Regex (Regular Expressions) is a really useful tool in text processing, especially in Natural Language Processing (NLP). As a student still learning, I find Regex helpful in extracting specific information or patterns from unstructured text. For example, Regex can be used to search for emails, phone numbers, or hashtags within large amounts of text. It's also really useful for cleaning and preprocessing data, like removing unnecessary characters or fixing text formats. One common use of Regex I've noticed is in the preprocessing stage, where text from sources like social media or surveys often needs to be cleaned before further processing. While Regex doesn’t fully replace more advanced NLP techniques, like sentiment analysis or context understanding, it is still a very important tool that makes working with text faster and easier. This video provides a clear explanation of how Regex works and how we can use it in various NLP applications, and I think it’s a very interesting topic to dive deeper into.

Dear Rahul Jain ,

Natural language processing is a large and multidisciplinary field as it contains infinitely many sentences. NLP began in the 1950s as the intersection of artificial intelligence and linguistics. Also there is much ambiguity in natural language. There are many words which have several meanings, such as can, bear, fly, orange, and sentences have meanings different in different contexts. This makes creation of programs that understands a natural language, a challenging task

https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=83e2851bb3843ef8f7dfb9f882c8e0fdf5613751

Regards,

Shafagat

Thanks for sharing this, Rahul Jain! 🙌

The video is a fantastic introduction to the practical power of Regular Expressions in NLP. It highlights how even before diving into advanced models, mastering regex gives you a solid grip on structuring and cleaning raw, unstructured data—an absolutely essential step in any NLP workflow.

I especially liked how clearly the video breaks down regex syntax and shows use cases like detecting emails, hashtags, and even sarcasm patterns. These examples provide practical clarity for newcomers in applying concepts effectively.

From my experience, regex is incredibly useful in:

• Preprocessing pipelines — removing unwanted characters, standardizing inputs, and segmenting noisy data.
• Custom tokenization — particularly when working with domain-specific text (e.g., code snippets, chemical names, emojis, or log files).
• Initial rule-based filtering — before passing data into sentiment analysis, NER, or classification models.

While regex can’t handle deep semantics or context, it’s a powerful ally when it comes to preparing the battlefield for higher-level NLP.

Appreciate you sparking this conversation—great content, and a great reminder of how foundational tools like regex remain vital even in today’s ML-driven workflows. 👏

#RegexInNLP #DataCleaning #PatternMatching #TextProcessing