Natural Language Processing: Bridging the Human-Machine Divide
The development of NLP includes three eras, each with a different approach to machine communication:
A large model is pretrained on billions of words (e.g., internet text) using self-supervised objectives (masked word prediction, next sentence prediction). Then it is fine-tuned on a small, task-specific dataset. natural language processing
| Level | What it analyzes | Example | |-------|----------------|---------| | | Speech sounds | Distinguishing "bat" from "pat" | | Morphology | Word structure (roots, affixes) | "Running" → "run" + "-ing" | | Syntax | Sentence grammar & phrase structure | "The cat sat" (valid) vs. "Sat cat the" (invalid) | | Semantics | Literal meaning | "He kicked the bucket" (died? or literally kicked?) | | Pragmatics | Intended meaning in context | "It's cold in here" → request to close a window | | Discourse | Coherence across sentences | Pronouns ("it") linking back to previous nouns |
NLP is ubiquitous in modern technology. Key sectors include: "Sat cat the" (invalid) | | Semantics |
The future of NLP holds much promise, with potential applications in areas like:
Would you like a deeper dive into any specific subtopic, such as training your own small BERT model or implementing RAG? This led to (PLMs): NLP covers a broad
This led to (PLMs):
NLP covers a broad spectrum of tasks, generally categorized into and Natural Language Generation (NLG) .