Natural Language Processing (NLP) Resources

Natural Language Processing (NLP) is a field of artificial intelligence that focuses on the interaction between computers and humans through natural language. This page provides resources and information about NLP.

Key Concepts

• Tokenization: The process of breaking text into words, phrases, symbols, or other meaningful elements called tokens.
• Part-of-Speech Tagging: Assigning a part of speech to each word in a sentence, such as noun, verb, adjective, etc.
• Named Entity Recognition (NER): Identifying and categorizing entities in text, such as names, locations, organizations, etc.
• Sentiment Analysis: Determining the sentiment or opinion expressed in a piece of text.

Resources

Here are some resources to help you learn more about NLP:

• Books:

  • "Speech and Language Processing" by Daniel Jurafsky and James H. Martin
  • "Natural Language Processing with Python" by Steven Bird, Ewan Klein, and Edward Loper

• Online Courses:

  • Coursera - Natural Language Processing
  • edX - Natural Language Processing with Python

• Websites:

  • NLTK (Natural Language Toolkit)
  • spaCy

• Documentation:

  • TensorFlow Text
  • PyTorch Text

Case Study

NLP in Action

In this case study, we explore how NLP can be used to analyze customer reviews and sentiment.

Further Reading

For more information on NLP, check out our Machine Learning Resources.

If you have any questions or need further assistance, please don't hesitate to reach out.