The Future of Turkish Text Classification: A Deep Dive into LLMs vs. Traditional NLP

Advancing Turkish NLP Through Language Models

With the explosive growth of digital data, the ability to classify text efficiently is more critical than ever. However, when it comes to morphologically complex languages like Turkish, conventional NLP approaches often struggle to deliver high accuracy and reliability.

Recognizing this challenge, we had the opportunity to present our research at the International Data Science and Statistics Congress (IDSSC) 2024, where we shared our findings on LLM-based and traditional NLP-based approaches for Turkish text classification. Our study, conducted by Furkan Ay?k, Mehmet Yal??n, Celal Ak?elik, and Serap Mungan Tanhan, has now been introduced to the literature as a significant contribution to the field. In addition to presenting our work at the congress, we have also formally submitted our paper, further reinforcing its value in advancing Turkish NLP research.

Our study, "Comparative Analysis of LLM-Based and Conventional NLP-Based Methods for Multi-Class Text Classification in Turkish," explores the capabilities of traditional machine learning models versus cutting-edge large language models (LLMs), including BERT, ConvBERT, XLM-RoBERTa, and LLaMA 3.1. The results revealed that the BERT Base Uncased - Turkish model achieved the highest accuracy at 93%, demonstrating LLMs’ effectiveness in capturing Turkish linguistic nuances. Among boosting models, CatBoost with N-gram TF-IDF reached 86% accuracy, reinforcing the relevance of traditional NLP methods, particularly in resource-limited settings.

By sharing this summary, we aim to foster further discussions and collaborations within the data science and NLP ecosystem. We firmly believe that knowledge grows as it is shared, and we look forward to engaging with fellow researchers, practitioners, and industry experts to push the boundaries of Turkish NLP even further.

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The Challenge: A Morphologically Complex Language

Turkish presents unique linguistic hurdles, including agglutinative structures and extensive vocabulary variations, making it harder for conventional methods to perform well. While English NLP research has flourished, Turkish text classification has remained underexplored—until now.

Our research tested both traditional ML models (CatBoost, XGBoost, LightGBM) and LLMs on a dataset of Turkish airline cabin reports. The goal is to determine which approach is best suited for high-accuracy, real-world classification tasks.

The Results: LLMs Take the Lead

The findings were clear: LLMs significantly outperformed traditional models.

?? BERT Base Uncased - Turkish achieved the highest accuracy at 93%, proving its ability to grasp Turkish linguistic nuances.

?? Among ML-based methods, CatBoost with N-gram TF-IDF reached 86% accuracy, highlighting the continued relevance of classical NLP in certain environments.

?? LLaMA 3.1 (zero-shot) classification hit 81% accuracy, showcasing its potential for task generalization without fine-tuning.

What This Means for NLP in Turkish

These results reinforce a growing trend: LLMs are revolutionizing text classification, even for languages with limited resources. Fine-tuning transformer-based models unlocks remarkable improvements over classical ML approaches, making automated Turkish text classification more feasible than ever.

However, the study also highlights the importance of computational efficiency. While LLMs deliver exceptional accuracy, traditional models still hold value in resource-limited settings, especially when large-scale GPUs aren't available.

What's Next?

As LLMs continue to evolve, we anticipate even greater performance gains for Turkish NLP applications. Future research could explore multilingual transformers, domain adaptation, and hybrid models combining the best of both worlds.

?? If you're working in AI, NLP, or machine learning, this research provides valuable insights into the evolving landscape of text classification in underrepresented languages.

Let’s keep the conversation going! How do you see LLMs transforming NLP for morphologically rich languages?

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