What is Natural Language Processing?
Natural Language Processing (NLP) is a subfield of artificial intelligence that enables computers to understand, interpret, and generate human language.
- Transforms unstructured text into structured data.
- Supports tasks such as sentiment analysis, entity recognition, translation, and summarization.
- Bridges the gap between human communication and machine computation.
How NLP Systems Operate
NLP pipelines combine linguistic knowledge with statistical and neural methods to process text.
Rule‑Based Approaches
- Define explicit patterns (e.g., regular expressions, grammars).
- Effective for well‑structured domains like medical coding where standards exist.
- Easy to audit and modify but brittle to language variation.
Large Language Model (LLM) Approaches
- Leverage deep neural networks trained on massive corpora.
- Capture contextual meaning and generate fluent text.
- Adaptable to diverse tasks via prompting or fine‑tuning.
Hybrid Systems
- Combine rule‑based precision with LLM flexibility.
- Example: Llettuce maps messy medical records to standard codes by first applying rules to detect clinical entities, then using an LLM to resolve ambiguities.
- Enable fine‑grained category identification while maintaining interpretability.
Why NLP Is Critical
Effective NLP drives automation, insight, and accessibility across industries.
- Healthcare: Automates coding of electronic health records, improves billing accuracy, and supports clinical decision‑making.
- Business: Analyzes customer feedback, powers chatbots, and extracts market intelligence.
- Research: Enables large‑scale literature mining and knowledge graph construction.
- Societal Impact: Facilitates language translation, accessibility tools, and information retrieval for diverse populations.