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How is data typically collected for NLP projects?
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In Natural Language Processing (NLP), the success of a model depends heavily on the quality and volume of data used for training, validation, and testing.
Data collection is a the most important step that involves gathering text, audio, or other language-related inputs from various sources. Since NLP systems learn from large datasets, the process of data collection needs to be strategic and thorough to ensure that the models can generalize well to real-world applications.
The following are the common methods and sources used to collect data for NLP projects:
1. Web Scraping:
Method: Web scraping is one of the most common techniques for collecting large amounts of text data from the internet. It involves using automated tools or scripts (such as BeautifulSoup or Scrapy in Python) to extract text from websites…
