The CLaC Discourse Parser at CoNLL-2016
Year: 2017
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Laali, Majid, Cianflone, Andre, Kosseim, Leila
Institution: Concordia University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4299452230