Natural Language Processing, IASD
This course is an introduction to Natural Language Processing (with mainly deep-learning approach). The lab sessions use the pytorch (python module).
News
- The course of Guillaume Pitel: slides are on the drive !
- Deadline for the project: May the 15th
- For project and reading, team and selection: deadline Thursday the 6th of February
- The project and the reading lists are available, see the drive !
- The course starts the 16/01
The resources / drive
Look at this drive for the slides and the material of lab sessions.
Expected schedules
It starts in january 2020 (the 16th). The course are scheduled on Thursay, starting at 8:30 in the morning.
16-jan, course: NLP, overview and the main tasks
- For the linguistic part you can refer to https://faculty.washington.edu/ebender/100things-sem_prag.html
- For the NLP basics: https://web.stanford.edu/~jurafsky/slp3/
23-jan, course: Text classification
30-jan, lab session
Two notebooks for two parts (see the drive)
- pytorch 101
- text classification
Further work: text classification with convolution
6-feb, course: sequence models
- ngram language model
- recurrent and LSTM network
Then for the first part
- 13-feb: Postponed
- 27-feb, course: readings
The second part:
- 03-mar, course: syntax, by B. Crabbé, at 13:30
- 05-mar, 12-mar: Large scale NLP : the business perspective, G. Pitel
Evaluation
The evaluation is in two parts. For both, first make your team (typically 3 students).
Reading
The goal is to read an article an to make a presentation (the 27-feb). A list will be availble soon, but you can also propose one (I must agree beforehand). Select one article per team to read and analyse the paper to make a clear and synthetic presentation. Some questions you may use to guide your reading are (among others):
- Did you like the paper? Did you find it interesting? Be honest!
- What are the most important things you learned from the paper? Why are they important?
- Do the lessons learned generalize beyond the specific task? Do they contribute towards building an important system or application?
- Is the experimental setup satisfying? Any experiments missing? Any obvious or important baseline missing?
- Is the problem/approach well motivated?
- Are you convinced by the results? Why?
- Is the writing clear? Is the paper well structured?
The important dates are :
- Make up your team and select the paper before the 6-feb
- Presentation: the 23-feb (10 minutes per team)
Project
A list will be availble soon, but you can also propose one (I must agree beforehand).
- Team and the project registration : before 6-feb
- Deliverable for 13-feb: 2 pages (pdf only) to describe the data, the task and your plan
- Deliverable for 27-feb: a github/gitlab repository
- Final deliverable: a report in pdf and the code via the git repos
- Final deadline: 15th of May