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  • Open Access


    Multi-Modal Military Event Extraction Based on Knowledge Fusion

    Yuyuan Xiang, Yangli Jia*, Xiangliang Zhang, Zhenling Zhang

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 97-114, 2023, DOI:10.32604/cmc.2023.040751

    Abstract Event extraction stands as a significant endeavor within the realm of information extraction, aspiring to automatically extract structured event information from vast volumes of unstructured text. Extracting event elements from multi-modal data remains a challenging task due to the presence of a large number of images and overlapping event elements in the data. Although researchers have proposed various methods to accomplish this task, most existing event extraction models cannot address these challenges because they are only applicable to text scenarios. To solve the above issues, this paper proposes a multi-modal event extraction method based on knowledge fusion. Specifically, for event-type… More >

  • Open Access


    Joint Event Extraction Based on Global Event-Type Guidance and Attention Enhancement

    Daojian Zeng1, Jian Tian2, Ruoyao Peng1, Jianhua Dai1,*, Hui Gao3, Peng Peng4

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 4161-4173, 2021, DOI:10.32604/cmc.2021.017028

    Abstract Event extraction is one of the most challenging tasks in information extraction. It is a common phenomenon where multiple events exist in the same sentence. However, extracting multiple events is more difficult than extracting a single event. Existing event extraction methods based on sequence models ignore the interrelated information between events because the sequence is too long. In addition, the current argument extraction relies on the results of syntactic dependency analysis, which is complicated and prone to error transmission. In order to solve the above problems, a joint event extraction method based on global event-type guidance and attention enhancement was… More >

  • Open Access


    Biomedical Event Extraction Using a New Error Detection Learning Approach Based on Neural Network

    Xiaolei Ma1, 2, Yang Lu1, 2, Yinan Lu1, *, Zhili Pei2, Jichao Liu3

    CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 923-941, 2020, DOI:10.32604/cmc.2020.07711

    Abstract Supervised machine learning approaches are effective in text mining, but their success relies heavily on manually annotated corpora. However, there are limited numbers of annotated biomedical event corpora, and the available datasets contain insufficient examples for training classifiers; the common cure is to seek large amounts of training samples from unlabeled data, but such data sets often contain many mislabeled samples, which will degrade the performance of classifiers. Therefore, this study proposes a novel error data detection approach suitable for reducing noise in unlabeled biomedical event data. First, we construct the mislabeled dataset through error data analysis with the development… More >

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