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UNITN scores high at CoNLL 2016 Shared Task

UNITN scores high at CoNLL 2016 Shared Task


SENSEI-UNITN team ranked 3rd at the CoNLL 2016 Shared Task on Shallow Discourse Parsing in the end-to-end parsing and 1st in the argument extraction sub-task.
The SENSEI team participated to the CoNLL 2016 Shared Task on Shallow Discourse Parsing on end-to-end parsing of English news. Discourse parsing has utility for many other NLP tasks such as summarization and opinion mining. Penn Discourse Treebank style discourse parsing is a composite task of detecting explicit and non-explicit discourse relations, their connective and argument spans, and assigning a sense to these relations. Due to the composite nature of the task, end-to-end discourse parsing is very challenging.

The system developed by SENSEI emphasized argument extraction, as it is one of the most difficult subtasks (and ranked 1st). The approach to discourse parsing consists of token-level sequence labeling with Conditional Random Fields for connective and argument span extraction tasks, and supervised classification for sense assignment tasks. 
The SENSEI system ranked third on the end-to-end parsing task out of 14 participants. The detailed description of the system will be available in the CoNLL 2016 proceedings.



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The research leading to these results has received funding from the European Union - Seventh Framework Programme (FP7/2007-2013) under grant agreement n° 610916 – SENSEI.