Text Preprocessing Pipeline — Build Your Own
This blog post is more of a hands-on demonstration to list the most important steps and elements to build an efficient natural language processing pipeline using Python. From getting the data, to passing it to an algorithm, to cleaning it, the idea is to have a clear process that improves efficiency.
Text preprocessing is a critical step in natural language processing and machine learning projects. It involves cleaning, transforming, and preparing raw text data for use in algorithms and models. However, text preprocessing can be a challenging and time-consuming process, especially when dealing with large and complex datasets. That’s why the goal of this post is to provide a comprehensive guide to building a text preprocessing pipeline that addresses these challenges and produces high-quality, preprocessed data for use in ML models.
A well-designed pipeline can save time by automating many of the manual preprocessing tasks and reducing the need for manual intervention. This can help data scientists focus on more complex tasks, such as feature engineering and model selection. In addition, a pipeline can help data scientists easily experiment with different preprocessing techniques and configurations, allowing them to fine-tune their models and achieve even better results.
