Lexicon information in neural sentiment analysis: a multi-task learning approach

Jeremy Barnes, Samia Touileb, Lilja Øvrelid, Erik Velldal


Abstract
This paper explores the use of multi-task learning (MTL) for incorporating external knowledge in neural models. Specifically, we show how MTL can enable a BiLSTM sentiment classifier to incorporate information from sentiment lexicons. Our MTL set-up is shown to improve model performance (compared to a single-task set-up) on both English and Norwegian sentence-level sentiment datasets. The paper also introduces a new sentiment lexicon for Norwegian.
Anthology ID:
W19-6119
Volume:
Proceedings of the 22nd Nordic Conference on Computational Linguistics
Month:
September–October
Year:
2019
Address:
Turku, Finland
Venues:
NoDaLiDa | WS
SIG:
Publisher:
Linköping University Electronic Press
Note:
Pages:
175–186
Language:
URL:
https://www.aclweb.org/anthology/W19-6119
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/W19-6119.pdf