Publications

  1. Tutek, M. & Šnajder, J. (2018). Iterative Recursive Attention Model for Interpretable Sequence Classification. In Proceedings of the 2018 EMNLP Workshop: Analyzing and interpreting neural networks for NLP. [arxiv]

  2. di Buono, M. P., Šnajder, J., Basic, B. D., Glavaš, G., Tutek, M., & Milic-Frayling, N. (2017). Predicting news values from headline text and emotions. In Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism (pp. 1-6). [aclweb]

  3. Rotim, L., Tutek, M., & Šnajder, J. (2017). TakeLab at SemEval-2017 Task 5: Linear aggregation of word embeddings for fine-grained sentiment analysis of financial news. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017) (pp. 866-871). [aclweb]

  4. di Buono, M. P., Tutek, M., Šnajder, J., Glavaš, G., Bašić, B. D., & Milic-Frayling, N. (2017). Two Layers of Annotation for Representing Event Mentions in News Stories. In Proceedings of the 11th Linguistic Annotation Workshop (pp. 82-90). [aclweb]

  5. Tutek, M., Glavas, G., Šnajder, J., Milić-Frayling, N., & Dalbelo Basic, B. (2016, October). Detecting and Ranking Conceptual Links between Texts Using a Knowledge Base. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management (pp. 2077-2080). ACM.

  6. Tutek, M., Sekulic, I., Gombar, P., Paljak, I., Culinovic, F., Boltuzic, F., … & Šnajder, J. (2016). Takelab at semeval-2016 task 6: stance classification in tweets using a genetic algorithm based ensemble. In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016) (pp. 464-468). [aclweb]