Welcome to our collection of NLP tutorials! Whether you're a beginner or an experienced developer, these tutorials will help you understand and implement various NLP techniques.
Basics of NLP
- What is NLP? Natural Language Processing (NLP) is a field of artificial intelligence that focuses on the interaction between computers and humans through natural language.
- Why NLP? NLP enables computers to understand, interpret, and generate human language, making it an essential technology for applications like chatbots, translation services, and sentiment analysis.
Tutorials
1. Text Preprocessing
Text preprocessing is the first step in most NLP tasks. It involves cleaning and preparing the text data for further analysis.
- Tokenization: Splitting text into words or tokens.
- Stopword Removal: Removing common words that do not contribute much meaning.
- Stemming/Lemmatization: Reducing words to their base or root form.
2. Sentiment Analysis
Sentiment analysis is the process of determining whether a piece of text is positive, negative, or neutral.
- VADER Sentiment Analysis: An example of a rule-based sentiment analysis tool.
- TextBlob Sentiment Analysis: An example of a library-based sentiment analysis tool.
3. Named Entity Recognition (NER)
Named Entity Recognition (NER) is the process of identifying and classifying named entities in text.
- SpaCy NER: An example of a library-based NER tool.
- Stanford CoreNLP NER: An example of a rule-based NER tool.
4. Language Modeling
Language modeling is the task of predicting the next word in a sequence of words.
- RNN Language Models: Recurrent Neural Networks for language modeling.
- Transformer Language Models: State-of-the-art models like BERT and GPT.
Resources
For further reading, we recommend checking out the following resources:
Natural Language Processing