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1
Intro
2
NLP Failures
3
Entities in Text
4
Class Objectives
5
NLP Systems
6
Feature Extraction
7
RuleBased Sentiment
8
Extract Features
9
Run Classifier
10
Labels
11
Plain Text
12
Live Coding
13
Tokenization
14
Error Analysis
15
Difficult Cases
16
Conjugation
17
Depth
18
PositiveNegative
19
NLP Methods
20
Neural Network Models
Description:
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.

CMU Advanced NLP: Introduction to NLP

Graham Neubig
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