Named Entity Recognition (NER) in NLP 🧬 || python for beginners
Hello Guys,
Welcome to Day 86 of our data science journey! In this tutorial, we delve into the fascinating world of Named Entity Recognition (NER) in Natural Language Processing (NLP). Join us as we unravel the intricacies of NER and explore its crucial role in extracting and identifying named entities from unstructured text data.
Named Entity Recognition is a fundamental NLP technique that involves identifying and classifying named entities within a text, such as persons, organizations, locations, dates, and more. In this video, we provide an in-depth exploration of NER and its applications across various domains, including information extraction, question answering, entity linking, and text summarization.
Here's what we cover in this tutorial:
Introduction to Named Entity Recognition (NER): Understanding the concept and objectives of NER in NLP.
Tokenization and Part-of-Speech Tagging: Preprocessing text data using tokenization and POS tagging techniques.
Named Entity Recognition (NER) Process: Leveraging the NLTK library in Python to perform NER on tokenized text data.
NER Examples: Demonstrating NER on sample text data and identifying named entities such as persons, organizations, and locations.
NER Applications: Discussing real-world applications of NER in NLP tasks, including information extraction, entity disambiguation, and knowledge graph construction.
Join us as we unravel the mysteries of Named Entity Recognition and discover how this powerful NLP technique enables machines to identify and classify named entities with remarkable accuracy and precision. Whether you're a beginner or an experienced practitioner, this tutorial will provide valuable insights into the foundations of NER and its practical applications in NLP.
Don't forget to like, share, and subscribe for more informative tutorials on Natural Language Processing, machine learning, and data science. Stay tuned for our next adventure as we delve deeper into the exciting realm of NLP!
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