斯坦福NLP（自然语言处理）技术教程

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【课程内容】

NLP Stanford

Week 1

• Course Introduction
• Basic Text Processing
• Edit Distance
  

Week 2

• Language Modeling
• Spelling Correction
  

Week 3

• Text_Classification
• Sentiment_Analysis
  

Week 4

• Discriminative classifiers- Maximum Entropy classifiers
• Named entity recognition and Maximum Entropy Sequence Models
• Relation Extraction
  

Week 5

• Advanced Maximum Entropy Models
• POS Tagging
• Parsing Introduction
• Instructor Chat
  

Week 6

• Probabilistic Parsing
• Lexicalized Parsing
• Dependency Parsing Optional
  

Week 7

• Information Retrieval
• Ranked Information Retrieval
  

Week 8

• Semantics
• Question Answering
• Summarization
• Instructor Chat II
  

Natural Language Processing Collins

Week 1

• Introduction to Natural Language Processing
• The Language Modeling Problem
• Parameter Estimation in Language Models
• Summary
  

Week 2

• Tagging Problems and Hidden Markov Models
  

Week 3

• Parsing and Context-Free Grammars
• Probabilistic Context-Free Grammars
  

Week 4

• Weaknesses of PCFGs
• Lexicalized PCFGs
  

Week 5

• Introduction to Machine Translation
• The IBM Translation Models
  

Week 6

• Phrase-based Translation Models
• Decoding of Phrase-based Translation Models
  

Week 7

• Log-linear Models
  

Week 8

• Log-linear Models for Tagging
• Log-Linear Models for History-based Parsing
  

Week 9

• Unsupervised Learning- Brown Clustering
• Global Linear Models
  

Week 10

• GLMs for Tagging
• GLMs for Dependency Parsing
  

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