Foundational NLP Techniques
Text Preprocessing
Includes tokenization, stemming/lemmatization, stopword removal, and text normalization to prepare raw text for analysis.
Sentiment Analysis
Algorithms that detect emotional tone and subjective information in text, enabling analysis of positive/negative/neutral sentiment.
Part-of-Speech Tagging
Identifies grammatical parts of speech (nouns, verbs, adjectives, etc.) to understand sentence structure and meaning.
Modern NLP Innovations
Transformer Models
Self-attention architecture that enables contextual understanding of entire text sequences simultaneously (BERT, GPT, etc.).
Learn More →Named Entity Recognition
Systematically identifies and classifies entities like people, organizations, locations, and dates from text.
Discover Details →Text Generation
Algorithms produce contextually relevant text based on prompts, including creative writing, summarization, and dialogue generation.
Explore Methods →Core Techniques in Action
Machine Translation
Systems that accurately translate text between languages while preserving context and meaning.
Speech Recognition
Converts spoken language to text with high accuracy across various accents and languages.
Essential NLP Tooling
spaCy
Industrial-strength NLP library for text processing with pre-trained models.
Transformers
Hugging Face's library for pre-trained transformer models and fine-tuning.
TextBlob
Simple NLP library with built-in sentiment analysis and POS tagging.