Text Retrieval & Text Mining Journal Club
Thursdays, 4:00 pm to 5:30 pm
Venue: B11, MLH
Previous Years Reading Groups
Some interesting conferences you may explore for papers to present:
- ACL/HLT (Association of Computational Linguistics/Human Language Technologies
- AMIA (American Medical Informatics Association) and JAMIA
- ACM SIGIR (Special Interest Group in Information Retrieval)
- AIRS - Asian Information Retrieval
- ICML (International Conference on Machine Learning)
- ICWSM (AAAI Conference on Web and Social Media)
- ACM KDD (Knowledge Discovery and Data Mining)
- WWW (World Wide Web Conference)
- WSDM (ACM International Conference on Web Search and Data Mining)
- TREC Proceedings
Goal: To study current papers from journals and conference proceedings in text
retrieval and text mining. Examples of problems include topic models,
web retrieval and web mining, ranking strategies, ambiguity resolution, knowledge discovery,
web phenomenon including social networks, information extraction and text
The reading group is led by Professor Padmini Srinivasan. Interested students (from beginning to advanced students) and faculty are invited to
participate in the reading group. Participation format is informal with individuals
taking turns to present an overview of the selected paper and lead the
This forum has resulted in collaborative projects and
February 1: Shehroze Farooqi
- January 25: Padmini Srinivasan
February 8: Jonathan Rusert
- Yao, Y., Viswanath, B., Cryan, J., Zheng, H., & Zhao, B. Y. (2017, October). Automated crowdturfing attacks and defenses in online review systems. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security (pp. 1143-1158). ACM.
February 15: Huyen Le
February 22: Momina Tabish
- An, J., & Weber, I. (2016). # greysanatomy vs.# yankees: Demographics and Hashtag Use on Twitter. arXiv preprint arXiv:1603.01973.
- Bergsma, S., Dredze, M., Van Durme, B., Wilson, T., & Yarowsky, D. (2013). Broadly improving user classification via communication-based name and location clustering on twitter.
In Proceedings of the 2013 Conference of the North American Chapter of
the Association for Computational Linguistics: Human Language
Technologies (pp. 1010-1019).
March 1: Dat Hong
March 8: Umar Iqbal
- Luo, Y., Cheng, Y., Uzuner, Ö., Szolovits, P., & Starren, J. (2017). Segment convolutional neural networks (Seg-CNNs) for classifying relations in clinical notes. Journal of the American Medical Informatics Association, 25(1), 93-98.
March 15: SPRING BREAK
March 22: Osama Khalid
Mach 29: Momina Tabish
April 5: Huyen Le
April 12: Shehroze Farooqi
- Li, Z., Zou, D., Xu, S., Ou, X., Jin, H., Wang, S., ... & Zhong, Y. (2018). VulDeePecker: A Deep Learning-Based System for Vulnerability Detection. arXiv preprint arXiv:1801.01681.
April 19: Jonathan Rusert
April 26: John Cook
- Nilizadeh, S., Labrèche, F., Sedighian, A., Zand, A., Fernandez, J., Kruegel, C., ... & Vigna, G. (2017, October). Poised: Spotting twitter spam off the beaten paths. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security (pp. 1159-1174). ACM.
May 3: Huyen Le
- Arras, L., Horn, F., Montavon, G., Müller, K. R., & Samek, W. (2017). " What is relevant in a text document?": An interpretable machine learning approach. PloS one, 12(8), e0181142.
- Rizoiu, M. A., Graham, T., Zhang, R., Zhang, Y., Ackland, R., & Xie, L. (2018). # debatenight: The role and influence of socialbots on twitter during the 1st us presidential debate. arXiv preprint arXiv:1802.09808.