Welcome to this tutorial on Natural Language Processing (NLP)! NLP is a branch of artificial intelligence that deals with the interaction between computers and humans through natural language. It involves understanding, interpreting, and generating human language.
Basics of NLP
Text Preprocessing: Cleaning and preparing text data for NLP tasks.
- Tokenization: Splitting text into words or sentences.
- Stopwords Removal: Removing common words that do not add much meaning.
- Stemming/Lemmatization: Reducing words to their base or root form.
Feature Extraction: Extracting meaningful information from text data.
- Bag of Words: Representing text as a collection of words.
- TF-IDF: Term Frequency-Inverse Document Frequency, a measure of importance.
Modeling: Building models to analyze and understand language.
- Naive Bayes: A probabilistic classifier based on Bayes' theorem.
- Support Vector Machines (SVM): A supervised learning model.
Useful Resources
- Python NLP Libraries: A comprehensive guide to Python libraries for NLP.
NLP Flowchart
Conclusion
NLP is a rapidly growing field with numerous applications in various industries. By understanding the basics of NLP, you can leverage its power to create innovative solutions.
For further reading, explore our NLP Resources.