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  • 1.
    Ming, Hong
    et al.
    Hefei University of Technology, 420 Jade Road, Hefei City, Anhui Province, Hefei, China.
    Yang, Jiaoyun
    Hefei University of Technology, 420 Jade Road, Hefei City, Anhui Province, Hefei, China.
    Gui, Fang
    Hefei University of Technology, 420 Jade Road, Hefei City, Anhui Province, Hefei, China.
    Jiang, Lili
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    An, Ning
    Hefei University of Technology, 420 Jade Road, Hefei City, Anhui Province, Hefei, China.
    Few-shot nested named entity recognition2024In: Knowledge-Based Systems, ISSN 0950-7051, E-ISSN 1872-7409, Vol. 293, article id 111688Article in journal (Refereed)
    Abstract [en]

    While Named Entity Recognition (NER) is a widely studied task, making inferences of entities with only a few labeled data has been challenging, especially for entities with nested structures commonly existing in NER datasets. Unlike flat entities, entities and their nested entities are more likely to have similar semantic feature representations, drastically increasing difficulties in classifying different entity categories. This paper posits that the few-shot nested NER task warrants its own dedicated attention and proposes a Global-Biaffine Positive-Enhanced (GBPE) framework for this new task. Within the GBPE framework, we first develop the new Global-Biaffine span representation to capture the span global dependency information for each entity span to distinguish nested entities. We then formulate a unique positive-enhanced contrastive loss function to enhance the utility of specific positive samples in contrastive learning for larger margins. Lastly, by using these enlarged margins, we obtain better margin constraints and incorporate them into the nearest neighbor inference to predict the unlabeled entities. Extensive experiments on three nested NER datasets in English, German, and Russian show that GBPE outperforms baseline models on the 1-shot and 5-shot tasks in terms of F1 score.

  • 2.
    Nakhaei, Niknaz
    et al.
    Faculty of New Science and Technologies, University of Tehran, Tehran, Iran.
    Ebrahimi, Morteza
    Faculty of New Science and Technologies, University of Tehran, Tehran, Iran.
    Hosseini, S. Ahmad
    Umeå University, Faculty of Science and Technology, Department of Computing Science. Centre for Information Technologies and Applied Mathematics, University of Nova Gorica, Slovenia.
    A solution technique to cascading link failure prediction2022In: Knowledge-Based Systems, ISSN 0950-7051, E-ISSN 1872-7409, Vol. 258, article id 109920Article in journal (Refereed)
    Abstract [en]

    The study of complex networks is a new powerful tool that can provide a profitable skeleton to better elucidate technology-related phenomena and interactions between components of real-world networks However, it is not easy to predict the communal behavior of such systems from their elements and on the other hand, the failure of one or few elements can trigger the failure of other elements throughout the network, resulting in network breakdown and catastrophic events at large scales. Therefore, developing predictive mathematical techniques to examine complex networks is one of the biggest challenges of the present time. Knowing that link failure prediction is less studied in the OR literature, the present study articulates a method to predict link failures in complex networks, which is primarily based on Bayesian Belief Networks (BBN) and TOPSIS. The method aims to predict failures based on the affective factors of failures in networks. To this end, effective factors of failures are first detected, and then the graph of the relationship of factors along with their weight is determined. After all, the method provides the prediction for future damaged components. The functionality of the method is validated by an extensive computational analysis employing simulation in scale-free, random, and actual international aviation networks and its performance is compared with other benchmark algorithms. The results and sensitivity analysis experiments arrive at prominent managerial insights and efficacious implications and show that our method can generate high-quality solutions in many networks.

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