Structured Prediction 10-710 in Fall 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

• Syllabus for Structured Prediction 10-710 in Fall 2011

Older syllabi:

• Course page for Language and Stats 2, one of the "parent" courses of Structured Prediction:
• Older syllabi for Information Extraction, another of the "parent" courses of Structured Prediction:
  • Fall 2010, Fall 2009, and for historical interest, 10-707 Spring 2007, 10-707 Spring 2004.

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:

• Antonio Juárez López
• Dana Movshovitz-Attias
• Yanchuan Sim (yc)
• William Yang Wang
• Anirudh Koul
• Avneesh Saluja
• Junyang Ng
• Wang Ling
• Matt Gardner
• Tony Navas
• Yun Wang (Maigo)
• Andrew Rodriguez
• Cheuk To Law (Kelvin)
• Manaj Srivastava
• Avinava Dubey
• Francis Keith
• Dhananjay Kulkarni
• Elijah Mayfield
• Tarun Sharma
• Mridul Gupta
• Xiaoqi Yin(Philip)
• Daegun Won
• Rui Correia
• Wangshu Pang(Wash)
• Dan Howarth
• Derry Wijaya
• Anuj Goyal
• Jeff Flanigan
• Tarun Kumar
• Dani Yogatama

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

Possible Projects

If you have an idea for a possible project, list it here, as William has done in the example. This is for coordination and brainstorming at this point. You probably want to include your name and the names of your team-mates in the project description.

• Mapping entity names in a document to places on a map.
• Wikipedia Infobox Generator Using Cross Lingual Unstructured Text - Anirudh Koul, Daegun Won and Tony Navas
• Including a knowledge base into Haghighi & Klein's coreference resolution system - Matt Gardner, Avinava Dubey
• Stylistic Structure Extraction from Early United States Slave-related Legal Opinions William Y. Wang and Elijah Mayfield
• Automatically generating headings for sections (group of contiguous paragraph) in unstructured text
• Semi-supervised Generation of Wikipedia Infoboxes - Wangshu Pang and Yun Wang
• Word Alignments using an HMM-based model - Wang Ling and Rui Correia
• Improving SMT word alignment with binary feedback - Avneesh Saluja
• Building domain specific NERs by using information from domain-general annotations - Junyang Ng, Yan Chuan Sim, Kelvin Law
• Relevant Information Extraction from Court-room Hearings To Predict Judgement - Manaj Srivastava, Mridul Gupta
• Identifying Abbreviations in Biomedical Text - Dana Movshovitz-Attias
• Finding out who you are from where, when, what and with whom you tweet - Derry Wijaya,Tarun Kumar, Dhananjay Kulkarni, Tarun Sharma
• Automated Template Extraction - Francis Keith
• Linearizing Dependency Trees - Jeff Flanigan

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