What is NLP?

Natural Language Processing (NLP) is the area of artificial intelligence that enables computers to understand, interpret, generate, and respond to human language — both written and spoken. NLP blends linguistics, statistics, and machine learning to make sense of text and speech.

Core Tasks in NLP

• Tokenization: splitting text into words, subwords, or sentences.
• POS Tagging: identifying parts of speech (noun, verb, etc.).
• Named Entity Recognition (NER): finding names, places, dates.
• Sentiment Analysis: determining polarity (positive/negative/neutral).
• Text Classification: assigning labels (spam detection, topic labels).
• Machine Translation: translating between languages.
• Question Answering & Retrieval: finding or generating answers to questions.
• Summarization: producing concise summaries of longer text.
• Language Generation: producing coherent text (chatbots, creative writing).

Key Techniques & Concepts

Practical Workflow for an NLP Project

1. Define the problem and evaluation metric (accuracy, F1, BLEU, ROUGE).
2. Collect and clean data (deduplicate, normalise text, handle missing values).
3. Choose representation (bag-of-words, TF-IDF, embeddings).
4. Select model (logistic regression → transformer) and baseline.
5. Train, validate, and tune hyperparameters; monitor overfitting.
6. Evaluate on held-out test set; consider human evaluation for generation tasks.
7. Deploy with monitoring (data drift, performance) and update model over time.

Common Applications

• Chatbots and virtual assistants (customer support, voice agents).
• Document summarization and information extraction.
• Sentiment and opinion analysis for market research.
• Search engines and semantic retrieval.
• Spam detection, content moderation, and compliance monitoring.
• Machine translation and cross-lingual tools.
• Clinical text processing and healthcare record analysis.

Limitations & Challenges

• Data Quality: noisy, biased, or limited data degrades models.
• Ambiguity & Context: language is often ambiguous, context-dependent.
• Resource Constraints: large models require compute and storage.
• Domain Shift: models trained on one domain may fail on another.
• Evaluation Difficulty: automatic metrics don’t always match human judgment.

Ethics & Safety Considerations

• Bias & Fairness: models can amplify social biases present in data.
• Privacy: handling personal data requires strong protections and consent.
• Misuse: generation models can create misinformation or harmful content.
• Transparency: document model limitations, training data sources, and intended use.

Quick Reference Table — Techniques & When to Use

Practical Tips for Beginners

• Start with clear, small problems (sentiment on a dataset), not building a general chatbot.
• Use pretrained models and fine-tune — they save time and data (e.g., Hugging Face models).
• Always build a simple baseline — it prevents overestimating complex models’ gains.
• Focus on data cleaning and labelling quality — often more impactful than model choice.
• Monitor model fairness and test on slices of data (different dialects, demographics).

Resources to Learn (Beginner → Intermediate)

• Introductory courses: Coursera / edX NLP basics and Stanford’s CS224N (for deeper dive).
• Practical libraries: spaCy, NLTK, Hugging Face’s transformers.
• Datasets: GLUE, SQuAD, IMDb reviews for sentiment, or domain-specific corpora.
• Tools: Jupyter notebooks, Colab (GPU access), and small-scale experiments before scaling up.

FAQs