Presentations
The presentations are the second most significant part of your grade
in this class (20%), and provide an additional way to gain a deeper
understanding of a specific topic.
Please come and see me as early as possible to pick-up a paper.
Prepare three (3) suggestions for papers you would like to present.
Due
Dates
- Paper selection: January 9th, 2011. Email
me.
- Slides: Two days before your talk. Meet
with instructor.
- Presentation: In Class.
Guidelines
There are three themes which extends the material
we learn in class. It can be a short conference paper or a long
journal paper. Each presentation will be twenty minutes long (20) with
an additional five minutes (5) for questions and comments. You should
decide what part or the paper you want to present. (For example the
details of an algorithm? analysis? specific set of experiments?). You
should use slides for the presentation (your favorite application). At
least three days before your presentation (Thursday, the week before)
you should meet with me to discuss the slides and
presentation.
You can choose to present either a conference
paper or a journal
paper. ACL Anthology
, NIPS and ICML are three of the main
resources where natural language processing is published, with
many of the recent papers online. The first is focused in NLP, where
the later two are in machine learning. JMLR
(http://www.jmlr.org/) is a main
relevant online journal, but there are many more, printed or
electronic, available through the library.
Themes
There are three themes. Some of them overlap with the material
taught in class and provide a way to gain a deeper understanding in
a specific subject; some are about new material
- Sequence Prediction and beyond:Learning models for making multiple, related predictions at once.
- Parsing: Learning to Parse and related topics
- Semi-supervised and Unsupervised Learning: Learning with indirect feedback.
Evaluation
Your presentations will be evaluated according to the following three criteria:
- Content: Is a good range of information included (not too obvious nor too specialized)? Are the main points covered?
- Delivery: Is the presentation clear and concise?
Well practiced?
- Organization: Is the talk well-structured? Easy to follow?
Some example papers:
-
Sequence Prediction and beyond:
-
Parsing:
-
Unsupervised and Semisupervised Learning: