Minimally-Supervised Joint Learning of Event Volitionality and Subject Animacy Classification

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  • Kiyomaru Hirokazu
    京都大学大学院 情報学研究科，Kyoto University, Graduate School of Informatics
  • Kurohashi Sadao
    京都大学大学院 情報学研究科，Kyoto University, Graduate School of Informatics

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  • 弱教師あり学習によるイベントの意志性・主語有生性の分類の同時学習

Abstract

<p>Volitionality and subject animacy are fundamental and closely related properties of an event. Their classification is challenging because it requires contextual text understanding and a huge amount of labeled data. This paper proposes a novel method that jointly learns volitionality and subject animacy at a low cost, heuristically labeling events in a raw corpus. Volitionality labels are assigned using a small lexicon of volitional and non-volitional adverbs such as “deliberately” and “accidentally”; subject animacy labels are assigned using a list of animate and inanimate nouns obtained from ontological knowledge. We then consider the problem of learning a classifier from the labeled data so that it can perform well on unlabeled events without the words used for labeling. We regard the problem as a bias reduction or unsupervised domain adaptation problem and apply the techniques. We conduct experiments with crowdsourced gold data in Japanese and English and show that our method effectively learns volitionality and subject animacy without manually labeled data. </p>

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