https://github.com/nayankanaparthi/regx_demo
RegEX demo
https://github.com/nayankanaparthi/regx_demo
Last synced: 7 months ago
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RegEX demo
- Host: GitHub
- URL: https://github.com/nayankanaparthi/regx_demo
- Owner: NayanKanaparthi
- Created: 2023-07-11T13:24:04.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-07-11T13:29:19.000Z (over 2 years ago)
- Last Synced: 2025-02-05T21:59:34.378Z (9 months ago)
- Language: Jupyter Notebook
- Size: 4.88 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Regular Expression Regex Code Demo Tutorial
This tutorial demonstrates how to use regular expressions (regex) to extract information from text. The code in the tutorial extracts mobile numbers, email addresses, and order numbers from text. It also extracts personal information from a biography, such as the person's age, full name, date of birth, and place of birth.
The tutorial is written in Python and uses the re module to perform the regex operations. The code is well-commented and easy to follow.
## Prerequisites
Basic knowledge of Python
Basic knowledge of regular expressions
How to use the tutorial
## Clone the GitHub repository.
Open the RegEX.ipynb file in a Python IDE.
Run the code.
The code will extract the information from the text and print it to the console.
## Who should use this tutorial?
This tutorial is a good resource for anyone who wants to learn how to use regular expressions to extract information from text. It is also a good resource for Python developers who want to learn how to use the re module.
## Why are Regular Expressions useful:
Regular expressions are a powerful tool for extracting information from text. This makes them a valuable tool for NLP chatbots, which need to be able to understand and respond to natural language queries.
One way that RegEx is used in NLP chatbots is to extract entities from text. Entities are things like names, dates, locations, and products. By extracting entities from text, chatbots can better understand the context of a user's query and provide more relevant responses.
For example, a chatbot that is designed to help users book flights might use RegEx to extract the departure and arrival airports from a user's query. Once the chatbot has extracted these entities, it can then use them to search for flights that match the user's criteria.
RegEx can also be used to filter text. This can be useful for chatbots that need to remove spam or profanity from user queries. For example, a chatbot that is designed to help users with customer service issues might use RegEx to filter out any text that contains the word "spam."
In addition to extracting entities and filtering text, RegEx can also be used to format text. This can be useful for chatbots that need to generate specific types of output, such as emails or invoices. For example, a chatbot that is designed to help users with their finances might use RegEx to format a user's financial information into a report.
Overall, regular expressions are a powerful tool that can be used to improve the functionality of NLP chatbots. By extracting entities, filtering text, and formatting text, RegEx can help chatbots to better understand user queries and provide more relevant responses.
Here are some additional examples of how RegEx is used in NLP chatbots:
To identify the intent of a user's query.
To extract keywords from a user's query.
To match patterns in a user's query.
To validate the format of a user's input.
RegEx is a complex topic, but it is a valuable tool for NLP chatbot developers. By understanding how to use RegEx, developers can create chatbots that are more powerful and efficient.
### Here are some additional resources that you may find helpful:
Regex101: https://regex101.com/