{"id":32149,"date":"2024-11-01T09:06:04","date_gmt":"2024-11-01T09:06:04","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=32149"},"modified":"2024-11-01T11:19:54","modified_gmt":"2024-11-01T11:19:54","slug":"deep-learning-for-natural-language-processing-splitting-data","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/32149\/","title":{"rendered":"Deep Learning for Natural Language Processing, Splitting Data"},"content":{"rendered":"

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Natural language processing is one of the fastest-growing fields in today’s artificial intelligence sector. In particular, the advancement of deep learning technologies has brought about revolutionary changes in solving natural language processing (NLP) problems. In this article, we will explain in detail the data processing processes that can occur in NLP, particularly the importance of data splitting. Data splitting is a critical factor that significantly affects the performance of models and must be conducted using the correct methods.<\/p>\n

1. The Importance of Data Splitting<\/h2>\n

Data splitting is one of the fundamental tasks in data science and machine learning. Since the quality of the data determines the success or failure of the model, the process of splitting data into training, validation, and test sets is very important. If the data is not well separated, the model may overfit or fail to generalize.<\/p>\n

2. Basic Concepts of Data Splitting<\/h2>\n

Generally, to train a natural language processing model, three types of data sets are used:<\/p>\n