The CLaC Discourse Parser at CoNLL-2016
This work addresses discourse parsing for natural language processing researchers, but it is incremental as it builds on existing shared task frameworks.
The paper tackled shallow discourse parsing by combining standard machine learning for explicit relations and deep learning for non-explicit relations, achieving an F1-score of 0.2106 on discourse relation identification in the CoNLL-2016 test set.
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.