The CLaC Discourse Parser at CoNLL-2015
TL
TL;DR
This paper describes the submission of the UIMA framework to the CoNLL-2015 shared task on shallow discourse parsing, and how it was used to develop the parser and to add machine learning functionality to the U IMA framework.
Abstract
This paper describes our submission (kosseim15) to the CoNLL-2015 shared task on shallow discourse parsing. We used the UIMA framework to develop our parser and used ClearTK to add machine learning functionality to the UIMA framework. Overall, our parser achieves a result of 17.3 F1 on the identification of discourse relations on the blind CoNLL-2015 test set, ranking in sixth place.
References9 items
1
A Constituent-Based Approach to Argument Labeling with Joint Inference in Discourse Parsing
2
ClearTK 2.0: Design Patterns for Machine Learning in UIMA
3
A PDTB-styled end-to-end discourse parser
4
The WEKA data mining software: an update
5