Natural Language Processing (NLP)

Natural Language Processing (NLP) is a field of AI that enables computers to understand, interpret and generate human language. NLP combines linguistics and computer science to process written and spoken words in useful ways.

What is NLP used for?

• Conversational interfaces: Chatbots and voice assistants use NLP to understand user requests and generate natural responses.
• Text analysis: Sentiment analysis, topic modelling and entity recognition help businesses analyse customer feedback, social media and reviews.
• Machine translation: Services like Google Translate convert text between languages using deep learning models.
• Summarisation & search: Automatic summarisation condenses documents, while information retrieval systems use NLP to match queries to relevant documents.
• Speech recognition & synthesis: Converting spoken language to text and vice versa powers dictation, subtitles and voice assistants.

Key Techniques

• Tokenisation & normalisation: Breaking text into tokens (words or subwords) and converting to a consistent format (lowercase, removing punctuation) prepare it for processing.
• Embeddings: Techniques like word2vec, GloVe and transformers convert words into numerical vectors that capture semantic meaning. Contextual embeddings (BERT, GPT) produce representations based on surrounding words.
• Sequence models: Recurrent neural networks (RNNs), long short‑term memory (LSTM) networks and transformers model relationships across sentences and paragraphs for tasks like translation and summarisation.
• Named‑entity recognition (NER): Detecting and classifying entities (names, organisations, locations, dates) in text helps structure unstructured data.
• Sentiment analysis: Classifying text as positive, negative or neutral aids brand monitoring and customer service.

Best Practices & Considerations

• Context matters: Words may have different meanings based on their context; using contextual embeddings improves understanding.
• Domain adaptation: Fine‑tuning general models on domain‑specific data (legal, medical, financial) yields better performance.
• Bias & fairness: Language models can perpetuate bias from their training data; careful evaluation and mitigation are essential.
• Privacy: NLP systems may process sensitive information; ensure data is anonymised and compliant with relevant laws.

Free Resources

• Natural Language Toolkit (NLTK) – A comprehensive Python library for tokenisation, parsing and basic NLP tasks.
• spaCy – Industrial‑strength NLP library with pre‑trained models, excellent performance and easy integration.
• Hugging Face Transformers – Library for state‑of‑the‑art transformer models (BERT, GPT, T5) with simple APIs for fine‑tuning.