Statistical Learning for Structured Prediction with Applications to NLP
Seminar: Computerlinguistik, B.Sc./M.Sc.Leitung: Titov
Ort: Geb. C 7.2, Konferenzraum 2.11
Zeit: Mi 10-12 Uhr
Beginn: wird noch bekanntgegeben
Geeignet für: B.Sc./M.Sc.
The class will cover machine learning methods for structured prediction problems, the main focus will be on problems from natural language processing but most of the considered methods will have applications in other domains (e.g., bioinformatics, vision, information retrieval, etc).
Structured prediction problems are classification problems where the classifier predicts not a binary/multiclass label but rather an element of some structured space. Examples of structured problems include sequence labeling problems, segmentation problems, parsing (syntactic or semantic in NLP or, e.g, image parsing in vision) and many others.
In the class we will cover most of the state-of-the-art methods for this class of problems: starting from hidden Markov models, structured perceptron, conditional random fields to more advanced techniques such as structured SVM, Searn and others.
Though most of the applications considered papers will be from the NLP domain, I do not require any prior exposure to NLP (though it would be a plus). Ideally, I expect that you have some prior experience with machine learning, statistical NLP or IR. If hesitant, do not hesitate to contact me and ask.
B.Sc. Computerlinguistik: 7 LP (Vortrag und Hausarbeit);
M.Sc. Language Science and Technology: 7 CP (presentation and paper); 4 CP (presentation only) - please register for the right version in HISPOS