2018
EMNLP
EMNLP 2018
Learning Concept Abstractness Using Weak Supervision
Abstract
AbstractWe introduce a weakly supervised approach for inferring the property of abstractness of words and expressions in the complete absence of labeled data. Exploiting only minimal linguistic clues and the contextual usage of a concept as manifested in textual data, we train sufficiently powerful classifiers, obtaining high correlation with human labels. The results imply the applicability of this approach to additional properties of concepts, additional languages, and resource-scarce scenarios.
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Interdisciplinary Bridge
— Machine Learning and Natural Language Processing
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Trend Setter
— Weakly Supervised Learning
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Keyword Pioneer
— concept abstractness
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Hot Topic Early Bird
— weak supervision
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy, Speech & Audio