The CLaC Discourse Parser at CoNLL-2016
Abstract
This paper describes our submission "CLaC" to the CoNLL-2016 shared task on shallow discourse parsing. We used two complementary approaches for the task. A standard machine learning approach for the parsing of explicit relations, and a deep learning approach for non-explicit relations. Overall, our parser achieves an F1-score of 0.2106 on the identification of discourse relations (0.3110 for explicit relations and 0.1219 for non-explicit relations) on the blind CoNLL-2016 test set.
- Publication:
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arXiv e-prints
- Pub Date:
- August 2017
- DOI:
- 10.48550/arXiv.1708.05798
- arXiv:
- arXiv:1708.05798
- Bibcode:
- 2017arXiv170805798L
- Keywords:
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- Computer Science - Computation and Language
- E-Print:
- In Proceedings of the Twentieth Conference on Computational Natural Language Learning: Shared Task. pp 92-99. July 7-12, 2016. Berlin, Germany