TTDS: Schedule

• IR in Practice Chapters 1 & 2

• Intro to IR Chapter 2 --> 2.2.4 
• IR in Practice Chapter 4 
• Zipf’s law, Vsouce 
• Benford’s law, Numberphile

• Intro to IR Chapters 1, 2.4 and 3.1-3.4 
• IR in Practice chapter 5

• Intro to IR Chapters 6.2 to 6.4 and 12
• IR in Practice Chapter 7
• Robertson, Stephen E., et al. "Okapi at TREC-3."
• J. Ponte and W. B. Croft. "A language modeling approach to information retrieval."

• Intro to IR Chapters 8
• IR in Practice Chapter 8
• C Buckley and E. M. Voorhees. "Retrieval Evaluation with Incomplete Information."

• Intro to IR Chapters 9
• IR in Practice Chapter 6.2, 6.3
• Magdy W. and G. J. F. Jones. A Study on Query Expansion Methods for Patent Retrieval

• Intro to IR Chapters 19, 21.1
• IR in Practice Chapter 3, 4.5, 10.3
• Page, L., Brin, S., Motwani, R., & Winograd, T. (1999). The PageRank citation ranking: Bringing order to the web.

• Intro to IR Chapters 13.5
• “Probabilistic Topic Models” by David Blei
• “Latent Dirichlet Allocation” by David Blei, Andrew Y. Ng, and Michael I. Jordan
• “Probabilistic Topic Models” by Mark Steyvers and Tom
  Griffiths
• To watch: Guest lecture (2017) by David Blei at University of Edinburgh School of Informatics

• "Machine Learning in Automated Text Categorization" by Fabrizio Sebastiani
• "A Primer on Neural Network Models for Natural
  Language Processing" by Yoav Goldberg
• "Bridging Social Media via Distant Supervision" by Walid Magdy et al.
• Text Classification pages on Huggingface.co

18 - Text Classification 2

Jupyter notebook

• Nallapati, Ramesh. Discriminative models for information retrieval. SIGIR 2004.
• Burges, C. J. (2010). From ranknet to lambdarank to lambdamart: An overview. Learning, 11(23-581), 81.
• SVMRank: http://svmlight.joachims.org/
• L2R test sets:
  • Microsoft’s LETOR project
    http://research.microsoft.com/en-us/um/beijing/projects/letor//default.aspx