Key Techniques in NLP

Key Techniques in NLP Named Entity Recognition (NER): Identifies names, dates, locations, etc. Sentiment Analysis: Evaluates the tone of a text. Dependency Parsing: Analyzes grammatical structure. Machine Translation: Converts text between languages. NLP Frameworks and Tools NLTK (Natural Language Toolkit): A popular library for basic NLP tasks. spaCy: Advanced NLP processing with high performance. TensorFlow & PyTorch: Used for deep learning-based NLP models. Hugging Face Transformers: Pretrained models like BERT, GPT, and RoBERTa. Data Science Course in Pune. https://www.sevenmentor.com/data-science-course-in-pune.php

Future of NLP The future of NLP lies in:

Contextual Understanding: Improved models for nuanced language comprehension. Low-Resource Languages: Developing tools for underrepresented languages. Multimodal NLP: Integrating text, image, and audio processing. Ethical NLP: Ensuring fairness and reducing biases in AI systems.