Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (16)
  • Open Access


    PAL-BERT: An Improved Question Answering Model

    Wenfeng Zheng1, Siyu Lu1, Zhuohang Cai1, Ruiyang Wang1, Lei Wang2, Lirong Yin2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2729-2745, 2024, DOI:10.32604/cmes.2023.046692

    Abstract In the field of natural language processing (NLP), there have been various pre-training language models in recent years, with question answering systems gaining significant attention. However, as algorithms, data, and computing power advance, the issue of increasingly larger models and a growing number of parameters has surfaced. Consequently, model training has become more costly and less efficient. To enhance the efficiency and accuracy of the training process while reducing the model volume, this paper proposes a first-order pruning model PAL-BERT based on the ALBERT model according to the characteristics of question-answering (QA) system and language More >

  • Open Access


    MVCE-Net: Multi-View Region Feature and Caption Enhancement Co-Attention Network for Visual Question Answering

    Feng Yan1, Wushouer Silamu2, Yanbing Li1,*

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 65-80, 2023, DOI:10.32604/cmc.2023.038177

    Abstract Visual question answering (VQA) requires a deep understanding of images and their corresponding textual questions to answer questions about images more accurately. However, existing models tend to ignore the implicit knowledge in the images and focus only on the visual information in the images, which limits the understanding depth of the image content. The images contain more than just visual objects, some images contain textual information about the scene, and slightly more complex images contain relationships between individual visual objects. Firstly, this paper proposes a model using image description for feature enhancement. This model encodes… More >

  • Open Access


    Improved Blending Attention Mechanism in Visual Question Answering

    Siyu Lu1, Yueming Ding1, Zhengtong Yin2, Mingzhe Liu3,*, Xuan Liu4, Wenfeng Zheng1,*, Lirong Yin5

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 1149-1161, 2023, DOI:10.32604/csse.2023.038598

    Abstract Visual question answering (VQA) has attracted more and more attention in computer vision and natural language processing. Scholars are committed to studying how to better integrate image features and text features to achieve better results in VQA tasks. Analysis of all features may cause information redundancy and heavy computational burden. Attention mechanism is a wise way to solve this problem. However, using single attention mechanism may cause incomplete concern of features. This paper improves the attention mechanism method and proposes a hybrid attention mechanism that combines the spatial attention mechanism method and the channel attention More >

  • Open Access


    Expert Recommendation in Community Question Answering via Heterogeneous Content Network Embedding

    Hong Li1,*, Jianjun Li1, Guohui Li1, Rong Gao2, Lingyu Yan2

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1687-1709, 2023, DOI:10.32604/cmc.2023.035239

    Abstract Expert Recommendation (ER) aims to identify domain experts with high expertise and willingness to provide answers to questions in Community Question Answering (CQA) web services. How to model questions and users in the heterogeneous content network is critical to this task. Most traditional methods focus on modeling questions and users based on the textual content left in the community while ignoring the structural properties of heterogeneous CQA networks and always suffering from textual data sparsity issues. Recent approaches take advantage of structural proximities between nodes and attempt to fuse the textual content of nodes for… More >

  • Open Access


    Information Extraction Based on Multi-turn Question Answering for Analyzing Korean Research Trends

    Seongung Jo1, Heung-Seon Oh1,*, Sanghun Im1, Gibaeg Kim1, Seonho Kim2

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2967-2980, 2023, DOI:10.32604/cmc.2023.031983

    Abstract Analyzing Research and Development (R&D) trends is important because it can influence future decisions regarding R&D direction. In typical trend analysis, topic or technology taxonomies are employed to compute the popularities of the topics or codes over time. Although it is simple and effective, the taxonomies are difficult to manage because new technologies are introduced rapidly. Therefore, recent studies exploit deep learning to extract pre-defined targets such as problems and solutions. Based on the recent advances in question answering (QA) using deep learning, we adopt a multi-turn QA model to extract problems and solutions from… More >

  • Open Access


    ALBERT with Knowledge Graph Encoder Utilizing Semantic Similarity for Commonsense Question Answering

    Byeongmin Choi1, YongHyun Lee1, Yeunwoong Kyung2, Eunchan Kim3,*

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 71-82, 2023, DOI:10.32604/iasc.2023.032783

    Abstract Recently, pre-trained language representation models such as bidirectional encoder representations from transformers (BERT) have been performing well in commonsense question answering (CSQA). However, there is a problem that the models do not directly use explicit information of knowledge sources existing outside. To augment this, additional methods such as knowledge-aware graph network (KagNet) and multi-hop graph relation network (MHGRN) have been proposed. In this study, we propose to use the latest pre-trained language model a lite bidirectional encoder representations from transformers (ALBERT) with knowledge graph information extraction technique. We also propose to applying the novel method, More >

  • Open Access


    An Ontology-Based Question Answering System for University Admissions Advising

    Thi Thanh Sang Nguyen*, Dang Huu Trong Ho, Ngoc Tram Anh Nguyen

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 601-616, 2023, DOI:10.32604/iasc.2023.032080

    Abstract Question-Answer systems are now very popular and crucial to support human in automatically responding frequent questions in many fields. However, these systems depend on learning methods and training data. Therefore, it is necessary to prepare such a good dataset, but it is not an easy job. An ontology-based domain knowledge base is able to help to reason semantic information and make effective answers given user questions. This study proposes a novel chatbot model involving ontology to generate efficient responses automatically. A case study of admissions advising at the International University–VNU HCMC is taken into account… More >

  • Open Access


    Impact of Data Quality on Question Answering System Performances

    Rachid Karra*, Abdelali Lasfar

    Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 335-349, 2023, DOI:10.32604/iasc.2023.026695

    Abstract In contrast with the research of new models, little attention has been paid to the impact of low or high-quality data feeding a dialogue system. The present paper makes the first attempt to fill this gap by extending our previous work on question-answering (QA) systems by investigating the effect of misspelling on QA agents and how context changes can enhance the responses. Instead of using large language models trained on huge datasets, we propose a method that enhances the model's score by modifying only the quality and structure of the data feed to the model.… More >

  • Open Access


    Answer Classification via Machine Learning in Community Question Answering

    Yue Jiang, Xinyu Zhang, Wohuan Jia, Li Xu*

    Journal on Artificial Intelligence, Vol.3, No.4, pp. 163-169, 2021, DOI:10.32604/jai.2021.027590

    Abstract As a new type of knowledge sharing platform, the community question answer website realizes the acquisition and sharing of knowledge, and is loved and sought after by the majority of users. But for multi-answer questions, answer quality assessment becomes a challenge. The answer selection in CQA (Community Question Answer) was proposed as a challenge task in the SemEval competition, which gave a data set and proposed two subtasks. Task-A is to give a question (including short title and extended description) and its answers, and divide each answer into absolutely relevant (good), potentially relevant (potential) and… More >

  • Open Access


    BERT for Conversational Question Answering Systems Using Semantic Similarity Estimation

    Abdulaziz Al-Besher1, Kailash Kumar1, M. Sangeetha2,*, Tinashe Butsa3

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4763-4780, 2022, DOI:10.32604/cmc.2022.021033

    Abstract Most of the questions from users lack the context needed to thoroughly understand the problem at hand, thus making the questions impossible to answer. Semantic Similarity Estimation is based on relating user’s questions to the context from previous Conversational Search Systems (CSS) to provide answers without requesting the user's context. It imposes constraints on the time needed to produce an answer for the user. The proposed model enables the use of contextual data associated with previous Conversational Searches (CS). While receiving a question in a new conversational search, the model determines the question that refers More >

Displaying 1-10 on page 1 of 16. Per Page