2015-2016 CoNLL Shared Task

Item Name: 2015-2016 CoNLL Shared Task
Author(s): Nianwen Xue, Hwee Tou Ng, Sameer Pradhan, Attapol T. Rutherford, Bonnie Webber, Chuan Wang, Hong Min Wang, Rashmi Prasad
LDC Catalog No.: LDC2017T13
ISBN: 1-58563-812-9
ISLRN: 228-559-981-287-1
DOI: https://doi.org/10.35111/x63k-xv09
Release Date: September 14, 2017
Member Year(s): 2017
DCMI Type(s): Text
Data Source(s): newswire
Project(s): CoNLL
Application(s): discourse parsing
Language(s): English, Chinese, Mandarin Chinese
Language ID(s): eng, zho, cmn
License(s): LDC User Agreement for Non-Members
Online Documentation: LDC2017T13 Documents
Licensing Instructions: Subscription & Standard Members, and Non-Members
Citation: Xue, Nianwen, et al. 2015-2016 CoNLL Shared Task LDC2017T13. Web Download. Philadelphia: Linguistic Data Consortium, 2017.
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2015-2016 CoNLL Shared Task, LDC Catalog Number LDC2017T13 and ISBN 1-58563-812-9, contains the Chinese and English training, development and test data for the 2015 and 2016 CoNLL (Conference on Computational Natural Language Learning) Shared Task Evaluation which focused on shallow discourse parsing.

The Conference on Computational Natural Language Learning (CoNLL) is accompanied every year by a shared task intended to promote natural language processing applications and evaluate them in a standard setting. Shallow discourse parsing is the task of parsing a piece of text into a set of discourse relations between two adjacent or non-adjacent discourse units. This task is called shallow discourse parsing because the relations in a text are not connected to one another to form a connected structure in the form of a tree or graph.

LDC has also released the following CoNLL Shared Task data sets:


This release consists of the tokenized, tagged, and parsed tags in English and Chinese. The English train, dev and test data are from Wall Street Journal material in Penn Discourse Treebank Version 2.0 (LDC2008T05); English blind test data are from wikinews. Chinese train, dev and test data are news material from Chinese Discourse Treebank 0.5 (LDC2014T21); Chinese blind test data are from wikinews.


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