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
This edition of SEA is in partnership with FNWI. As usual we will have two talks: one academic and one industry talk, followed by drinks. This Friday, we will have a talk by Harrie Oosterhuis from ILPS about Online Evaluation and another by Or Levi from Marktplaats.nl/Ebay about Sponsored Search. Continue reading →
“What does attention in neural machine translation pay attention to?” by ILPS-ers Hamidreza Ghader and Christof Monz was accepted at IJCNLP 2017, the 8th International Joint Conference on Natural Language Processing.
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
“Do News Consumers Want Explanations for Personalized News Rankings?” by Maartje Ter Hoeve, Mathieu Heruer, Daan Odijk, Anne Schuth, Martijn Spitters, Ron Mulder, Nick van der Wildt, and Maarten de Rijke was accepted at the FATREC Workshop on Responsible Recommendation at RecSys 2017.
Four 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.