Machine Learning and NLP group at Trento.

Iryna Haponchyk

PhD student
University of Trento
Department of Information Engineering and Computer Science
Italy, TN 38123
Email: iryna.haponchyk@unitn.it

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. (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

  • 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

News and Activities

[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).

[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.