Minimally-Supervised Joint Learning of Event Volitionality and Subject Animacy Classification
Keywords:Speech & Natural Language Processing (SNLP), Machine Learning (ML)
AbstractVolitionality 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 events so that it can perform well on unlabeled events without the words used for labeling. We view 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.
How to Cite
Kiyomaru, H., & Kurohashi, S. (2022). Minimally-Supervised Joint Learning of Event Volitionality and Subject Animacy Classification. Proceedings of the AAAI Conference on Artificial Intelligence, 36(10), 10921-10929. https://doi.org/10.1609/aaai.v36i10.21339
AAAI Technical Track on Speech and Natural Language Processing