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Results: 44
Number of items: 44
  • Open Access
    Dehghani, M., Zamani, H., Severyn, A., Kamps, J., & Croft, W. B. (2017). Neural Ranking Models with Weak Supervision. In SIGIR'17 : proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval: August 7-11, 2017, Shinjuku, Tokyo, Japan (pp. 65-74). Association for Computing Machinery. https://doi.org/10.1145/3077136.3080832
  • Open Access
    Dehghani, M., Severyn, A., Rothe, S., & Kamps, J. (2017). Learning to Learn from Weak Supervision by Full Supervision. Paper presented at Workshop on Meta-Learning (MetaLearn 2017), Long Beach, California, United States. https://meta-learn.github.io/2017/papers/metalearn17_dehghani.pdf
  • Open Access
    Dehghani, M., Zamani, H., Severyn, A., Kamps, J., & Croft, W. B. (2017). Neural Ranking Models with Weak Supervision. In B. Verheij, & M. Wiering (Eds.), BNAIC 2017: Benelux Conference on Artificial Intelligence: Preproceedings of the 29th Benelux Conference on Artificial Intelligence, November 8–9, 2017 in Groningen, The Netherlands (pp. 372-373). (BNAIC; Vol. 29). University of Groningen. http://bnaic2017.ai.rug.nl/preproceedings.pdf
  • Open Access
    Azarbonyad, H., Dehghani, M., Beelen, K., Arkut, A., Marx, M., & Kamps, J. (2017). Words are Malleable: Computing Semantic Shifts in Political and Media Discourse. Abstract from 16th Dutch-Belgian Information Retrieval Workshop, Hilversum, Netherlands. https://marijnkoolen.github.io/dir2017/accepted-submissions/DIR2017_paper_10.pdf
  • Open Access
    Kenter, T., Borisov, A., Van Gysel, C., Dehghani, M., de Rijke, M., & Mitra, B. (2017). Neural Networks for Information Retrieval. In SIGIR'17 : proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval: August 7-11, 2017, Shinjuku, Tokyo, Japan (pp. 1403-1406). Association for Computing Machinery. https://doi.org/10.1145/3077136.3082062
  • Open Access
    Azarbonyad, H., Dehghani, M., Kenter, T., Marx, M., Kamps, J., & de Rijke, M. (2017). Hierarchical Re-estimation of Topic Models for Measuring Topical Diversity. In J. M. Jose, C. Hauff, I. S. Altıngovde, D. Song, D. Albakour, S. Watt, & J. Tait (Eds.), Advances in Information Retrieval: 39th European Conference on IR Research, ECIR 2017, Aberdeen, UK, April 8–13, 2017 : proceedings (pp. 68-81). (Lecture Notes in Computer Science; Vol. 10193). Springer. https://doi.org/10.1007/978-3-319-56608-5_6
  • Open Access
    Azarbonyad, H., Dehghani, M., Beelen, K., Arkut, A., Marx, M., & Kamps, J. (2017). Words are Malleable: Computing Semantic Shifts in Political and Media Discourse. In CIKM'17 : proceedings of the 2017 ACM on Conference on Information and Knowledge Management: November 6-10, 2017, Singapore, Singapore (pp. 1509-1518). Association for Computing Machinery. https://doi.org/10.1145/3132847.3132878
  • Open Access
    Dehghani, M. (2016). Significant Words Representations of Entities. In SIGIR'16: the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval: Pisa, Italy , July 17-21, 2016 (pp. 1183). Association for Computing Machinery. https://doi.org/10.1145/2911451.2911474
  • Open Access
    Dehghani, M., Azarbonyad, H., Kamps, J., & Marx, M. (2016). Generalized Group Profiling for Content Customization. In CHIIR'16: proceedings of the 2016 ACM Conference on Human Information Interaction and Retrieval : March 13-17, 2016, Carrboro, NC, USA (pp. 245-248). The Association for Computing Machinery. https://doi.org/10.1145/2854946.2855003
  • Open Access
    Dehghani, M., Azarbonyad, H., Kamps, J., & Marx, M. (2016). Two-Way Parsimonious Classification Models for Evolving Hierarchies. In N. Fuhr, P. Quaresma, T. Gonçalves, B. Larsen, K. Balog, C. Macdonald, L. Cappellato, & N. Ferro (Eds.), Experimental IR Meets Multilinguality, Multimodality, and Interaction: 7th International Conference of the CLEF Association, CLEF 2016, Évora, Portugal, September 5-8, 2016: proceedings (pp. 69-82). (Lecture Notes in Computer Science ; Vol. 9822). Springer. https://doi.org/10.1007/978-3-319-44564-9_6
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