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Search Results (113)
  • Open Access

    REVIEW

    Unlocking the Potential: A Comprehensive Systematic Review of ChatGPT in Natural Language Processing Tasks

    Ebtesam Ahmad Alomari*

    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 43-85, 2024, DOI:10.32604/cmes.2024.052256 - 20 August 2024

    Abstract As Natural Language Processing (NLP) continues to advance, driven by the emergence of sophisticated large language models such as ChatGPT, there has been a notable growth in research activity. This rapid uptake reflects increasing interest in the field and induces critical inquiries into ChatGPT’s applicability in the NLP domain. This review paper systematically investigates the role of ChatGPT in diverse NLP tasks, including information extraction, Name Entity Recognition (NER), event extraction, relation extraction, Part of Speech (PoS) tagging, text classification, sentiment analysis, emotion recognition and text annotation. The novelty of this work lies in its… More >

  • Open Access

    REVIEW

    AI-Driven Learning Management Systems: Modern Developments, Challenges and Future Trends during the Age of ChatGPT

    Sameer Qazi1,*, Muhammad Bilal Kadri2, Muhammad Naveed1,*, Bilal A. Khawaja3, Sohaib Zia Khan4, Muhammad Mansoor Alam5,6,7, Mazliham Mohd Su’ud6

    CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3289-3314, 2024, DOI:10.32604/cmc.2024.048893 - 15 August 2024

    Abstract COVID-19 pandemic restrictions limited all social activities to curtail the spread of the virus. The foremost and most prime sector among those affected were schools, colleges, and universities. The education system of entire nations had shifted to online education during this time. Many shortcomings of Learning Management Systems (LMSs) were detected to support education in an online mode that spawned the research in Artificial Intelligence (AI) based tools that are being developed by the research community to improve the effectiveness of LMSs. This paper presents a detailed survey of the different enhancements to LMSs, which… More >

  • Open Access

    ARTICLE

    A Hybrid Query-Based Extractive Text Summarization Based on K-Means and Latent Dirichlet Allocation Techniques

    Sohail Muhammad1, Muzammil Khan2, Sarwar Shah Khan2,3,*

    Journal on Artificial Intelligence, Vol.6, pp. 193-209, 2024, DOI:10.32604/jai.2024.052099 - 07 August 2024

    Abstract Retrieving information from evolving digital data collection using a user’s query is always essential and needs efficient retrieval mechanisms that help reduce the required time from such massive collections. Large-scale time consumption is certain to scan and analyze to retrieve the most relevant textual data item from all the documents required a sophisticated technique for a query against the document collection. It is always challenging to retrieve a more accurate and fast retrieval from a large collection. Text summarization is a dominant research field in information retrieval and text processing to locate the most appropriate… More >

  • Open Access

    ARTICLE

    Leveraging Pre-Trained Word Embedding Models for Fake Review Identification

    Glody Muka1,*, Patrick Mukala1,2,*

    Journal on Artificial Intelligence, Vol.6, pp. 211-223, 2024, DOI:10.32604/jai.2024.049685 - 07 August 2024

    Abstract Reviews have a significant impact on online businesses. Nowadays, online consumers rely heavily on other people's reviews before purchasing a product, instead of looking at the product description. With the emergence of technology, malicious online actors are using techniques such as Natural Language Processing (NLP) and others to generate a large number of fake reviews to destroy their competitors’ markets. To remedy this situation, several researches have been conducted in the last few years. Most of them have applied NLP techniques to preprocess the text before building Machine Learning (ML) or Deep Learning (DL) models… More >

  • Open Access

    ARTICLE

    Generating Factual Text via Entailment Recognition Task

    Jinqiao Dai, Pengsen Cheng, Jiayong Liu*

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 547-565, 2024, DOI:10.32604/cmc.2024.051745 - 18 July 2024

    Abstract Generating diverse and factual text is challenging and is receiving increasing attention. By sampling from the latent space, variational autoencoder-based models have recently enhanced the diversity of generated text. However, existing research predominantly depends on summarization models to offer paragraph-level semantic information for enhancing factual correctness. The challenge lies in effectively generating factual text using sentence-level variational autoencoder-based models. In this paper, a novel model called fact-aware conditional variational autoencoder is proposed to balance the factual correctness and diversity of generated text. Specifically, our model encodes the input sentences and uses them as facts to… More >

  • Open Access

    ARTICLE

    Comparing Fine-Tuning, Zero and Few-Shot Strategies with Large Language Models in Hate Speech Detection in English

    Ronghao Pan, José Antonio García-Díaz*, Rafael Valencia-García

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2849-2868, 2024, DOI:10.32604/cmes.2024.049631 - 08 July 2024

    Abstract Large Language Models (LLMs) are increasingly demonstrating their ability to understand natural language and solve complex tasks, especially through text generation. One of the relevant capabilities is contextual learning, which involves the ability to receive instructions in natural language or task demonstrations to generate expected outputs for test instances without the need for additional training or gradient updates. In recent years, the popularity of social networking has provided a medium through which some users can engage in offensive and harmful online behavior. In this study, we investigate the ability of different LLMs, ranging from zero-shot… More >

  • Open Access

    ARTICLE

    Sentiment Analysis of Low-Resource Language Literature Using Data Processing and Deep Learning

    Aizaz Ali1, Maqbool Khan1,2, Khalil Khan3, Rehan Ullah Khan4, Abdulrahman Aloraini4,*

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 713-733, 2024, DOI:10.32604/cmc.2024.048712 - 25 April 2024

    Abstract Sentiment analysis, a crucial task in discerning emotional tones within the text, plays a pivotal role in understanding public opinion and user sentiment across diverse languages. While numerous scholars conduct sentiment analysis in widely spoken languages such as English, Chinese, Arabic, Roman Arabic, and more, we come to grappling with resource-poor languages like Urdu literature which becomes a challenge. Urdu is a uniquely crafted language, characterized by a script that amalgamates elements from diverse languages, including Arabic, Parsi, Pashtu, Turkish, Punjabi, Saraiki, and more. As Urdu literature, characterized by distinct character sets and linguistic features,… More >

  • Open Access

    ARTICLE

    Identification of Software Bugs by Analyzing Natural Language-Based Requirements Using Optimized Deep Learning Features

    Qazi Mazhar ul Haq1, Fahim Arif2,3, Khursheed Aurangzeb4, Noor ul Ain3, Javed Ali Khan5, Saddaf Rubab6, Muhammad Shahid Anwar7,*

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4379-4397, 2024, DOI:10.32604/cmc.2024.047172 - 26 March 2024

    Abstract Software project outcomes heavily depend on natural language requirements, often causing diverse interpretations and issues like ambiguities and incomplete or faulty requirements. Researchers are exploring machine learning to predict software bugs, but a more precise and general approach is needed. Accurate bug prediction is crucial for software evolution and user training, prompting an investigation into deep and ensemble learning methods. However, these studies are not generalized and efficient when extended to other datasets. Therefore, this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification problems. The methods involved feature… More >

  • Open Access

    ARTICLE

    Design of Artificial Intelligence Companion Chatbot

    Xiaoying Chen1,*, Jie Kang1, Cong Hu2

    Journal of New Media, Vol.6, pp. 1-16, 2024, DOI:10.32604/jnm.2024.045833 - 28 March 2024

    Abstract With the development of cities and the prevalence of networks, interpersonal relationships have become increasingly distant. When people crave communication, they hope to find someone to confide in. With the rapid advancement of deep learning and big data technologies, an enabling environment has been established for the development of intelligent chatbot systems. By effectively combining cutting-edge technologies with human-centered design principles, chatbots hold the potential to revolutionize our lives and alleviate feelings of loneliness. A multi-topic chat companion robot based on a state machine has been proposed, which can engage in fluent dialogue with humans… More >

  • Open Access

    ARTICLE

    Classification of Conversational Sentences Using an Ensemble Pre-Trained Language Model with the Fine-Tuned Parameter

    R. Sujatha, K. Nimala*

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1669-1686, 2024, DOI:10.32604/cmc.2023.046963 - 27 February 2024

    Abstract Sentence classification is the process of categorizing a sentence based on the context of the sentence. Sentence categorization requires more semantic highlights than other tasks, such as dependence parsing, which requires more syntactic elements. Most existing strategies focus on the general semantics of a conversation without involving the context of the sentence, recognizing the progress and comparing impacts. An ensemble pre-trained language model was taken up here to classify the conversation sentences from the conversation corpus. The conversational sentences are classified into four categories: information, question, directive, and commission. These classification label sequences are for… More >

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