Discover the sophisticated machinery beneath the surface of natural language processing. From structural parsing to statistical prediction.
Language is an intricate tapestry. Parsing is the process of deciphering this structure, breaking text into constituent parts to illuminate grammatical relationships.
Parsers create "parse trees" to resolve ambiguity (like "I saw the man with the telescope"). This structure is crucial for machine translation, sentiment analysis, and voice synthesis.
Start with the highest-level grammar rule and work down.
Start with individual words and build up the structure.
Before parsing relationships, we must understand individual words. POS tagging categorizes words (noun, verb, adjective) to build the bedrock for syntactic analysis and Named Entity Recognition.
Microsoft announced on Tuesday that John Smith will visit New York City to discuss a $10 million investment plan. The summit is scheduled for April 2023.
If POS tagging is the foundation, NER is the detective work. It sifts through unstructured text to locate and classify specific information gems: names, organizations, locations, dates, and monetary values.
The engine of modern NLP. Unlike static rules, ML algorithms learn from data. From Support Vector Machines to Deep Learning neural networks, these systems adapt and improve over time.
The theoretical foundation. Statistical models handle the ambiguity of language by calculating probabilities. They predict the likelihood of word sequences (N-grams) to make sense of chaotic data.
"Deep learning models, at their core, also rely on statistical techniques to learn from data... minimizing the discrepancy between predictions and actual data."