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    Entity-aware capsule network for multi-class classification of big data: a deep learning approach

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    Authors
    Jaiswal, Amit Kumar
    Tiwari, Prayag
    Garg, Sahil
    Hossain, M. Shamim
    Affiliation
    University of Bedfordshire
    University of Padova
    École de technologie supérieure, Montréal
    King Saud University
    Issue Date
    2020-11-20
    Subjects
    natural language processing
    capsule network
    named entity recognition
    
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    Abstract
    Named entity recognition (NER) is one of the most challenging natural language processing (NLP) tasks, as its performance is related to constantly evolving languages and dependency on expert (human) annotation. The diverse and dynamic content on the web significantly raises the need for a more generalized approach—one that is capable of correctly classifying terms in a corpus and feeding subsequent NLP tasks, such as machine translation, query expansion, and many other applications. Although extensively researched in recent times, the variety of public corpora available nowadays provides room for new and more accurate methods to tackle the NER problem. This paper presents a novel method that uses deep learning techniques based on the capsule network architecture for predicting entities in a corpus. This type of network groups neurons into so-called capsules to detect specific features of an object without reducing the original input unlike convolutional neural networks and their ‘max-pooling’ strategy. Our extensive evaluation on several benchmarked datasets demonstrates how competitive our method is in comparison with state-of-the-art techniques and how the usage of the proposed architecture may represent a significant benefit to further NLP tasks, especially in cases where experts are needed. Also, we explore NER using a theoretical framework that leverages big data for security. For the sake of reproducibility, we make the codebase open-source.
    Citation
    Jaiswal AK, Tiwari P, Garg S, Hossain MS (2021) 'Entity-aware capsule network for multi-class classification of big data: a deep learning approach', Future Generation Computer Systems, 117, pp.1-11.
    Publisher
    Elsevier B.V.
    Journal
    Future Generation Computer Systems
    URI
    http://hdl.handle.net/10547/624733
    DOI
    10.1016/j.future.2020.11.012
    Additional Links
    https://www.sciencedirect.com/science/article/pii/S0167739X20330363
    Type
    Article
    Language
    en
    ISSN
    0167-739X
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.future.2020.11.012
    Scopus Count
    Collections
    Computing

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