2017
EACL
EACL 2017
BabelDomains: Large-Scale Domain Labeling of Lexical Resources
Abstract
AbstractIn this paper we present BabelDomains, a unified resource which provides lexical items with information about domains of knowledge. We propose an automatic method that uses knowledge from various lexical resources, exploiting both distributional and graph-based clues, to accurately propagate domain information. We evaluate our methodology intrinsically on two lexical resources (WordNet and BabelNet), achieving a precision over 80% in both cases. Finally, we show the potential of BabelDomains in a supervised learning setting, clustering training data by domain for hypernym discovery.
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Interdisciplinary Bridge
— Interdisciplinary and Knowledge & Reasoning and Machine Learning and Natural Language Processing
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Trend Setter
— Transfer Learning
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Keyword Pioneer
— domain labeling
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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, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Natural Language Processing > Resources & Methods > Lexical Semantics
Natural Language Processing > Resources & Methods > Text Representation
Knowledge & Reasoning > Representation > Knowledge Graphs
Interdisciplinary > Linguistics > Computational Linguistics
Machine Learning > Application Areas > Transfer Learning