2012机器学习欧洲暑期学校课件-9(共9个).pdfVIP

Learning Structured Predictors Xavier Carreras Universitat Polit`ecnica de Catalunya thanks to: M. Collins, A. Globerson, T. Koo, A. Quattoni Supervised (Structured) Prediction ◮ Learning to predict: given training data (1) (1) (2) (2) (m) (m) (x , y ), (x , y ), . . . , (x , y ) learn a predictor x → y that works well on unseen inputs x ◮ Non-Structured Prediction: outputs y are atomic ◮ Binary prediction: y ∈ {−1, +1} ◮ Multiclass prediction: y ∈ {1, 2, . . . , L} ◮ Structured Prediction: outputs y are structured ◮ Sequence prediction: y are sequences ◮ Parsing: y are trees ◮ . . . Named Entity Recognition y per - qnt - - org org - time x Jim bought 300 shares of Acme Corp. in 2006 Named Entity Recognition y per - qnt - - org org - time x Jim bought 300 shares of Acme Corp. in 2006 y per per - - loc x Jack London went to Paris y per per - - loc x Paris Hilton went to London Part-of-speech Tagging y NNP NNP VBZ NNP . x Ms. Haag plays Elianti . Syntactic Parsing * John saw a movie that he liked today x are sentences y are syntactic dependency trees Machine Translation (