Tokenization 🧩
Tokenization divides text into smaller units, improving the text analysis process for various NLP tasks.
Stemming 🌿
Stemming simplifies words to their stem forms, facilitating a basic level of understanding for machine processes.
Lemmatization 📜
Lemmatization involves more sophisticated analysis to reduce words to their lemmatized form based on actual language usage.
Named Entity Recognition (NER) 👥
NER identifies entities within text, classifying them into categories like person, organization, or location, crucial for information extraction.
Syntax Analysis 🌲
Syntax Analysis utilizes parsing techniques to understand and interpret the grammatical structure of sentences.
Semantic Analysis 🧠
Semantic Analysis goes beyond the literal meaning to understand nuance, intent, and contextual meanings in text.
Machine Learning Models 🤖
Models such as NLP classifiers and neural networks learn from data to predict outcomes and interpret new input.
Deep Learning in NLP 🌐
Deep learning models, especially those using transformers, have revolutionized how machines understand human language by capturing deeper levels of language complexity.
Cloud NLP Services ☁️
Cloud-based NLP services provide scalable solutions for businesses to deploy NLP tasks without investing in hardware infrastructure.