What is NLP?
Natural Language Processing is a field of AI focused on enabling computers to read, understand, and produce human language. Modern NLP combines linguistics, machine learning, and large datasets to support tasks like translation, Q&A, and summarization.
Core Tasks
- Text Classification: spam detection, topic labeling
- Named Entity Recognition (NER): find names, places, dates
- Sentiment Analysis: positive/negative/neutral feelings
- Machine Translation: translate between languages
- Question Answering / Chat: respond to user questions
- Summarization: shorten text while keeping key info
Common Techniques
- Tokenization & Normalization: split text, lowercasing, stemming/lemmatization
- Feature Representations: Bag-of-Words, TF-IDF, word embeddings (Word2Vec, GloVe)
- Neural Models: RNNs/LSTMs, CNNs for text
- Transformers: attention-based models (BERT, GPT) for state-of-the-art performance
Modern NLP: Transformers
Transformers use a mechanism called self-attention to understand relationships between words, even when they’re far apart. Pretrained language models are fine-tuned for tasks like classification, QA, and summarization with relatively small labeled datasets.
Zero-shot & Few-shot
Large models can perform new tasks from instructions or just a few examples, reducing labeled-data needs.
Safety & Bias
NLP systems may reflect training data bias; evaluate outputs and apply safeguards.
Evaluation
Use proper metrics: accuracy/F1 for classification, BLEU/ROUGE for translation/summarization.
Simple NLP Workflow
- Collect text & clean it (remove noise, normalize)
- Choose representation (TF-IDF or embeddings)
- Train or fine-tune a model
- Evaluate on held-out data
- Deploy & monitor for drift/safety
Practical Uses
- Customer support chatbots
- Auto-tagging emails or documents
- Summarizing meeting notes
- Language learning assistance