The CLaC Discourse Parser at CoNLL-2015

08/19/2017
by   Majid Laali, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
08/19/2017

The CLaC Discourse Parser at CoNLL-2016

This paper describes our submission "CLaC" to the CoNLL-2016 shared task...
research
11/03/2010

A PDTB-Styled End-to-End Discourse Parser

We have developed a full discourse parser in the Penn Discourse Treebank...
research
05/14/2019

A Unified Linear-Time Framework for Sentence-Level Discourse Parsing

We propose an efficient neural framework for sentence-level discourse an...
research
11/06/2020

Unleashing the Power of Neural Discourse Parsers – A Context and Structure Aware Approach Using Large Scale Pretraining

RST-based discourse parsing is an important NLP task with numerous downs...
research
09/02/2020

A Simple Global Neural Discourse Parser

Discourse parsing is largely dominated by greedy parsers with manually-d...
research
12/03/2016

Using Discourse Signals for Robust Instructor Intervention Prediction

We tackle the prediction of instructor intervention in student posts fro...
research
01/20/2023

Blind Spots: Automatically detecting ignored program inputs

A blind spot is any input to a program that can be arbitrarily mutated w...

Please sign up or login with your details

Forgot password? Click here to reset