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  • Open Access


    Construction of Intelligent Recommendation Retrieval Model of FuJian Intangible Cultural Heritage Digital Archives Resources

    Xueqing Liao*

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 677-690, 2023, DOI:10.32604/iasc.2023.037219

    Abstract In order to improve the consistency between the recommended retrieval results and user needs, improve the recommendation efficiency, and reduce the average absolute deviation of resource retrieval, a design method of intelligent recommendation retrieval model for Fujian intangible cultural heritage digital archive resources based on knowledge atlas is proposed. The TG-LDA (Tag-granularity LDA) model is proposed on the basis of the standard LDA (Linear Discriminant Analysis) model. The model is used to mine archive resource topics. The Pearson correlation coefficient is used to measure the relevance between topics. Based on the measurement results, the FastText deep learning model is used… More >

  • Open Access


    Chinese Relation Extraction on Forestry Knowledge Graph Construction

    Qi Yue, Xiang Li, Dan Li*

    Computer Systems Science and Engineering, Vol.37, No.3, pp. 423-442, 2021, DOI:10.32604/csse.2021.014448

    Abstract Forestry work has long been weak in data integration; its initial state will inevitably affect the forestry project development and decision-quality. Knowledge Graph (KG) can provide better abilities to organize, manage, and understand forestry knowledge. Relation Extraction (RE) is a crucial task of KG construction and information retrieval. Previous researches on relation extraction have proved the performance of using the attention mechanism. However, these methods focused on the representation of the entire sentence and ignored the loss of information. The lack of analysis of words and syntactic features contributes to sentences, especially in Chinese relation extraction, resulting in poor performance.… More >

  • Open Access


    Fast Sentiment Analysis Algorithm Based on Double Model Fusion

    Zhixing Lin1,2, Like Wang3,4, Xiaoli Cui5, Yongxiang Gu3,4,*

    Computer Systems Science and Engineering, Vol.36, No.1, pp. 175-188, 2021, DOI:10.32604/csse.2021.014260

    Abstract Nowadays, as the number of textual data is exponentially increasing, sentiment analysis has become one of the most significant tasks in natural language processing (NLP) with increasing attention. Traditional Chinese sentiment analysis algorithms cannot make full use of the order information in context and are inefficient in sentiment inference. In this paper, we systematically reviewed the classic and representative works in sentiment analysis and proposed a simple but efficient optimization. First of all, FastText was trained to get the basic classification model, which can generate pre-trained word vectors as a by-product. Secondly, Bidirectional Long Short-Term Memory Network (Bi-LSTM) utilizes the… More >

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