviewed each of their notes for the presence of a physician-documented gout flare. Months containing at least 1 note mentioning gout flares were considered months with events. We used 60% of patients to train predictive models with LASSO. We evaluated the models by the area under the curve (AUC) in the validation data and examined positive/negative predictive values (P/NPV). We extracted and labeled 839 months of follow-up (280 with gout flares). The claims-only model selected 20 variables (AUC = 0.69). The NLP concept-only model selected 15 (AUC = 0.69). The combined model selected 32 claims variables and 13 NLP concepts (AUC = 0.73). The claims-only model had a PPV of 0.64 [0.50, 0.77] and an NPV of 0.71 [0.65, 0.76], whereas the combined model had a PPV of 0.76 [0.61, 0.88] and an NPV of 0.71 [0.65, 0.76]. Adding NLP concept variables to claims variables resulted in a small improvement in the identification of gout flares. Our data-driven claims-only model and our combined claims/NLP-concept model outperformed existing rule-based claims algorithms reliant on medication use, diagnosis, and procedure codes.

PolyBERT: a chemical language model to enable fully machine-driven ultrafast polymer informatics

Polymers are a vital part of everyday life. Their chemical universe is so large that it presents unprecedented opportunities as well as significant challenges to identify suitable application-specific candidates. We present a complete end-to-end machine-driven polymer informatics pipeline that can search this space for suitable candidates at unprecedented speed and accuracy. This pipeline includes a polymer chemical fingerprinting capability called polyBERT (inspired by Natural Language Processing concepts), and a multitask learning approach that maps the polyBERT fingerprints to a host of properties. polyBERT is a chemical linguist that treats the chemical structure of polymers as a chemical language. The present approach outstrips the best presently available concepts for polymer property prediction based on handcrafted fingerprint schemes in speed by two orders of magnitude while preserving accuracy, thus making it a strong candidate for deployment in scalable architectures including cloud infrastructures.

Development of natural language processing on morphologybased Minangkabau language stemming algorithm

Minangkabau language (ML) is one of the daily communication tools used by the people of West Sumatra, Indonesia. ML is a challenge in communicating. The ML language translation process is necessary to facilitate communication. This study aims to build a translation system for ML into Indonesian by developing the concept of natural language processing (NLP). NLP development adopts the performance of morphology-based Minangkabau language stemming algorithm (MLSA) which can separate basic words with affixes and endings. The research dataset adopts 600 basic ML words sourced from the big Minangkabau dictionary. The results of this study provide analytic output that can translate ML into Indonesian well. These results are presented based on the testing process on basic word input with an accuracy rate of 97.16% and based on text documents of 91.65%. Thus, the MLSA performance process presents the accuracy of the translation process. Based on these results, this research contributes to developing a stemming algorithm model in carrying out the process of removing prefixes, inserts, and suffixes in the Minangkabau language. Overall, this research can be useful as a tool for translating the ML into Indonesian.

SOCIOLOGICAL ANALYSIS OF THE ROLE OF THE MEDIA IN THE FORMATION OF TRUST IN ARTIFICIAL INTELLIGENCE

This article discusses the issue related to the sociological analysis of the role of the media in the formation of trust in artificial intelligence. The author studied the fields of application of artificial intelligence, its relationship with the media, as well as the concept of natural language processing. Natural language processing is one of the newest technologies of artificial intelligence application and can be integrated by mass media into the process of writing reports and texts. An important place in this work is given to the author’s research conducted in August 2022. The purpose of this study was to identify the relationship between how people relate to the media and artificial intelligence, and how they evaluate headlines created by journalists or AI. The results of the study revealed similarities between texts compiled by artificial intelligence and texts written by journalists. In the final part of the article, the authors formulated the main recommendations for increasing confidence in artificial intelligence in the context of media influence on it. The key conclusion of this scientific work is the following aspect: with automatic text generation, consumers’ perception of the quality of news plays an important role in establishing the relationship between people and artificial intelligence.

Lim, Saeyi;Han, SunKwan

The purpose of this study is to design and develop an education program about natural language processing (NLP) in the school curriculum, understand, experience, and utilize the concept of natural language processing, and considers related ethical issues. Though NLP is a major learning component of artificial intelligence (AI) education, few educational programs have been researched about NLP education. Accordingly, the development direction of the NLP education program was set by analyzing the relevant literature, curriculum, and textbook contents, and the results of expert validity analysis were reviewed. According to the reviewed development direction, the 22nd session of education programs and materials was developed. As a result of expert validity analysis, the educational program’s content validity was assessed. The program was then augmented and updated by the open response. It is expected that the developed curriculum-based NLP education program will be used as data for realizing AI education in the school field.

MODELS OF NATURAL LANGUAGE PROCESSING FOR IMPROVING SEMANTIC SEARCH RESULTS

Most people rely on search engines to get and share information from all sorts of resources. All totals returned by search engines are not always important because they are drawn from heterogeneous data sources. Moreover, it is not easy for a user to prove that the acquired results are relevant for the request. As a result, the semantic network plays a significant role in interpreting the relevance of search results. This paper proposes a new method for searching for appropriate documents using the semantic web, based on the concept of natural language processing (NLP). In the proposed system, NLP is used to analyze a user request in terms of parts of speech. The extracted definitions are compared with a domain dictionary to identify the appropriate domain of the user's interest. The retrieved user request papers are examined with natural language processing support to identify the respective domain. Keywords: semantic search engine, natural language processing, context analysis, information retrieval systems, graphematic analysis, ontology

Improving Portfolio Performance via Natural Language Processing Methods

Recent natural language processing (NLP) breakthroughs have proven effective for addressing many language-directed tasks, such as completing sentences and addressing search queries. This technology has been successfully implemented by tech firms including Google and others. An important element consists of language embeddings linked to pretraining systems. This article describes NLP concepts and their application to portfolio models via a modern version of sentiment analysis. The authors demonstrate the advantages of employing information from Twitter along with the NLP for constructing a portfolio of stocks, especially during unusual events such as the COVID-19 pandemic.

A dual framework for implicit and explicit emotion recognition: An ensemble of language models and computational linguistics

One of the research domains in the field of sentiment analysis is automatic emotion recognition in texts which is a worthy topic in human-computer interaction. Text processing has always faced many challenges. The main one is the structural and semantic differences of sentences which have had a significant impact on the malfunction of auto-recognition systems. This problem becomes more prominent in short texts in which words and their concurrences are limited and insufficient. As a result of this, word frequency and TF-IDF weighing cannot well represent the relationship between words and the appropriate feature vector, leading to an undesirable accuracy of emotion recognition. Thus, different strategies should be applied to improve the feature vector and to formulate the features properly. The desired strategy should be able to identify the words that can distinguish between classes well and also to find the relationships between words and meaningful phrases using natural language processing concepts. In this paper, a combination of emotional models, categorical and hierarchical, are used for an emotional text recognition which could discover simultaneously explicit and implicit emotion in a short text. Our approach called DuFER, proposed a weighed method which improves the feature vector using language models and computational linguistics through applying a modified TF-IDF weighing to words as well as Maximum Likelihood Estimation weighing to expressions. Four implicit and explicit emotion datasets are used for the experiments. The results show that the accuracy of both implicit and explicit emotion recognition has increased and DuFER is actually the first successful dual framework in recognizing implicit and explicit emotions from text.

AI-Driven Clinical Decision Support: Enhancing Disease Diagnosis Exploiting Patients Similarity

Detecting diseases at early stage can help to overcome and treat them accurately. A Clinical Decision Support System (CDS) facilitates the identification of diseases together with the most suitable treatments. In this paper, we propose a CDS framework able to integrate heterogeneous health data from different sources, such as laboratory test results, basic information of patients, health records and social media data. Using the data so collected, innovative machine learning and deep learning approaches can be employed. A neural network model for predicting patients’ future health conditions is proposed. The approach employs word embedding to model the semantic relations of hospital admissions, symptoms and diagnosis, and it introduces a mechanism to measure the relationships of different diagnosis in terms of symptoms similarity to exploit for the prediction task. Several CDSs, including diagnostic decision support systems for inferring patient diagnosis, have been proposed in the literature. However, these methods typically focus on a single patient and apply manually or automatically constructed decision rules to produce a diagnosis. Even worst, they consider only a single medical condition, whereas it is not uncommon that a patient has more than one medical condition at the same time. The novelty of the proposed approach is the combination of supervised and unsupervised artificial intelligence methods allowing to combine several and heterogeneous data sources related to a multitude of patients and concerning different medical conditions. Furthermore, with respect to previous approaches, the diagnosis prediction problem is formulated to predict the exact diagnosis in terms of semantic meaning by exploiting Natural Language Processing concepts. Experimental results, performed on a real-world EHR dataset, show that the proposed approach is effective and accurate and provides clinically meaningful interpretations. The obtained outcomes are promising for future extensions of the framework that could be a valuable means for automatic inferring disease diagnosis.

Natural Language Processing: Practical Applications in Medicine and Investigation of Contextual Autocomplete.

Natural language processing (NLP) is the task of converting unstructured human language data into structured data that a machine can understand. While its applications are far and wide in healthcare, and are growing considerably every day, this chapter will focus on one particularly relevant application for healthcare professionals-reducing the burden of clinical documentation. More specifically, the chapter will discuss two studies (Gopinath et al., Fast, structured clinical documentation via contextual autocomplete. arXiv: 2007.15153, 2020; Greenbaum et al., Contextual autocomplete: a novel user interface using machine learning to improve ontology usage and structured data capture for presenting problems in the emergency department, 2017) that have implemented contextual autocompletion in electronic medical records and their promising results with regards to time saved for clinicians. The goals of this chapter are to introduce to the curious healthcare provider the basics of natural language processing, zoom into the use case of contextual autocomplete for electronic medical records, and provide a hands-on tutorial that introduces the basic NLP concepts required to build a model for predictive suggestions.

Sentiment Analysis-Enhancements and Applications

The concept of Natural Language Processing that deals with problems of identifying the sentiment from the voice or text of a speaker or writer and then use that analysis further for making predictions, market survey, customer service, product satisfaction, precision targeting etc. is called Sentiment analysis. From one viewpoint, it is an abstract evaluation of something dependent on close to home observational experience. It Is mostly established in target realities and incompletely governed by feelings. Then again, a sentiment can be deciphered as a kind of measurement in the information in regards to a specific subject. It is a lot of markers that mix present a point of view, i.e., perspective for the specific issue. So as to enhance the accuracy of sentiment analysis/classification, it is imperative to appropriately recognize the semantic connections between the various words and phrases that are describing the subject or aspect. This can be done by applying semantic analysis with a syntactic parser and supposition vocabulary. This research will discuss different sets of approaches for application or domain specific problems and then compare them to obtain the best possible approaches to the problem of sentiment analysis.

Logistics and Freight Transportation Management: An NLP based Approach for Shipment Tracking

Tracking and tracing systems have become basic services for most logistics companies and are particularly essential for the shipping and logistics industry. Dynamic logistics management today need constant supervision and management of continuously-changing supply chains that motivate the necessity of goods-centric logistics monitoring and tracking, which guarantees a chance to improve transparency and control of a company’s multiple logistical activities. However, operational inefficiencies due to the conventional monitoring system for the supply chain management can also result in sales loss, higher cost, poor customer service–and eventually lower profits. Based on research literature, this paper aims to provide a novel approach for tracking and tracing shipment in a logistics organisation by implementing deep natural language processing concepts. The study aims to allow the stakeholders to think in new ways in their organisation and helping them to have a powerful influence on tracking and tracing to make the best decision possible at the right time. The proposed method is compared based on the accuracy of identifying the query, and results are significantly acceptable. This study is of related interest to researchers, academicians, and practitioners.

Improving the Efficiency of Clinical Trial Recruitment Using an Ensemble Machine Learning to Assist With Eligibility Screening.

ObjectiveEfficiently identifying eligible patients is a crucial first step for a successful clinical trial. The objective of this study was to test whether an approach using electronic health record (EHR) data and an ensemble machine learning algorithm incorporating billing codes and data from clinical notes processed by natural language processing (NLP) can improve the efficiency of eligibility screening.MethodsWe studied patients screened for a clinical trial of rheumatoid arthritis (RA) with one or more International Classification of Diseases (ICD) code for RA and age greater than 35 years, from a tertiary care center and a community hospital. The following three groups of EHR features were considered for the algorithm: 1) structured features, 2) the counts of NLP concepts from notes, 3) health care utilization. All features were linked to dates. We applied random forest and logistic regression with least absolute shrinkage and selection operator penalty against the following two standard approaches: 1) one or more RA ICD code and no ICD codes related to exclusion criteria (ScreenRAICD1 +EX) and 2) two or more RA ICD codes (ScreenRAICD2). To test the portability, we trained the algorithm at one institution and tested it at the other.ResultsIn total, 3359 patients at Brigham and Women’s Hospital (BWH) and 642 patients at Faulkner Hospital (FH) were studied, with 461 (13.7%) eligible patients at BWH and 84 (13.4%) at FH. The application of the algorithm reduced ineligible patients from chart review by 40.5% at the tertiary care center and by 57.0% at the community hospital. In contrast, ScreenRAICD2 reduced patients for chart review by 2.7% to 11.3%; ScreenRAICD1+EX reduced patients for chart review by 63% to 65% but excluded 22% to 27% of eligible patients.ConclusionThe ensemble machine learning algorithm incorporating billing codes and NLP data increased the efficiency of eligibility screening by reducing the number of patients requiring chart review while not excluding eligible patients. Moreover, this approach can be trained at one institution and applied at another for multicenter clinical trials.

I-Pulse: A NLP based novel approach for employee engagement in logistics organization

Although most logistics and freight forwarding organizations, in one way or another, claim to have core values. The engagement of employees is a vast structure that affects almost every part of the company’s core environmental values. There is little theoretical knowledge about the relationship between firms and the engagement of employees. Based on research literature, this paper aims to provide a novel approach for insight around employee engagement in a logistics organization by implementing deep natural language processing concepts. The artificial intelligence enabled solution named Intelligent Pulse (i-Pulse) can evaluate hundreds and thousands of pulse survey comments and provides the actionable insights and gist of employee feedback. i-Pulse allows the stakeholders to think in new ways in their organization, helping them to have a powerful influence on employee engagement, retention, and efficiency. This study is of corresponding interest to researchers and practitioners.