Semi-Supervised Learning and Domain Adaptation in Natural Language Processing

日時 内容　　　　　　　　　　　　　　　　　　　　　　　 スライドのファイル 補足資料など 発表者
10月22日 Introduction
1.1 Introduction
1.2 Learning under Bias		 da-kunii-1022.pdf 國井
1.3 Empirical Evaluations da-kikuchi-1022.pdf 菊池
10月29日 2 Supervised and Unsupervised Prediction
2.1 Standard Assumptions in Supervised Learning
2.1.1 How to Check whether the Assumptions Hold		 da-xiao-1029.pdf Xiao
2.2 Nearest Neighbor
2.3 Naive Bayes		 da-onodera-1029.pdf 小野寺
11月5日 2.4 Perceptron
2.4.1 Large-margin Methods		 da-yoshida-1105.pdf 吉田
2.5 Comparisons of Classification Algorithms
2.6 Learning from Weighted Data
2.6.1 Weighted k-nearest Neighbor
2.6.2 Weighted Naive Bayes
2.6.3 Weighted Perceptron
2.6.4 Weighted Large-margin Learning		 da-kunii-1105.pdf 國井
11月12日 2.7 Clustering Algorithms
2.7.1 Hierarchical Clustering
2.7.2 k-means
2.7.3 Expectation Maximization
2.7.4 Evaluating Clustering Algorithms		 da-kikuchi-1112.pdf 菊池
11月12日 2.8 Part-of-speech Tagging
2.9 Dependency Parsing
2.9.1 Transition-based Dependency Parsing
2.9.2 Graph-based Dependency Parsing		 da-xiao-1112.pdf Xiao
11月19日 3 Semi-Supervised Learning
3.1 Wrapper Methods
3.1.1 Self-training
3.1.2 Co-training
3.1.3 Tri-training
3.1.4 Soft Self-training, EM and co-EM		 da-onodera-1119.pdf 小野寺
3.2 Clusters-as-features
3.3 Semi-supervised Nearest Neighbor
3.3.1 Label Propagation
3.3.2 Semi-supervised Nearest Neighbor Editing
3.3.3 Semi-supervised Condensed Nearest Neighbor		 da-yoshida-1119.pdf 吉田
12月3日 4 Learning under Bias
4.1 Semi-supervised Learning as Transfer Learning
4.2 Transferring Data
4.2.1 Outlier Detection
4.2.2 ImportanceWeighting		 da-kunii-1203.pdf 國井
11月26日 4.3 Transferring Features
4.3.1 Changing Feature Representation to Minimize Divergence
4.3.2 Structural Correspondence Learning
4.4 Transferring Parameters		 da-kikuchi-1126.pdf 菊池
12月3日 5 Learning under Unknown Bias
5.1 Adversarial Learning
5.2 Ensemble-based Methods and Meta-learning		 da-xiao-1203.pdf Xiao
6 Evaluating under Bias
6.1 What is Language?
6.2 Significance Across Corpora		 da-onodera-1203.pdf 小野寺
12月10日 6.3 Meta-analysis
6.4 Performance and Data Characteristics
6.5 Down-stream Evaluation		 da-yoshida-1210.pdf 吉田