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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

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

Projekt

—

Návaznosti

—

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název statě ve sborníku

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

ISBN

978-1-66541-411-1

ISSN

—

e-ISSN

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Počet stran výsledku

5

Strana od-do

129-133

Název nakladatele

IEEE Conference Publishing Services

Místo vydání

Piscataway

Místo konání akce

Beijing

Datum konání akce

26. 3. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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