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In this video tutorial, Amun, a data scientist, discusses the importance of cleaning techniques for natural language processing. Cleaning data is a fundamental step in the data science pipeline, which includes techniques such as missing value treatment and outlier treatment for numerical data. However, when dealing with text data, different techniques are required for cleaning. The video covers two main categories of text cleaning techniques: basic cleaning and advanced cleaning. Basic cleaning is essential and includes tasks like correcting spelling errors. Advanced cleaning may be needed in some cases.
