Papers › Lexicon information in neural sentiment analysis: a multi-task learning approach
Lexicon information in neural sentiment analysis: a multi-task learning approach
Jeremy Barnes, Samia Touileb, Lilja Øvrelid, Erik Velldal
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.
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