Course Description:
Named Entity Recognition (NER) is a fundamental task in Natural Language Processing (NLP), which involves identifying and classifying entities in text. In this course, students will learn the basic concepts of NER, the different methods and techniques used in NER, and how to build NER models using different NLP tools and libraries. The course will cover the entire NER pipeline, from data preparation and pre-processing to evaluation and model optimization.
What is Named Entity Recognition?
Named Entity Recognition (NER) is a sub-task of Natural Language Processing (NLP) that aims to identify and classify named entities in text into predefined categories such as names of persons, organizations, locations, time expressions, quantities, and monetary values. It involves analyzing the text to recognize and extract specific entities and then categorizing them based on their attributes. NER is widely used in a variety of applications, such as information retrieval, chatbots, sentiment analysis, and text classification.
Why should you learn Name Entity Recognition?
You should learn Named Entity Recognition (NER) because it is a crucial task in Natural Language Processing (NLP) that helps in identifying and extracting entities from unstructured text data. NER is used in various applications such as information retrieval, text summarization, sentiment analysis, machine translation, and question-answering systems. By learning NER, you can enhance your skills in NLP and develop solutions that can help organizations in automating their data analysis, improving customer service, and gaining insights from large volumes of text data. Moreover, NER is a field that is continuously evolving, and learning NER can help you keep up with the latest advancements in NLP.
Prerequisites:
Basic knowledge of Python programming and Natural Language Processing is required for this course.
Course Goals:
By the end of the course, students will be able to:
Understand the basic concepts and techniques used in Named Entity Recognition
Build and train NER models using different NLP tools and libraries
Evaluate NER models and optimize them for better performance
Pre-process text data for NER
Build a practical application using NER
Course Outline:
Introduction to Named Entity Recognition (NER)
What is Named Entity Recognition?
Why is NER important in NLP?
Applications of NER
Challenges in NER
Data Preparation and Pre-processing for NER
Understanding text data
Text data cleaning and normalization
Tokenization
Part-of-speech tagging
Chunking and parsing
Rule-based NER
Basic rule-based NER
Regular expressions for NER
Advanced rule-based NER
Statistical NER
Basic statistical NER
Hidden Markov Models (HMM) for NER
Conditional Random Fields (CRF) for NER
Deep Learning based NER
Evaluation and Optimization of NER Models
Metrics for evaluating NER models
Cross-validation
Hyperparameter tuning
Regularization
Practical Application of NER
Building a practical NER application
Data collection and preparation
Building and training an NER model
Evaluating and optimizing the NER model
Future of Named Entity Recognition
Recent advancements in NER
Potential areas of research and innovation
Emerging trends in NER
Through this course, students will gain an in-depth understanding of Named Entity Recognition and its applications. They will learn the basic concepts and techniques used in NER, and how to build and train NER models using different NLP tools and libraries. Students will also learn how to evaluate and optimize NER models for better performance and build a practical NER application.
How can Codersarts help in this project?
Consultation: Codersarts can provide expert consultation on your project and offer guidance on best practices for preprocessing text data, model selection, and deployment.
Custom Development: Codersarts can develop custom software solutions for your project, including data preprocessing tools, feature extraction scripts, and machine learning models for toxic comment classification.
Code Review: Codersarts can review your code and offer suggestions for improving efficiency, scalability, and maintainability.
Training: Codersarts can provide online training courses on natural language processing and machine learning to help you and your team develop the skills you need for your project.
Contact us
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