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Journals / Bilişim Teknolojileri Dergisi / 2019 / Cilt: 12 - Sayı: 2

Comparison of Modeling Time of Word Vector Methods

Kelime Vektörü Yöntemlerinin Model Oluşturma Sürelerinin Karşılaştırılması

Metin BİLGİN
Pages
141–146
Publication Date
2019-04-30
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

As a result, it was experimentally shown that CBoW is more successful than Skip-Gram based for modeling time.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this study, two different datasets for sentiment analysis have been modeled by Word2Vec that it is a word vector algorithm.
📊
Key Findings & Data:While the model is creating that has used two different methods CBoW and Skip-Gram of Word2Vec.
💡
Conclusion & Impact:As a result, it was experimentally shown that CBoW is more successful than Skip-Gram based for modeling time.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Word2Vec · modeling time · training time

Abstract

In this study, two different datasets for sentiment analysis have been modeled by Word2Vec that it is a word vector algorithm. While the model is creating that has used two different methods CBoW and Skip-Gram of Word2Vec. Generally, the arithmetic mean is used for modeling a text with Word2Vec. In this study, three different methods for modeling a text are suggested on both CBoW and Skip-Gram. Its modeling time (training time) is measured. As a result, it was experimentally shown that CBoW is more successful than Skip-Gram based for modeling time.

Özet

Bu çalışmada duygu analizi için oluşturulan iki farklı veri kümesi, kelime vektörü algoritması olan Word2Vec ile modellenmiştir. Model oluşturulurken Word2Vec’in iki farklı yöntemi olan CBoW (Continous Bag of Words) ve Skip-Gram kullanılmıştır. Word2Vec ile bir metnin modelini oluşturmak için genellikle Ortalama yöntemi kullanılmaktadır. Bu çalışmada hem CBoW hem de Skip-Gram yöntemleriyle bir metni modellemek için üç farklı yöntem önerilmiştir. Model oluşturma (eğitim zamanı) süreleri her ikisi içinde ölçülmüştür. Sonuç olarak modelleme süresi açısından CBoW’un Skip-Gram’dan daha başarılı olduğu deneysel olarak gösterilmiştir.

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Cite This Article

BİLGİN, M. (2019). Comparison of Modeling Time of Word Vector Methods. Bilişim Teknolojileri Dergisi, Cilt: 12 - Sayı: 2, 141–146. https://ajindex.com/articles/251780

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