2017
IJCNLP
IJCNLP 2017
Identifying Speakers and Listeners of Quoted Speech in Literary Works
Abstract
AbstractWe present the first study that evaluates both speaker and listener identification for direct speech in literary texts. Our approach consists of two steps: identification of speakers and listeners near the quotes, and dialogue chain segmentation. Evaluation results show that this approach outperforms a rule-based approach that is state-of-the-art on a corpus of literary texts.
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Interdisciplinary Bridge
— Interdisciplinary and Natural Language Processing
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Keyword Pioneer
— listener identification
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Machine Learning, Mathematics & Optimization, Natural Language Processing, Speech & Audio