Natural Language Processing (NLP)
Natural Language Processing (NLP) is a branch of artificial intelligence that enables computers to understand, interpret, and generate human language. It sits at the intersection of linguistics, computer science, and machine learning, aiming to make interactions between humans and machines as seamless as possible.
Historical context
NLP began in the mid-20th century with rule-based systems and symbolic approaches, such as early machine translation experiments. The 1990s saw a shift to statistical models, which leveraged probabilities to handle ambiguity. Today, deep learning has revolutionized NLP, with models like BERT, GPT, and large-scale Transformers delivering state-of-the-art results across many tasks.
Applications
- Machine translation (Google Translate, DeepL).
- Sentiment analysis for social media monitoring or customer feedback.
- Chatbots and virtual assistants like Siri or Alexa.
- Text summarization and question answering in search engines and productivity tools.
- Healthcare: analyzing clinical notes to assist medical decisions.
Challenges
- Ambiguity: Words can have multiple meanings depending on context.
- Bias: Models trained on internet data may reproduce stereotypes.
- Multilingualism: Many languages remain underrepresented in datasets.
- Ethics: NLP applications in surveillance or misinformation detection raise concerns.
Why it matters
NLP has transformed how humans interact with technology: from voice assistants and translation apps to content moderation. It is one of the most active research areas in AI and central to the development of generative models.
📚 Further Reading
- Jurafsky, D., & Martin, J. H. (2023). Speech and Language Processing.
- Devlin, J. et al. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.
