Natural Language Processing Technology Research Articles

Research on the Development and Application of ChatGPT

With the rapid development of AI technology, AI is increasingly capable of performing complex computational and creative tasks. The combination of automation, machine learning, and natural language processing technologies provides powerful capabilities for AI systems. This project uses ChatGPT as an example to explore in depth the dynamic cross-cutting features of AI and its impact on human behavior. This paper utilizes a multifaceted research methodology, including case studies, and focuses on ChatGPT as a relevant case study. The findings of this project not only deepen the understanding of ChatGPT's functions, but also have implications for the interaction between AI and human intelligence. This research emphasizes the need for consultative and ethical architectures and the importance of using them in a balanced way in this ever-changing field of technology. This thesis offers valuable thoughts on the coexistence of AI and human endeavors.

A tree-based corpus annotated with Cyber-Syndrome, symptoms, and acupoints

Prolonged and over-excessive interaction with cyberspace poses a threat to people’s health and leads to the occurrence of Cyber-Syndrome, which covers not only physiological but also psychological disorders. This paper aims to create a tree-shaped gold-standard corpus that annotates the Cyber-Syndrome, clinical manifestations, and acupoints that can alleviate their symptoms or signs, designating this corpus as CS-A. In the CS-A corpus, this paper defines six entities and relations subject to annotation. There are 448 texts to annotate in total manually. After three rounds of updating the annotation guidelines, the inter-annotator agreement (IAA) improved significantly, resulting in a higher IAA score of 86.05%. The purpose of constructing CS-A corpus is to increase the popularity of Cyber-Syndrome and draw attention to its subtle impact on people’s health. Meanwhile, annotated corpus promotes the development of natural language processing technology. Some model experiments can be implemented based on this corpus, such as optimizing and improving models for discontinuous entity recognition, nested entity recognition, etc. The CS-A corpus has been uploaded to figshare.

SMART CAMPUS BOT

A chat-bots aims to make a conversation between both human and machine.The machine has been embedded knowledge to identify the sentences and making a decision itself as a response to answer a question. Chat-bots will becompletely based on a text-based user interface, allowing the user to type commands and receive text as well as text to speech response.Chat-bots are usually stateful services, remembering previous commands in order to provide functionality. It can be utilized securely by an even largeraudience when chat-bots technology is integrated with popular web services. The college inquiry chat-bots will be built using artificial algorithms that analyze user's queries and understand user's message.The response principle is matching the input sentence from a user. The User can ask the question any college-related activities through the chat-bot without physically available to the college for inquiry. The System analyses the question and then answers to the user. With the help of artificial intelligence, the system answers the query asked by the students. The system replies using an effective Graphical User Interface as if a real person is talking to the user. The user just has to register himself to the system and has to login to the system. The chatbots consists of core and interface that is accessing in the Telegram by Ngrok. Natural language processing technologies are used for parsing, tokenizing, stemming and filtering the content of the complaint. Keywords— “Anaconda Prompt”,“College System,“Placement Information”, “Python”, “Rasa”, “Telegram User Interface”

Video Summarization for Marathi Language

The Video Summarization Platform using Python Flask is a comprehensive tool designed to summarize Marathi and English videos while providing summaries in Hindi, Marathi, and English languages. Leveraging machine learning and natural language processing (NLP) techniques, this platform offers a sophisticated solution for efficiently extracting key information from videos. The platform begins by transcribing the audio content of the video into text using automatic speech recognition (ASR) technology. This transcription process ensures that the platform can accurately analyze and summarize the video's content. Next, the text is translated into the target languages, namely Hindi, Marathi, and English, enabling users from diverse linguistic backgrounds to access the summarized content. To generate concise and informative summaries, advanced NLP algorithm is applied. This algorithm analyze the transcribed text to identify the most significant phrases, sentences, and concepts. By considering factors such as keyword frequency, semantic relevance, and context, the platform effectively distils the video's content into digestible summaries. Additionally, machine learning models are employed to classify the type of video content. These models are trained on diverse datasets encompassing various video genres and topics. By recognizing patterns and features within the video content, the platform can accurately categorize videos into distinct types, such as news, interviews, tutorials, or entertainment. The platform's user interface, powered by Python Flask, offers a seamless experience for users to upload videos, select their preferred language for summarization, and receive concise summaries in their chosen languages. The intuitive design ensures accessibility and ease of use, catering to both novice and advanced users. Overall, the Video Summarization Platform serves as a valuable resource for individuals seeking efficient ways to consume multimedia content. Whether for educational, informational, or entertainment purposes, this platform empowers users to access summarized video content in multiple languages, facilitated by cutting-edge machine learning and NLP technologies. Key Words: Transcription, Marathi-speaking users, Marathi YouTube videos, video content, transcription, summary, translation, Natural Language Toolkit (NLTK), content comprehension, user interaction data, past summaries, recommendation

Leveraging mobile NER for real-time capture of symptoms, diagnoses, and treatments from clinical dialogues

In the dynamic world of healthcare technology, efficiently and accurately extracting medical data from physician-patient conversations is vital. This paper presents a new approach in healthcare technology, employing Natural Language Processing (NLP) to identify and extract critical information from doctor-patient conversations on mobile devices. Unlike traditional methods that rely on Electronic Health Records, our novel application enables the extraction of symptoms, diagnoses, and treatments directly on a mobile device during medical consultations, significantly enhancing patient privacy. We managed to integrate both Bidirectional Encoder Representations from Transformers (BERT) models and optimized Large Language Models (LLMs) on a mobile device without compromising performance significantly. Our findings reveal that the BERT model attained an F1-score of 85.1%, while FLERT and its compressed variant DistilFLERT showed superior performance. The FLAN-T5 model outperformed all models we tested with scores up to 92.7%. These results highlight the efficacy of leveraging advanced NLP and LLM technologies in healthcare environments on a mobile device, offering a promising direction for accessible and efficient patient care.

Analysis of the Pros and Cons of ChatGPT for College Paper Writing

Chat GPT is a chatbot system that utilizes natural language processing and deep learning technologies to mimic human discussions, potentially reaching or exceeding human intelligence. Recently, Chat GPT has shown significant advancements in various areas, particularly in natural language comprehension, automated question answering, and intelligent customer support, demonstrating promising possibilities. As Chat GPT technology advances and becomes popularity, there are concerns over whether it may bring an end to traditional university essays. This study utilizes a questionnaire-based research methodology to examine the advantages and disadvantages of including ChatGPT in the dissertation writing process for university students. The study seeks to investigate the variables affecting students' inclination to utilize these items, including gender, age, field of study, and prior exposure to comparable products, as well as the potential repercussions on academic integrity.

Prediction mechanism of depression tendency among college students under computer intelligent systems

Abstract In response to the existing problems in the prediction accuracy, data collection, real-time monitoring, and consideration of factors leading to depression in the current mechanism for predicting depression among college students, this article used computer intelligent systems to study the prediction mechanism of depression among college students. This article conducted a survey on students at University A using a survey questionnaire to understand the main reasons that affect their tendency to develop depression. It processed and analyzed the data using the Beck Depression Scale and Statistical Product and Service Solution 21.0 (SPSS 21.0). Meanwhile, natural language processing techniques in computer intelligence systems can be utilized. This article combines emotional dictionaries and word frequency-inverse document frequency to construct a prediction mechanism model for depression tendencies among college students, improving the accuracy of predicting student depression tendencies. The experiment shows that the average accuracy of the depression tendency prediction mechanism model constructed based on Natural Language Processing technology after 50 experiments was 97.02%, which was 5.33% higher than the model constructed based on neural network calculations. Overall, research on the prediction mechanism of depression tendency among college students based on computer intelligence systems can provide more effective mental health support and intervention measures for schools, helping students improve their psychological state, academic achievement, and quality of life.

AI Call Assistant – Extractive Summarization of Call Recordings

In today's fast-paced business landscape, effective communication is a key for success. With the increasing volume of phone calls in various industries, there is a growing need for efficient call management and analysis tools. The AI Call Assistant project aims to address this need by leveraging artificial intelligence (AI) technology to summarize call recordings, enhancing productivity and decision-making processes. The core functionality of this project revolves around its ability to process audio data from call recordings and extract key insights and information. Using advanced natural language processing (NLP) techniques, the system identifies important topics, sentiments, and actions discussed during the call. The call transcripts attained from call recording pose unique challenges that are not adequately addressed by most open-source automatic text summarizers. This project aims to contribute to the field of artificial intelligence by providing efficient and effective methods to recognize call recording and summarize, offering a valuable tool for extracting insights from spoken audio efficiently.Top of Form This research aligns with the broader fields of natural language processing (NLP), machine learning, and artificial intelligence (AI), particularly in the domain of speech recognition and understanding. It also intersects with communication technology and data analytics, focusing on optimizing call management processes and extracting actionable insights from conversational data. Moreover, it aligns with the goal of improving efficiency and decision-making in various industries through the application of AI-driven solutions. Overall, this project offers a novel approach to improving call management processes through AI-driven extractive summarization, contributing to advancements in natural language processing and communication technologies. Key Words: Artificial Intelligence, BERT, Textual Summarization, Transformers, Natural Language Processing, Speech Recognition, Speech-To-Text.

Awareness about the Agriculture Needs through Interactive Web Application for Farmers

Agriculture occupies an important position in the Indian economy. Indian farmers today are facing the problem of low income due to the lack of information about government schemes, fertilizers, farming equipment, etc. Some smallholders Agriculture occupies an important position in the Indian economy. Indian farmers today are facing the problem of low income due to the lack of information about government schemes, fertilizers, farming equipment, etc. Some smallholders and marginalized farmers have low awareness as most of them live in remote areas and don't have access to information about soil properties, seeds, recently used tools, fertilizers, etc. The document proposes an intelligent, portable system that uses natural language processing Agriculture occupies an important position in the Indian economy. Indian farmers today are facing the problem of low income due to the lack of information about government schemes, fertilizers, farming equipment etc. Some smallholders and marginalized farmers have low awareness as most of them live in remote areas and don't have access to information about soil properties, seeds, recently used tools, fertilizers, etc. The document proposes an intelligent, portable system that uses natural language processing methods to help farmers use different farming methods, and further help them to answer their queries and solve their basic and intermediate-level doubts using a chatbot which will save their time. To meet all the requirements of farmers, a chatbot is proposed using natural language processing technology. The system will act as an interactive virtual assistant for farmers, answering all queries related to agriculture. This paper will go through the implementation of the chatbot using the chatterweb bot libraries and the Django framework

A COMPREHENSIVE REVIEW OF THE IMPACT OF ARTIFICIAL INTELLIGENCE ON MODERN ACCOUNTING PRACTICES AND FINANCIAL REPORTING

The rapid integration of artificial intelligence (AI) into various industries has catalyzed transformative changes in accounting practices and financial reporting. This comprehensive review explores the multifaceted impact of AI on modern accounting, shedding light on the ways in which advanced technologies are reshaping traditional financial processes. The implementation of AI in accounting has led to increased efficiency and accuracy in routine tasks. Automation of data entry, reconciliation, and routine bookkeeping activities has not only reduced the risk of human errors but has also allowed accountants to redirect their focus towards more strategic and value-added activities. Machine learning algorithms are adept at analyzing vast datasets, identifying patterns, and predicting financial trends, enabling accountants to make more informed decisions. Furthermore, AI has revolutionized the auditing process, enhancing the detection of anomalies and fraudulent activities. Through continuous monitoring and analysis of financial data, AI-powered systems can quickly identify discrepancies, mitigating risks and ensuring the integrity of financial reports. This has profound implications for regulatory compliance and corporate governance, fostering greater transparency and accountability. In the realm of financial reporting, AI has played a pivotal role in improving the quality and timeliness of information. Natural Language Processing (NLP) technologies enable the extraction of valuable insights from unstructured data sources, facilitating the generation of comprehensive and insightful financial reports. This not only accelerates the reporting process but also enhances the communicative value of financial information to stakeholders. Despite the evident benefits, the widespread adoption of AI in accounting brings forth challenges such as ethical considerations, data security, and the need for upskilling the workforce. Ethical concerns regarding bias in AI algorithms and the responsible use of automation in decision-making processes necessitate a thoughtful approach towards AI integration in accounting practices. In conclusion, this review underscores the transformative impact of AI on modern accounting practices and financial reporting. As organizations navigate this technological revolution, a balanced approach that addresses ethical concerns while maximizing the benefits of AI will be crucial for the continued evolution of the accounting profession. Keywords: Impact, Artificial Intelligence, Modern, Accounting, Practices.

Awareness about the Agriculture Needs through Interactive Web Application for Farmers

Agriculture occupies an important position in the Indian economy. Indian farmers today are facing the problem of low income due to the lack of information about government schemes, fertilizers, farming equipment etc. Some smallholders Agriculture occupies an important position in the Indian economy. Indian farmers today are facing the problem of low income due to the lack of information about government schemes, fertilizers, farming equipment etc. Some smallholders and marginalized farmers have low awareness as most of them live in remote areas and don't have access to information about soil properties, seeds, recently used tools, fertilizers, etc. The document proposes an intelligent, portable system that uses natural language processing Agriculture occupies an important position in the Indian economy. Indian farmers today are facing the problem of low income due to the lack of information about government schemes, fertilizers, farming equipment etc. Some smallholders and marginalized farmers have low awareness as most of them live in remote areas and don't have access to information about soil properties, seeds, recently used tools, fertilizers, etc. The document proposes an intelligent, portable system that uses natural language processing methods to help farmers use differentfarming methods, and further help them to answer their queries and solve their basic and intermediate level doubts using chatbot which will save their time. To meet all the requirements of farmers, a chatbot is proposed using natural language processing technology. The system will act as an interactive virtual assistant for farmers, answering all queries related to agriculture. This paper will go through the implementation of the chatbot using the chatterweb bot libraries and Django framework

IntelliView: An AI Based Mock Interview Platform

The IntelliView initiative represents a pioneering endeavor designed to empower novice job seekers through the integration of state-of-the-art Artificial Intelligence (AI) and Natural Language Processing (NLP) technologies. This platform employs a sophisticated blend of HTML, CSS, JavaScript, and the Deep Face method, introducing a comprehensive framework to redefine interview assessment and augment job application preparation. The primary module facilitates text-based analysis, enabling users to engage with real-time interview questions by entering responses directly into the web interface. Subsequently, advanced algorithms compare users' responses to expected answers, yielding a percentage similarity score and presenting the correct answer. Beyond affording essential interview practice, this feature furnishes constructive feedback, enhancing users' responses and ultimately refining their interview performance. In the second module, IntelliView introduces a dynamic interview environment with video-based analysis. Users respond to inquiries utilizing webcams, enabling the system to meticulously record both verbal responses and facial expressions. Leveraging the Deep Face method, the platform conducts real-time emotion and sentiment analysis, offering users insights into their emotional states throughout interviews. This feedback facilitates the refinement of non-verbal communication skills, empowering candidates to recognize emotional tendencies and adapt interview strategies accordingly. The third module functions as a comprehensive resume builder, employing HTML, CSS, and JavaScript to provide diverse templates tailored to individual needs. In summation, IntelliView heralds a transformative paradigm in job application preparation, seamlessly amalgamating technological advancements and human interaction to equip first-time job seekers with the requisite tools for navigating the competitive job market successfully. Key Words: Interview Assessment, DeepFace, NLP, Text-based analysis, Video-based analysis, Resume Builder

Exploring the Influence of Natural Language Processing Technology on Marketing Strategy Innovation Management in Emerging Markets for Multinational Corporations

Exploring the Influence of Natural Language Processing Technology on Marketing Strategy Innovation Management in Emerging Markets for Multinational Corporations

TEC-miTarget: enhancing microRNA target prediction based on deep learning of ribonucleic acid sequences

BackgroundMicroRNAs play a critical role in regulating gene expression by binding to specific target sites within gene transcripts, making the identification of microRNA targets a prominent focus of research. Conventional experimental methods for identifying microRNA targets are both time-consuming and expensive, prompting the development of computational tools for target prediction. However, the existing computational tools exhibit limited performance in meeting the demands of practical applications, highlighting the need to improve the performance of microRNA target prediction models.ResultsIn this paper, we utilize the most popular natural language processing and computer vision technologies to propose a novel approach, called TEC-miTarget, for microRNA target prediction based on transformer encoder and convolutional neural networks. TEC-miTarget treats RNA sequences as a natural language and encodes them using a transformer encoder, a widely used encoder in natural language processing. It then combines the representations of a pair of microRNA and its candidate target site sequences into a contact map, which is a three-dimensional array similar to a multi-channel image. Therefore, the contact map's features are extracted using a four-layer convolutional neural network, enabling the prediction of interactions between microRNA and its candidate target sites. We applied a series of comparative experiments to demonstrate that TEC-miTarget significantly improves microRNA target prediction, compared with existing state-of-the-art models. Our approach is the first approach to perform comparisons with other approaches at both sequence and transcript levels. Furthermore, it is the first approach compared with both deep learning-based and seed-match-based methods. We first compared TEC-miTarget’s performance with approaches at the sequence level, and our approach delivers substantial improvements in performance using the same datasets and evaluation metrics. Moreover, we utilized TEC-miTarget to predict microRNA targets in long mRNA sequences, which involves two steps: selecting candidate target site sequences and applying sequence-level predictions. We finally showed that TEC-miTarget outperforms other approaches at the transcript level, including the popular seed match methods widely used in previous years.ConclusionsWe propose a novel approach for predicting microRNA targets at both sequence and transcript levels, and demonstrate that our approach outperforms other methods based on deep learning or seed match. We also provide our approach as an easy-to-use software, TEC-miTarget, at https://github.com/tingpeng17/TEC-miTarget. Our results provide new perspectives for microRNA target prediction.

Research on the Actual Sentence Partition and Translation of Chinese Cultural Landscape Discourses Based on Natural Language Processing Technology

In recent years, ChatGPT has been extensively applied across various fields, with translation being one of them. The objective of this study is to investigate the influence of Natural Language Processing (NLP) technology on the translation process. The research method involved utilizing the concept of Actual Sentence Partition (ASP) within the framework of Function Sentence Perspective (FSP). ASP, introduced by V. Mathesius, the founder of the Prague School, divides sentences into the point of departure and the nucleus of an utterance, providing insights into their communicative and semantic characteristics. Conversely, Formal Sentence Division (FSD) solely focuses on marking grammatical elements without identifying the correct ASP, resulting in ambiguity and inaccuracy. In Chinese discourse, incomplete sentences are commonly observed, including the omission of known information and the use of multiple clauses. Consequently, when translating from Chinese to English, it is crucial to appropriately visualize the hidden information in Chinese and analyze Chinese discourse using ASP to reconstruct its equivalent in English. The findings of this study emphasize the significance of ASP in translation. By understanding the ASP, translators can better comprehend the intention and meaning behind Chinese discourse, ensuring a more accurate and faithful representation in English. Therefore, the application of ASP in translation serves as a valuable tool for bridging the linguistic and cultural gaps between languages. In conclusion, ASP, as a pivotal concept in FSP, offers a comprehensive framework for understanding and translating complex sentence structures in Chinese discourse. By utilizing ASP, translators can capture the communicative and semantic nuances of Chinese sentences, leading to more accurate and effective translation outcomes..

Discourse Analysis and Guidance Strategies for Network Groups Based on Computer-aided Technology

With the development of social media, discourse analysis of online group interaction has gradually become a research hotspot. The research results in this field will provide reference and guidance for promoting the benign development of social networking. This paper aims to explore the application of computer-aided technology in online group discourse analysis and guidance strategies. Taking the social media platform as an example, this paper analyzes the online group discourse by using natural language processing technology and other computer-aided technologies and networking and analysis methods, and designs effective guiding strategies. The results show that computer-aided technology can bring significant advantages to online group discourse analysis and guidance strategies. Specifically, it can promote the decision-making of administrators, grasp the emotional needs and concerns of network groups, and formulate guidance strategies more adaptively. At the same time, combining computer-aided technology with networking and analysis methods is also one of the important trends of discourse analysis and guidance strategies in the future. Generally speaking, the application of computer-aided technology will bring more extensive, in-depth and accurate online group discourse analysis and guidance strategies, and promote the healthy development of online socialization.

Design and Implementation of a Primary Healthcare Cloud Platform

This paper presents the design and implementation of a primary healthcare cloud platform that aims to optimize primary medical services through informatization. The cloud platform integrates multiple functional modules including patient information management, diagnostic and treatment processes, pharmaceutical inventory, online appointments, financial management, and data analysis to meet the daily operational needs of primary healthcare institutions. Starting with the project background and current situation analysis, the paper details user needs research and functional design, and gives key discussions on the selection of system architecture and critical technology modules. During system development, technologies such as Spring Boot, Spring Cloud, and MySQL were used to build an efficient, stable, and secure platform. The system's reliability was ensured through unit testing, integration testing, system testing, and user acceptance testing. The paper concludes with a look into the future development direction of the platform, including further optimization of user experience, introduction of natural language processing and artificial intelligence technologies, and expansion of collaborative functions between primary healthcare institutions.

Enhancing Financial Sentiment Analysis: A Deep Dive into Natural Language Processing for Market Prediction Industries

The purpose of this study is to investigate the enhancement of Financial Sentiment Analysis by conducting an in-depth investigation of Natural Language Processing (NLP) approaches for the purpose of improving market prediction. The purpose of this research is to investigate the potential of natural language processing (NLP) to improve the accuracy and efficiency of sentiment analysis. This is in response to the complex structure of financial markets and the crucial role that sentiment plays. The examination of the relevant literature highlights the limits of traditional methods and the urgent need for creative solutions in the field of financial sentiment research. The approach that we use entails the careful collecting of data from social media and financial news, with a particular emphasis on the utilization of strong pre-processing tools. The research assesses the performance parameters of accuracy, precision, recall, and correlation with market trends by using natural language processing (NLP) technologies such as algorithms for sentiment analysis, Named Entity Recognition, and deep learning models. The findings include a comparative examination of conventional methods and those based on natural language processing (NLP), therefore revealing insights into the significant influence that sentiment has on market patterns. The results not only provide a contribution to the theoretical knowledge of sentiment research, but they also offer real consequences for financial analysts who are looking to make market forecasts that are more accurate and timelier. The research suggests ways for refinement, with an emphasis on enhanced pre-processing and Explainable AI integration. These tactics are being proposed to address issues in data quality and bias. When looking to the future, the study provides an overview of potential future paths, which include the investigation of external influences and the development of deep learning models for accurate market forecasting respectively. To summaries, the findings of this research establish natural language processing (NLP) as a revolutionary force in the process of redefining financial sentiment analysis. Furthermore, it offers a path for future developments in the ever-changing world of market prediction.

Applications of Natural Language Processing for Automated Clinical Data Analysis in Orthopaedics.

Natural language processing is an exciting and emerging field in health care that can transform the field of orthopaedics. It can aid in the process of automated clinical data analysis, changing the way we extract data for various purposes including research and registry formation, diagnosis, and medical billing. This scoping review will look at the various applications of NLP in orthopaedics. Specific examples of NLP applications include identification of essential data elements from surgical and imaging reports, patient feedback analysis, and use of AI conversational agents for patient engagement. We will demonstrate how NLP has proven itself to be a powerful and valuable tool. Despite these potential advantages, there are drawbacks we must consider. Concerns with data quality, bias, privacy, and accessibility may stand as barriers in the way of widespread implementation of NLP technology. As natural language processing technology continues to develop, it has the potential to revolutionize orthopaedic research and clinical practices and enhance patient outcomes.

Predicting attitudes toward ambiguity using natural language processing on free descriptions for open-ended question measurements

Individual traits and reactions to ambiguity differ and are conceptualized in terms of an individual’s attitudes toward ambiguity or ambiguity tolerance. The development of natural language processing technology has made it possible to measure mental states and reactions through open-ended questions, rather than predefined numerical rating scales, which have traditionally been the dominant method in psychological research. This study presented three ambiguity-related situations and responses collected online from 591 participants in an open-ended format. After the analysis with bidirectional encoder representations from transformers, correlations were calculated using scores from the numerical evaluation by conventional questionnaire, and a significant moderate positive correlation was found. Therefore, this study found that attitudes toward ambiguity can be measured using an open-ended response method of reporting everyday life states. It is a novel methodology that can be expanded to other scales in psychology and can potentially be used in educational and clinical situations where participants can be asked to respond with minimal burden.