Unleashing the Potential of Deep Learning Methods for Detecting Defective Expressions Using Hyperparameter Optimization Techniques

Atilla Suncak; Özlem Varlıklar

Makale detayları

Unleashing the Potential of Deep Learning Methods for Detecting Defective Expressions Using Hyperparameter Optimization Techniques

Yazarlar:

Atilla Suncak | Özlem Varlıklar

Yayıncı:

Dokuz Eylul University

Ana Başlıklar:

Performans Değerlendirmesi | Yüksek Performanslı Hesaplama

Dergi:

Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi Vol.27 Issue 79, pp.72-79

Belge Tipi:

Makale

Yayın Tarihi:

1/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001336030

pISSN:

1302-9304

Anahtar kelimeler:

Özet:

 Natural Language Processing NLP has emerged remarkable progress in the field of deep learning studies. Not only a superior alternative to rule-based NLP methods, deep learning-based techniques have also succeeded more accurate performances in various NLP tasks such as text classification, sentiment analysis or document clustering. Since the performance of a deep learning model undoubtedly depends on adjusting its hyperparameters ideally, tuning the most optimum hyperparameters determines the capability of the model learning in terms of meaningful pattern extraction from the input data. In this paper, hyperparameter optimization techniques of Bayesian Optimization, Random Search and Grid Search have been applied on the deep learning models of Long Short-Term Memory LSTM and Convolutional Neural Network CNN for the purpose of detecting defective expressions in Turkish sentences. The hyperparameters of previously implemented LSTM and CNN models for this purpose have been adjusted using trial-and-error approach, which is time-consuming and cannot guarantee the most ideal model in general. After these hyperparameters have been adjusted using optimization techniques, the performances in terms of accuracy have been increased from 87.94 to 92.82 and from 84.33 to 89.79 for the models of LSTM and CNN respectively.

 Natural Language Processing NLP has emerged remarkable progress in the field of deep learning studies. Not only a superior alternative to rule-based NLP methods, deep learning-based techniques have also succeeded more accurate performances in various NLP tasks such as text classification, sentiment analysis or document clustering. Since the performance of a deep learning model undoubtedly depends on adjusting its hyperparameters ideally, tuning the most optimum hyperparameters determines the capability of the model learning in terms of meaningful pattern extraction from the input data. In this paper, hyperparameter optimization techniques of Bayesian Optimization, Random Search and Grid Search have been applied on the deep learning models of Long Short-Term Memory LSTM and Convolutional Neural Network CNN for the purpose of detecting defective expressions in Turkish sentences. The hyperparameters of previously implemented LSTM and CNN models for this purpose have been adjusted using trial-and-error approach, which is time-consuming and cannot guarantee the most ideal model in general. After these hyperparameters have been adjusted using optimization techniques, the performances in terms of accuracy have been increased from 87.94 to 92.82 and from 84.33 to 89.79 for the models of LSTM and CNN respectively.

 Natural Language Processing NLP has emerged remarkable progress in the field of deep learning studies. Not only a superior alternative to rule-based NLP methods, deep learning-based techniques have also succeeded more accurate performances in various NLP tasks such as text classification, sentiment analysis or document clustering. Since the performance of a deep learning model undoubtedly depends on adjusting its hyperparameters ideally, tuning the most optimum hyperparameters determines the capability of the model learning in terms of meaningful pattern extraction from the input data. In this paper, hyperparameter optimization techniques of Bayesian Optimization, Random Search and Grid Search have been applied on the deep learning models of Long Short-Term Memory LSTM and Convolutional Neural Network CNN for the purpose of detecting defective expressions in Turkish sentences. The hyperparameters of previously implemented LSTM and CNN models for this purpose have been adjusted using trial-and-error approach, which is time-consuming and cannot guarantee the most ideal model in general. After these hyperparameters have been adjusted using optimization techniques, the performances in terms of accuracy have been increased from 87.94 to 92.82 and from 84.33 to 89.79 for the models of LSTM and CNN respectively.

 Doğal Dil İşleme DDİ, derin öğrenme çalışmaları alanında dikkat çekici ilerlemeler ortaya koymuştur. Derin öğrenme tabanlı teknikler, yalnızca kural tabanlı DDİ yöntemlerine üstün bir alternatif olmakla kalmayıp, aynı zamanda metin sınıflandırma, duygu analizi veya belge kümeleme gibi çeşitli DDİ görevlerinde de daha doğru performanslar elde etmeyi başarmıştır. Bir derin öğrenme modelinin performansı, şüphesiz ki hiperparametrelerinin ideal şekilde ayarlanmasına bağlı olduğundan, en ideal hiperparametrelerin ayarlanması, girdi verilerinden anlamlı örüntü çıkarma açısından model öğrenmesinin kapasitesini belirler. Bu makalede, Türkçe cümlelerdeki anlatım bozukluklarını tespit etmek amacıyla Uzun Kısa-Süreli Bellek UKSB ve Evrişimsel Sinir Ağları ESA derin öğrenme modelleri üzerinde Bayesian Optimization, Random Search ve Grid Search hiperparametre optimizasyon teknikleri uygulanmıştır. Bu amaçla daha önce geliştirilmiş UKSB ve ESA modellerinin hiperparametreleri, zaman alan ve genel olarak en ideal modeli garanti edemeyen deneme-yanılma yaklaşımı kullanılarak ayarlanmıştı. Bu hiperparametreler, optimizasyon teknikleri kullanılarak ayarlandıktan sonra ise, doğruluk açısından performansları UKSB ve ESA modelleri için sırasıyla 87,94ten 92,82ye ve 84,33ten 89,79a yükseltilmiştir.

Makale detayları

Unleashing the Potential of Deep Learning Methods for Detecting Defective Expressions Using Hyperparameter Optimization Techniques

Yazarlar:

Atilla Suncak | Özlem Varlıklar

Yayıncı:

Dokuz Eylul University

Ana Başlıklar:

Performans Değerlendirmesi | Yüksek Performanslı Hesaplama

Dergi:

Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi Vol.27 Issue 79, pp.72-79

Belge Tipi:

Makale

Yayın Tarihi:

1/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001336030

pISSN:

1302-9304

Bayesian optimization

Grid search

Hyperparameter optimization

NLP

Random search

TurkishBayesian optimization

Grid search

Hiperparametre optimizasyonu

Doğal dil işleme

Random search

Türkçe

Sayı

Makale detayları

Yazarlar: Atilla Suncak | Özlem Varlıklar

Yayıncı: Dokuz Eylul University

Ana Başlıklar: Performans Değerlendirmesi | Yüksek Performanslı Hesaplama

Dergi: Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi Vol.27 Issue 79, pp.72-79

Belge Tipi: Makale

Yayın Tarihi: 1/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001336030

pISSN: 1302-9304

Sayı

Yazarlar:

Atilla Suncak | Özlem Varlıklar

Yayıncı:

Dokuz Eylul University

Ana Başlıklar:

Performans Değerlendirmesi | Yüksek Performanslı Hesaplama

Dergi:

Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi Vol.27 Issue 79, pp.72-79

Belge Tipi:

Makale

Yayın Tarihi:

1/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001336030

pISSN:

1302-9304