Project presentations will be in class on Wednesday, April 28.
2CR projects will have 5 minute presentations, and 3CR projects will
have 10 minute presentations. Team projects have additive time (I
added up the time that would be allocated for each individual). Due to
the large number of presentations, we will strictly adhere to this
schedule ... you will be cut-off when your time is up! So please plan
your talk to ensure that you can concisely describe your project and
present your results within the given time period.
IMPORTANT: Please email your slides to Prof. Riloff by
11:00pm on Tuesday, April 27 so that we will not need to switch
laptops between presentations. Your slides should be in either .pdf or
.ppt format ... do not send .pptx slides!
- 1:25-1:30 Adam Teichert : A Bayesian Model of Document Relevance
- 1:30-1:35 Shreyas Ramalingam : Wikipedia-based Document Clustering
- 1:35-1:40 Youngjun Kim : Experiments with Extractive Summarization
- 1:40-1:45 Anusua Trivedi : Exploiting Tag Information to
Improve Web Page Clustering
- 1:45-1:50 Yu Su : Blocked Sort-based Indexing
- 1:50-2:00 Iljoo Kim : Predicting the Demographics of Web Page Visitors
- 2:00-2:10 Chong Oh : Clustering of Twitter Postings
- 2:10-2:20 Jihoon Yoo : Information Retrieval over Social
- 2:20-2:30 Shuying Shen : Identifying Acute Respiratory
Infection Cases in Clinical Text
- 2:30-2:45 Hongchang Peng & Zhan Wang : Research for the Use of
Semantic Information in Multi-label Text Classification
- 2:45-3:05 Hoa Nguyen & Huong Nguyen : Structural Querying Wikipedia Using Infoboxes
Each team must submit two things by 11:59pm, Monday, May 3:
Please submit all of these items via electronic handin on the CADE lab
machines using the following command:
- The source code for your project. Please turn in all of
the source code files that you wrote for the project. You do not need
to turn in the data set(s) that you used, or any external code that you
used (e.g., a part-of-speech tagger). You only need to submit the
source code files that you wrote.
- A short (~2-3 page) project report that contains the following:
- System Description: A ~1 page description of the system that you created.
Clearly explain the task that your system performs, how the system works
(i.e., what processing steps are done), and the algorithms that you
implemented. If you used any external tools, be sure to say what they
- Data Set: Briefly describe the text collection(s) that you used.
- Evaluation: Briefly describe how you evaluated your system (i.e., Where did
your gold standard answers come from? What evaluation metrics did you
use? What results did you get?)
- Contributions: If your project was a 2-person team, explain what the
contribution of each team member was (i.e., who worked on what).
- Lessons Learned: what did you learn as a
result of this project? Were you happy with the results? If you were
starting this project over again, what would you do the same and what
would you do differently? Feel free to
discuss any general lessons that you learned from this project (positive or negative) and
feelings that you've come away with about IR and text analysis.
handin cs7961 project YourFileNameHere
I hope you all found the projects to be interesting and enlightening!