Veranstaltungen im Sommersemester 2018 / Master-Seminar: Data Analytics

Vorlesungsfolien

	Course Introduction	[PDF]
	Reading List and Group Breakdown	[PDF]
	Presentation Order	[PDF]

How tos:

	How to read a paper	[PDF]
	Presentation and Summary Paper	[PDF]
	Summary Paper Template	[PDF]

Group Presentations

Fundamentals: Presented by Daniel Obando Montero, Shabanaz Chamurally, and Daniela Thyssens.

• Tsuruoka, Y., Tsujii, J. I., & Ananiadou, S. (2009, August). Stochastic gradient descent training for l1-regularized log-linear models with cumulative penalty. In Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1-Volume 1 (pp. 477-485). Association for Computational Linguistics.
• Bengio, Y., Louradour, J., Collobert, R., & Weston, J. (2009, June). Curriculum learning. In Proceedings of the 26th annual international conference on machine learning (pp. 41-48). ACM.
• Sculley, D. (2010, July). Combined regression and ranking. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 979-988). ACM.

Presentation Slides

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Text Categorization: Presented by Arooba Khokhar and Baha Tanveer.

• Leopold, E., & Kindermann, J. (2002). Text categorization with support vector machines. How to represent texts in input space?. Machine Learning, 46(1-3), 423-444.
• Johnson, R., & Zhang, T. (2014). Effective use of word order for text categorization with convolutional neural networks. arXiv preprint arXiv:1412.1058.

Presentation Slides

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Text Categorization: Presented by Nurbakyt Kulbatshayeva, Olasubomi Saheed KASALI and Jia-Jen .

• Tan, S., & Cheng, X. (2007, September). An effective approach to enhance centroid classifier for text categorization. In European Conference on Principles of Data Mining and Knowledge Discovery (pp. 581-588). Springer, Berlin, Heidelberg.
• Dumais, S., Platt, J., Heckerman, D., & Sahami, M. (1998, November). Inductive learning algorithms and representations for text categorization. In Proceedings of the seventh international conference on Information and knowledge management (pp. 148-155). ACM.
• Zhang, X., Zhao, J., & LeCun, Y. (2015). Character-level convolutional networks for text classification. In Advances in neural information processing systems (pp. 649-657).

Presentation Slides

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Text Categorization: Presented by Mohit Bansal, Kalaiselvan Panneerselvam and John Robert.

• Pang, B., Lee, L., & Vaithyanathan, S. (2002, July). Thumbs up?: sentiment classification using machine learning techniques. In Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10 (pp. 79-86). Association for Computational Linguistics.
• Pak, A., & Paroubek, P. (2010, May). Twitter as a corpus for sentiment analysis and opinion mining. In LREc (Vol. 10, No. 2010).
• dos Santos, C., & Gatti, M. (2014). Deep convolutional neural networks for sentiment analysis of short texts. In Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers (pp. 69-78).

Presentation Slides

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Text Categorization: Presented by Sathish kumar Chandrasekaran and Sharmila Ragunathan

• Wilson, T., Wiebe, J., & Hoffmann, P. (2005). Recognizing contextual polarity in phrase-level sentiment analysis. In Proceedings of the conference on human language technology and empirical methods in natural language processing . Association for Computational Linguistics.
• Jin, W., Ho, H. H., & Srihari, R. K. (2009). OpinionMiner: a novel machine learning system for web opinion mining and extraction. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining . ACM.

Presentation Slides

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Applications: Presented by Feven Tsegage Aga, Mohammed Ikbal Nacer and Godwin Namwamba

• Tang, J., Qu, M., & Mei, Q. (2015). Pte: Predictive text embedding through large-scale heterogeneous text networks. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ACM.
• Prabhu, Y., & Varma, M. (2014). Fastxml: A fast, accurate and stable tree-classifier for extreme multi-label learning. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM.
• Yu, H. F., Jain, P., Kar, P., & Dhillon, I. (2014). Large-scale multi-label learning with missing labels. In International conference on machine learning

Presentation Slides

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Applications: Presented by Johannes Burchet, Torben Windler and Tilman Elze

• Pennacchiotti, M., & Popescu, A. M. (2011). A Machine Learning Approach to Twitter User Classification. Icwsm, 11(1).
• 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.
• Abel, F., Gao, Q., Houben, G. J., &Tao, K. (2013). Twitter-Based User Modeling for News Recommendations. In IJCAI .

Presentation Slides

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Applications: Presented by Haider Shabbir ,Famakin Olawole Taiwo and Rameez Ahmed Khan

• Mohan, A., Chen, Z., & Weinberger, K. (2011). Web-search ranking with initialized gradient boosted regression trees. In Proceedings of the Learning to Rank Challenge .
• Kannan, A., Baker, S., Ramnath, K., Fiss, J., Lin, D., Vanderwende, L., ... & Wang, X. J. (2014). Mining text snippets for images on the web. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM.
• Kannan, A., Kurach, K., Ravi, S., Kaufmann, T., Tomkins, A., Miklos, B., ... & Ramavajjala, V. (2016). Smart reply: Automated response suggestion for email. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ACM.

Presentation Slides

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