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Register a new userMinimally Supervised Learning of Affective Events Using Discourse Relations
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Added by
Jun Saito
Publication date
2019
fields
Informatics Engineering
and research's language is
English
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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.
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