2020
EMNLP
EMNLP 2020
Within-Between Lexical Relation Classification
Abstract
AbstractWe propose the novel Within-Between Relation model for recognizing lexical-semantic relations between words. Our model integrates relational and distributional signals, forming an effective sub-space representation for each relation. We show that the proposed model is competitive and outperforms other baselines, across various benchmarks.
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Interdisciplinary Bridge
— Interdisciplinary and Machine Learning and Natural Language Processing
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Keyword Pioneer
— lexical relation classification
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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
Authors
Topics
Machine Learning > Core Methods > Classification
Machine Learning > Core Methods > Representation Learning
Machine Learning > Core Methods > Metric Learning
Natural Language Processing > Understanding > Semantic Analysis
Interdisciplinary > Linguistics > Semantics
Machine Learning > Learning Types > Classification
Natural Language Processing > Applications > Semantic Analysis