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The Importance of Token Granularity Matching of Pre-trained Word Vectors for Deep Learning-Based Spam Classification

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F21%3A10441733" target="_blank" >RIV/00216208:11320/21:10441733 - isvavai.cz</a>

  • Result on the web

    <a href="https://doi.org/10.1109/ICNLP52887.2021.00007" target="_blank" >https://doi.org/10.1109/ICNLP52887.2021.00007</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/ICNLP52887.2021.00007" target="_blank" >10.1109/ICNLP52887.2021.00007</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    The Importance of Token Granularity Matching of Pre-trained Word Vectors for Deep Learning-Based Spam Classification

  • Original language description

    Spam email detection is a research hotspot, and the most efficient detection method is based on deep learning. In the context of the extensive use of pre-trained word vectors in deep neural networks, this paper studies the impact of pre-trained word vector models on the Text-CNN-based spam classification model, and uses token granularity matching technology to optimize the word2vec pre-trained word vector model in the vector representation on the spam email. By comparing the accuracy and time complexity of the spam classification with or without token granularity matching, it can be concluded that the Word2Vec pre-trained word vectors combined with token granularity processing can improve the performance of the Text-CNN model on spam email classification.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

Others

  • Publication year

    2021

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Proceedings - 2021 3rd International Conference on Natural Language Processing, ICNLP 2021

  • ISBN

    978-1-66541-411-1

  • ISSN

  • e-ISSN

  • Number of pages

    5

  • Pages from-to

    129-133

  • Publisher name

    IEEE Conference Publishing Services

  • Place of publication

    Piscataway

  • Event location

    Beijing

  • Event date

    Mar 26, 2021

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article