1. All-paths graph kernel for protein-protein interaction extraction with evaluation of cross-corpus learning;Airola;BMC Bioinformatics,2008
2. Bahdanau, D., Cho, K., & Bengio, Y. (2015). Neural machine translation by jointly learning to align and translate. Proceedings of the ICLR 2015 – International Conference on Learning Representations. Retrieved from http://arxiv.org/abs/1409.0473.
3. Bastings, J., Titov, I., Aziz, W., Marcheggiani, D. & Simaan, K. (2017). Graph Convolutional Encoders for Syntax-aware Neural Machine Translation. Proceedings of the EMNLP 2017 – Conference on Empirical Methods in Natural Language Processing 1957–1967. https://doi.org/10.18653/v1/d17-1209.
4. Björne, J., Kaewphan, S. & Salakoski, T. (2013). UTurku: Drug named entity recognition and drug-drug interaction extraction using SVM classification and domain knowledge. Proceedings of the 2nd Joint Conference on Lexical and Computational Semantics (∗SEM), Volume 2: 7th International Workshop on Semantic Evaluation (SemEval 2013) (pp. 651–659).
5. Chowdhury, M. F. M. & Lavelli, A. (2013a). Exploiting the scope of negations and heterogeneous features for relation extraction: A case study for drug-drug interaction extraction. Proceedings of the NAACL HLT 2013 – 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 765–771).