Time: Wednesday and Friday 2pm-3:15pm
Place: Online Live
Instructor: Prof. Heng Ji (Email: hengji@illinois.edu; Office: Online; Office hours: Wednesday and Friday 3:15pm-3:45pm; need to sign up in advance)
TA: Xiaoman Pan (xiaoman6@illinois.edu) (Office hours: Wednesday and Friday 3:15pm-3:45pm; need to sign up in advance)
Date |
Lecture |
Reading |
Assignment |
08/26 |
|
||
08/28 |
|
||
09/02 |
|
||
09/04 |
Combining Symbolic Semantics and Distributional Semantics for Entity Linking |
|
|
09/09 |
|
|
|
09/10 |
|
|
|
09/15 |
|
||
09/17 |
|
|
|
09/25 |
|
|
|
09/30 6pm-8pm Makeup 3403 |
|
||
10/02 |
|
||
10/04 |
|
|
|
10/07 |
|
|
|
10/09 |
|
||
10/14 |
|
|
|
10/16 |
Project Proposal Presentation |
|
|
10/21 |
|
|
|
10/23 |
|
|
|
10/28 |
|
|
|
10/30 |
|
|
|
11/04 |
|
|
|
11/06 |
|
|
|
11/11 |
|
|
|
11/13 |
|
|
|
11/18 |
Paper Peer Review |
|
|
11/20 |
Paper Peer Review |
|
|
12/02 |
Final Project Open-House |
|
|
12/04 |
Final Project Open-House |
|
|
12/09 |
Final Project Open-House |
|
Final Paper Due |
This is an advanced research-centric course to introduce the most up-to-date techniques in Information Extraction and Knowledge Acquisition, which aim to create the next generation of information access in which humans can communicate with computers in any natural language beyond keyword search, and computers can discover accurate, concise, and trustable information and knowledge embedded in big data from heterogeneous sources. We will select ten trending topics such as deep neural networks for Information Extraction, never-ending knowledge acquisition, zero-shot learning for cross-domain transfer. and give a comprehensive overview for each topic. We will review where we have been (the most successful methods in literature), and where we are going (the remaining challenges, and novel methods to tackle these challenges). The target audience of this course is PhD students who do thesis research related to these topics. We also expect to invite several top researchers in this field to give guest lectures. The goal is for each student to have at least one solid paper submission ready at the end of this course. We will select classic papers about each topic and ask students to duplicate the core algorithms and even advance state-of-the-art with new ideas. We also aim to strengthen everyone’s presentation and writing skills, so we will do peer review on the presentations and paper submissions.
Grading
The instructor will write most of the slides and hand-outs, give survey about the best papers from
. The following books may provide some useful background:
ie-2020@lists.cs.illinois.edu