Identifying temporality of word senses based on minimum cuts
Hasanuzzaman, MohammedORCID: 0000-0003-1838-0091, Dias, Gaël, Ferrari, Stéphane, Mathet, Yann and Way, AndyORCID: 0000-0001-5736-5930
(2016)
Identifying temporality of word senses based on minimum cuts.
In: CoNLL 2016:The SIGNLL Conference on Computational Natural Language Learning, 11-12 Aug 2016, Berlin, Germany.
ISBN 978-1-945626-19-7
The ability to capture time information is
essential to many natural language processing and information retrieval applications. Therefore, a lexical resource associating word senses to their temporal orientation might be crucial for the computational tasks aiming at the interpretation of
language of time in texts. In this paper,
we propose a semi-supervised minimum
cuts strategy that makes use of WordNet
glosses and semantic relations to supplement WordNet entries with temporal information. Intrinsic and extrinsic evaluations
show that our approach outperforms prior
semi-supervised non-graph classifiers.
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Funders:
ADAPT Centre for Digital Content Technology is funded under the SFI Research Centres Programme (Grant 13/RC/2106) and is co-funded under the European Regional Development Fund.
ID Code:
23235
Deposited On:
02 May 2019 12:30 by
Thomas Murtagh
. Last Modified 04 Jan 2021 16:55