Extracting Meaningful Information from Turkish Chemistry and Physics Texts with Machine Learning

This study emphasizes the importance of processing a dataset consisting of Turkish chemistry and physics texts created by us through artificial intelligence systems. A model is proposed to pave the way for artificial intelligence-based analyses and discoveries in the basic sciences of chemistry and physics. Chemistry and physics, the basic sciences, are critical in many industrial, medical, and environmental applications. However, significant data analysis is required to access and understand information in these areas. This study aims to demonstrate the effectiveness of machine learning methods in extracting meaningful information from Turkish chemistry and physics texts. For this purpose, the tokenization process is first performed, and then the features are extracted with Term frequency-inverse document frequency (TF-IDF) and Bag-of-Words (BOW) methods. The combined features are classified separately with Support Vector Machine (SVM), Naive Bayes (NB), Quadratic Discriminant Analysis (QDA), k-nearest Neighbors (KNN), Logistic Regression (LR), Random Forest (RF), and Gradient Boosting (GB) algorithms. According to the classification results, the best calculation time and the most successful accuracy rate are obtained with NB at 95%. These results are essential for artificial intelligence systems to understand and process information correctly. It shows that scientists and researchers can access information faster and accelerate scientific discovery using Turkish sources. Such artificial intelligence models can also be essential in education, providing students with a more effective and personalized learning experience. Therefore, processing Turkish chemistry and physics texts with artificial intelligence systems is essential in including studies conducted in this language in global studies in scientific research, education, and industrial applications.