PhD studentUniversity of Trento
Department of Information Engineering and Computer Science
Italy, TN 38123
Such complex prediction tasks like coreference resolution in NLP are tackled with learning algorithms making inference in structured output spaces. These algorithms produce a complex output object in question at once, globally. We aim at elaborating methods for consolidation of structured learning with the task specific knowledge.
Haponchyk, I., Moschitti, A. (2017) Don't understand a measure? Learn it: Structured Prediction for Coreference Resolution optimizing its measures. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1018-1028, Vancouver, Canada, 2017.
Haponchyk, I., Moschitti, A. (2017) A Practical Perspective on Latent Structured Prediction for Coreference Resolution. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2017), pp. 143-149, Valencia, Spain, 2017. poster
Haponchyk, I., Moschitti, A. (2014) Making Latent SVMstruct Practical for Coreference Resolution. In Proceedings of the First Italian Conference on Computational Linguistics (CLiC-it 2014), pp. 203-207, Pisa, Italy, 2014. poster
News and Activities
[April, 2017] Our paper entitled
Don’t understand a measure? Learn it: Structured Prediction for Coreference Resolution optimizing its measures.
was accepted to appear in ACL 2017.
[September, 2016] Participation in the NetCla: The ECML-PKDD Network Classification Challenge to predict applications by the network traffic. The proposed model won the 1st place (among 25 competing teams).