斯坦福NLP(自然语言处理)技术教程
【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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