The Information and Language Processing Systems group is part of the Informatics Institute of the University of Amsterdam. Our research is aimed at intelligent information access, especially in the face of massive amounts of information. We work on finding and analyzing content (information retrieval, machine translation, language technology), the analysis of structural information (social networks, linked data) and the analysis of user behavior (self-learning search, log analysis, user studies).

We combine fundamental, experimental and applied research, and we do so using a broad range of textual data, data from the web or enterprises, edited or user generated, or obtained from (automatic) transcriptions of audio or video. We are involved in a large number of projects with other groups, both within and outside academia. Our research is funded by NWO, KNAW, the EU and through a range of public-private partnerships.

Recent News

Christophe Van Gysel releases pytrec_eval

All your favorite IR measures from trec_eval are now available in Python! Today, ILPS-er Christophe Van Gysel releases pytrec_eval, a Python interface to TREC’s evaluation tool, trec_eval. It is an attempt to stop the cultivation of custom implementations of Information Retrieval evaluation measures for the Python programming language. See https://github.com/cvangysel/pytrec_eval

Papers accepted at CIKM 2017

The following papers by ILPS-ers were accepted at CIKM 2017:

  • Active Sampling for Large-scale Information Retrieval Evaluation by Dan Li and Evangelos Kanoulas
  • Balancing Speed and Quality in Online Learning to Rank for Information Retrieval by Harrie Oosterhuis and Maarten de Rijke
  • Classy: A visual analytics environment for supervised text classification and model evaluation by Ilias Koutsakis, Evangelos Kanoulas, George Tsatsaronis and Eamonn Maguire
  • Learning to Attend, Copy, and Generate for Session Based Query Suggestion by Mostafa Dehghani, Sascha Rothe, Enrique Alfonseca and Pascal Fleury
  • Online Expectation-Maximization for Click Models by Ilya Markov, Alexey Borisov, Maarten de Rijke
  • Reply With: Proactive Recommendation of Email Attachments by Christophe Van Gysel, Bhaskar Mitra, Matteo Venanzi, Roy Rosemarin, Grzegorz Kukla, Piotr Grudzien and Nicola Cancedda
  • Sensitive and Scalable Online Evaluation with Theoretical Guarantees by Harrie Oosterhuis and Maarten de Rijke
  • Words are Malleable: Computing Semantic Shifts in Political and Media Discourse by Hosein Azarbonyad, Mostafa Dehghani, Kaspar Beelen, Alexandra Arkut, Maarten Marx, and Jaap Kamps

Four papers accepted at Neu-IR 2017

logo-uvaFour papers were accepted at Neu-IR 2017: The SIGIR 2017 Workshop on Neural Information Retrieval. They are “Share your model instead of your data: Privacy preserving mimic learning for ranking” by Mostafa Dehghani, Hosein Azarbonyad, Jaap Kamps, and Maarten de Rijke; “Modeling label ambiguity for listwise neural learning to rank” by Rolf Jagerman, Julia Kiseleva, and Maarten de Rijke; “Thread reconstruction in conversational data using neural coherence models” by Dat Tien Nguyen, Shafiq Joty, Basma El Amel Boussaha, and Maarten de Rijke; and “Semantic entity retrieval toolkit” by Christophe Van Gysel, Maarten de Rijke, and Evangelos Kanoulas.