Overview of NLP Core Methods 🌐
Natural Language Processing (NLP) is a field of artificial intelligence that focuses on the interaction between computers and humans through language. Below are key techniques commonly used in NLP:
Word Embedding 🤖
- Converts text into numerical vectors to capture semantic relationships.
- Popular models: Word2Vec, GloVe, FastText
Machine Learning Models 🧠
- Traditional approaches like Naive Bayes, SVM, and CRF for tasks such as sentiment analysis and text classification.
Deep Learning & Neural Networks 🎯
- Modern methods using RNNs, LSTMs, and Transformers (e.g., BERT) for complex language understanding.
Text Preprocessing 🔧
- Includes tokenization, stemming, lemmatization, and stopword removal to clean raw text data.
Advanced Topics 🚀
For deeper exploration, check our NLP Advanced Concepts Tutorial to learn about:
- Language models (e.g., GPT, T5)
- Named Entity Recognition (NER)
- Machine translation pipelines
- Contextual embeddings
Practical Tools 💡
- spaCy for rule-based processing
- Hugging Face Transformers for pre-trained models
- NLTK for text analysis tasks
Stay Updated 📈
Follow our NLP Techniques Blog for the latest research and tools in the field.