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    Quantum-like generalization of complex word embedding: a lightweight approach for textual classification.

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    Authors
    Jaiswal, Amit Kumar
    Holdack, Guilherme
    Frommholz, Ingo
    Liu, Haiming
    Affiliation
    University of Bedfordshire
    Issue Date
    2018-09-30
    Subjects
    word embedding
    quantum theory
    word-context
    G730 Neural Computing
    
    Metadata
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    Abstract
    In this paper, we present an extension, and an evaluation, to existing Quantum like approaches of word embedding for IR tasks that (1) improves complex features detection of word use (e.g., syntax and semantics), (2) enhances how this method extends these aforementioned uses across linguistic contexts (i.e., to model lexical ambiguity) - specifically Question Classification -, and (3) reduces computational resources needed for training and operating Quantum based neural networks, when confronted with existing models. This approach could also be latter applicable to significantly enhance the state-of the-art across Natural Language Processing (NLP) word-level tasks such as entity recognition, part-of-speech tagging, or sentence-level ones such as textual relatedness and entailment, to name a few.
    Citation
    Jaiswal AK, Holdack G, Frommholz I, Liu H (2018) 'Quantum-like generalization of complex word embedding: a lightweight approach for textual classification.', Lernen, Wissen, Daten, Analysen 2018 - Mannheim, CEUR Workshop Proceedings.
    Publisher
    CEUR Workshop Proceedings
    URI
    http://hdl.handle.net/10547/623794
    Additional Links
    http://ceur-ws.org/Vol-2191/paper19.pdf
    Type
    Conference papers, meetings and proceedings
    Language
    en
    Collections
    Computing

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