2017
ACL
ACL 2017
Improving Semantic Composition with Offset Inference
Abstract
AbstractCount-based distributional semantic models suffer from sparsity due to unobserved but plausible co-occurrences in any text collection. This problem is amplified for models like Anchored Packed Trees (APTs), that take the grammatical type of a co-occurrence into account. We therefore introduce a novel form of distributional inference that exploits the rich type structure in APTs and infers missing data by the same mechanism that is used for semantic composition.
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Interdisciplinary Bridge
— Knowledge & Reasoning and Machine Learning and Natural Language Processing
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Trend Setter
— Semantics
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Keyword Pioneer
— sparsity problem
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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
Machine Learning > Core Methods > Representation Learning
Natural Language Processing > Resources & Methods > Lexical Semantics
Knowledge & Reasoning > Representation > Knowledge Representation
Machine Learning > Bayesian & Probabilistic > Probabilistic Modeling
Machine Learning > Bayesian & Probabilistic > Bayesian Inference
Natural Language Processing > Understanding > Semantics