Computer Science > Computation and Language
[Submitted on 2 Sep 2019 (v1), last revised 28 Dec 2019 (this version, v2)]
Title:Minimally Supervised Learning of Affective Events Using Discourse Relations
View PDFAbstract:Recognizing affective events that trigger positive or negative sentiment has a wide range of natural language processing applications but remains a challenging problem mainly because the polarity of an event is not necessarily predictable from its constituent words. In this paper, we propose to propagate affective polarity using discourse relations. Our method is simple and only requires a very small seed lexicon and a large raw corpus. Our experiments using Japanese data show that our method learns affective events effectively without manually labeled data. It also improves supervised learning results when labeled data are small.
Submission history
From: Jun Saito [view email][v1] Mon, 2 Sep 2019 12:46:26 UTC (113 KB)
[v2] Sat, 28 Dec 2019 13:32:04 UTC (113 KB)
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