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Results: 90
Number of items: 90
  • Open Access
    Marcheggiani, D., Frolov, A., & Titov, I. (2017). A simple and accurate syntax-agnostic neural model for dependency-based semantic role labeling. In R. Levy, & L. Specia (Eds.), The 21st Conference on Computational Natural Language Learning: Proceedings of the Conference : CoNNL 2017 : August 2-august 4, 2017, Vancouver, Canada (pp. 411-420). Association for Computational Linguistics. https://doi.org/10.18653/v1/k17-1041
  • Open Access
    Le, P., & Titov, I. (2017). Optimizing differentiable relaxations of coreference evaluation metrics. In R. Levy, & L. Specia (Eds.), The 21st Conference on Computational Natural Language Learning: Proceedings of the Conference : CoNNL 2017 : August 2-august 4, 2017, Vancouver, Canada (pp. 390-399). Association for Computational Linguistics. https://doi.org/10.18653/v1/k17-1039
  • Open Access
    Bastings, J., Titov, I., Aziz, W., Marcheggiani, D., & Sima'an, K. (2017). Graph Convolutional Encoders for Syntax-aware Neural Machine Translation. In M. Palmer, R. Hwa, & S. Riedel (Eds.), The Conference on Empirical Methods in Natural Language Processing: proceedings of the conference : EMNLP 2017 : September 9-11, 2017, Copenhagen, Denmark (pp. 1957-1967). Association for Computational Linguistics. https://doi.org/10.18653/v1/D17-1209
  • Open Access
    Marcheggiani, D., & Titov, I. (2017). Encoding sentences with graph convolutional networks for semantic role labeling. In M. Palmer, R. Hwa, & S. Riedel (Eds.), The Conference on Empirical Methods in Natural Language Processing: proceedings of the conference : EMNLP 2017 : September 9-11, 2017, Copenhagen, Denmark (pp. 1506-1515). Association for Computational Linguistics. https://doi.org/10.18653/v1/d17-1159
  • Open Access
    Hoàng, C. (2017). Latent domain models for statistical machine translation. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Šuster, S., Titov, I., & van Noord, G. (2016). Bilingual learning of multi-sense embeddings with discrete autoencoders. In K. Knight, A. Nenkova, & O. Rambow (Eds.), NAACL HLT 2016 : The 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Proceedings of the Conference : June 12-17, 2016, San Diego, California, USA (pp. 1346-1356). The Association for Computational Linguistics. https://doi.org/10.18653/v1/N16-1160
  • Open Access
    van Cranenburgh, A. W. (2016). Rich statistical parsing and literary language. [Thesis, fully internal, Universiteit van Amsterdam].
  • Titov, I., & Khoddam, E. (2015). Unsupervised Induction of Semantic Roles within a Reconstruction-Error Minimization Framework. In R. Mihalcea, J. Chai, & A. Sarkar (Eds.), NAACL HLT 2015: The 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Proceedings of the Conference : May 31-June 5, 2015, Denver, Colorado, USA (pp. 1-10). The Association for Computational Linguistics. http://aclweb.org/anthology/N/N15/N15-1001.pdf
  • Open Access
    Zhai, F., Szymanik, J., & Titov, I. (2015). Toward probabilistic natural logic for syllogistic reasoning. In T. Brochhagen, F. Roelofsen, & N. Theiler (Eds.), Proceedings of the 20th Amsterdam Colloquium (pp. 468-477). Institute for Logic, Language and Computation, University of Amsterdam. https://semanticsarchive.net/Archive/mVkOTk2N/AC2015-proceedings.pdf
  • Frermann, L., Titov, I., & Pinkal, M. (2014). A Hierarchical Bayesian Model for Unsupervised Induction of Script Knowledge. In S. Wintner, S. Goldwater, & S. Riezler (Eds.), EACL 2014: 14th Conference of the European Chapter of the Association for Computational Linguistics: proceedings of the conference: April 26-30, 2014, Gothenburg, Sweden (pp. 49-57). Association for Computational Linguistics. http://www.aclweb.org/anthology/E/E14/E14-1006.pdf
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