structured output prediction なんですが、sequential labeling に特化した2006年の論文です。
少ないデータで学習する系統
Prototypeを使った Unsupervised Learning。
Prototype-Driven Learning for Sequence Models
http://
Semi-Supervised Conditional Random Fields for Improved Sequence Segmentation and Labeling
http://
Semi-Markov を速くする系統
Improving the Scalability of Semi-Markov Conditional Random Fields for Named Entity Recognition
http://
Efficient inference on sequence segmentation models
http://
F-measureが大事
Training Conditional Random Fields with Multivariate Evaluation Measures
http://
NER Systems that Suit Users Preferences: Adjusting the
Recall-Precision Trade-off for Entity Extraction
http://
その他
Segment-based Hidden Markov Models for Information Extraction
http://
An Effective Two-Stage Model for Exploiting Non-Local Dependencies in Named Entity Recognition
http://
少ないデータで学習する系統
Prototypeを使った Unsupervised Learning。
Prototype-Driven Learning for Sequence Models
http://
Semi-Supervised Conditional Random Fields for Improved Sequence Segmentation and Labeling
http://
Semi-Markov を速くする系統
Improving the Scalability of Semi-Markov Conditional Random Fields for Named Entity Recognition
http://
Efficient inference on sequence segmentation models
http://
F-measureが大事
Training Conditional Random Fields with Multivariate Evaluation Measures
http://
NER Systems that Suit Users Preferences: Adjusting the
Recall-Precision Trade-off for Entity Extraction
http://
その他
Segment-based Hidden Markov Models for Information Extraction
http://
An Effective Two-Stage Model for Exploiting Non-Local Dependencies in Named Entity Recognition
http://
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