Statistical Learning for Structured Prediction with Applications to Natural Language Processing

Instructor: Ivan Titov
Time: Wednesday, 10.15 - 11.45 am
Location: Building C 7.2, room 2.11 (may be changed later)
Office hours: Wednesday, 2 pm - 3 pm in Ivan's office (C7.4, 3.22), or send me a message by e-mail.
Note: First class is on October 29
Note: Second class: November 10 (no class during the week of November 1 - 5)

Short Description

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, feel free to contact me and ask.



Attendance policy

You can skip ONE class without giving any explanation to me (if it is not the class on which you are presenting). If you need to skip more, you will need to write an additional critical review for every paper presented while you were absent.


Critical reviews

Term paper


Grading criteria:

Length: 12 - 15 pages

Deadline: Available Later I would recommend to submit it soon after your presentation, as it would probably be easy.

Submitted in PDF to my email

Topics (some changes possible)

Note: References to papers, dates, and speakers are provided in the Google Docs (a reference was sent to attenders)

Past seminars (for information about future seminars see the Google Doc)