Overview
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Explore the fundamentals of Natural Language Processing (NLP) in this introductory lecture from CMU's Advanced NLP course. Delve into the core concepts of NLP, including its definition, key features of natural language, practical applications, and inherent challenges. Learn about various NLP systems, feature extraction techniques, rule-based sentiment analysis, and the importance of tokenization. Examine error analysis methods, difficult cases in language processing, and the role of neural network models in modern NLP approaches. Gain insights into entity recognition, class objectives, and the intricacies of positive and negative sentiment analysis through live coding demonstrations and in-depth discussions.
Syllabus
Intro
NLP Failures
Entities in Text
Class Objectives
NLP Systems
Feature Extraction
RuleBased Sentiment
Extract Features
Run Classifier
Labels
Plain Text
Live Coding
Tokenization
Error Analysis
Difficult Cases
Conjugation
Depth
PositiveNegative
NLP Methods
Neural Network Models
Taught by
Graham Neubig