2017
EMNLP
EMNLP 2017
Noise-Clustered Distant Supervision for Relation Extraction: A Nonparametric Bayesian Perspective
Abstract
AbstractFor the task of relation extraction, distant supervision is an efficient approach to generate labeled data by aligning knowledge base with free texts. The essence of it is a challenging incomplete multi-label classification problem with sparse and noisy features. To address the challenge, this work presents a novel nonparametric Bayesian formulation for the task. Experiment results show substantially higher top precision improvements over the traditional state-of-the-art approaches.
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Interdisciplinary Bridge
— Artificial Intelligence and Machine Learning and Natural Language Processing
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Keyword Pioneer
— noise clustering
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Hot Topic Early Bird
— noisy label
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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 > Classification
Machine Learning > Learning Types > Weakly Supervised Learning
Machine Learning > Optimization & Theory > Bayesian Inference
Natural Language Processing > Applications > Information Extraction
Machine Learning > Bayesian & Probabilistic > Bayesian Inference
Artificial Intelligence > Core AI > Information Extraction
Machine Learning > Bayesian & Probabilistic > Nonparametric Bayesian