Difference between revisions of "Structured Prediction 10-710 in Fall 2011"

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* [[Wikipedia Infobox Generator Using Cross Lingual Unstructured Text]] - [[User:Daegunw|Daegun Won]] and [[User:Aanavas|Tony Navas]]
 
* [[Wikipedia Infobox Generator Using Cross Lingual Unstructured Text]] - [[User:Daegunw|Daegun Won]] and [[User:Aanavas|Tony Navas]]
 
* [[Semi-supervised Generation of Wikipedia Infoboxes]] - [[User:wpang|Wangshu Pang]] and [[User:Yunwang|Yun Wang]]
 
* [[Semi-supervised Generation of Wikipedia Infoboxes]] - [[User:wpang|Wangshu Pang]] and [[User:Yunwang|Yun Wang]]
* [[Restaurant_Recommendations_Based_On_Review_Content (updated!)]] - [[User:Junyangn|Junyang Ng]], [[User:Ysim| Yan Chuan Sim]], [[User:Cheuktol|Kelvin Law]]
+
* [[Restaurant_Recommendations_Based_On_Review_Content]]  (updated!) - [[User:Junyangn|Junyang Ng]], [[User:Ysim| Yan Chuan Sim]], [[User:Cheuktol|Kelvin Law]]
 
* [[Automatic Segmentation of Receipts]] - [[User:howarth | Dan Howarth]]
 
* [[Automatic Segmentation of Receipts]] - [[User:howarth | Dan Howarth]]
 
* [[Project:Dmovshov_abbreviations | Identifying Abbreviations in Biomedical Text]] - [[User:Dmovshov|Dana Movshovitz-Attias]]
 
* [[Project:Dmovshov_abbreviations | Identifying Abbreviations in Biomedical Text]] - [[User:Dmovshov|Dana Movshovitz-Attias]]

Revision as of 23:32, 29 September 2011

Instructor and Venue

  • Instructors: William Cohen and Noah Smith, Machine Learning Dept and LTI
  • Course secretary: Sharon Cavlovich, sharonw+@cs.cmu.edu, 412-268-5196
  • When/where: Tues-Thursday 3:00-4:20 in Gates-Hillman 4211
  • Course Number: ML 10-710 and LTI 11-763
  • Prerequisites: a machine learning course (e.g., 10-701 or 10-601) or consent of the instructor.
  • TA: Brendan O'Connor
  • Syllabus: Syllabus for Structured Prediction 10-710 in Fall 2011
  • Office hours:
    • Noah, GHC 5723, Thursdays 4:30-5:30 (starting 9/8)
    • Brendan, GHC 8005, Tuesdays 4:30-5:30
    • William, GHC 8217, Fridays 11:00-12:00 (starting 9/16)

Description

This course seeks to cover statistical modeling techniques for discrete, structured data such as text. It brings together content previously covered in Language and Statistics 2 (11-762) and Information Extraction (10-707 and 11-748), and aims to define a canonical set of models and techniques applicable to problems in natural language processing, information extraction, and other application areas. Upon completion, students will have a broad understanding of machine learning techniques for structured outputs, will be able to develop appropriate algorithms for use in new research, and will be able to critically read related literature. The course is organized around methods, with example tasks introduced throughout.

The prerequisite is Machine Learning (10-601 or 10-701), or permission of the instructors.

Syllabus

Older syllabi:

Readings

Unless there's announcement to the contrary, required readings should be done before the class.

Grading

Grades are based on

  • The class project
    • Choose teams and a general project topic. (This can change in the coming weeks/month.) Create a team wiki page, add its members and the project topic. Every team member then should link to it from their own user homepage.
  • Wiki writeup assignments
  • Class participation

Attendees

People taking this class in Fall 2011 include:

Here are sample pages for William, Noah, and Brendan.

Projects




In general, a nice way to find already-made datasets is to read papers in the literature and see what they use and reference. A few data ideas: Project Brainstorming for 10-710 in Fall 2011/Some data ideas