2017
EACL
EACL 2017
Large-scale Opinion Relation Extraction with Distantly Supervised Neural Network
Abstract
AbstractWe investigate the task of open domain opinion relation extraction. Different from works on manually labeled corpus, we propose an efficient distantly supervised framework based on pattern matching and neural network classifiers. The patterns are designed to automatically generate training data, and the deep learning model is design to capture various lexical and syntactic features. The result algorithm is fast and scalable on large-scale corpus. We test the system on the Amazon online review dataset. The result shows that our model is able to achieve promising performances without any human annotations.
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Interdisciplinary Bridge
— Deep Learning and Machine Learning and Natural Language Processing
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Keyword Pioneer
— opinion extraction
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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 > Learning Types > Weakly Supervised Learning
Deep Learning > Architectures > Neural Networks
Natural Language Processing > Applications > Information Extraction
Machine Learning > Learning Types > Representation Learning
Deep Learning > Learning Types > Self-Supervised Learning
Deep Learning > Learning Types > Representation Learning