2017
EMNLP
EMNLP 2017
Deep Multi-Task Learning for Aspect Term Extraction with Memory Interaction
Abstract
AbstractWe propose a novel LSTM-based deep multi-task learning framework for aspect term extraction from user review sentences. Two LSTMs equipped with extended memories and neural memory operations are designed for jointly handling the extraction tasks of aspects and opinions via memory interactions. Sentimental sentence constraint is also added for more accurate prediction via another LSTM. Experiment results over two benchmark datasets demonstrate the effectiveness of our framework.
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Interdisciplinary Bridge
— Artificial Intelligence and Deep Learning and Machine Learning and Natural Language Processing
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Keyword Pioneer
— aspect term extraction
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Hot Topic Early Bird
— opinion mining
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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
Topics
Machine Learning > Core Methods > Representation Learning
Machine Learning > Learning Types > Multi-Task Learning
Natural Language Processing > Applications > Sentiment Analysis
Deep Learning > Learning Types > Multi-Task Learning
Artificial Intelligence > Core AI > Natural Language Processing
Artificial Intelligence > Core AI > Multi-Task Learning