2017
IJCNLP
IJCNLP 2017
Integrating Subject, Type, and Property Identification for Simple Question Answering over Knowledge Base
Abstract
AbstractThis paper presents an approach to identify subject, type and property from knowledge base (KB) for answering simple questions. We propose new features to rank entity candidates in KB. Besides, we split a relation in KB into type and property. Each of them is modeled by a bi-directional LSTM. Experimental results show that our model achieves the state-of-the-art performance on the SimpleQuestions dataset. The hard questions in the experiments are also analyzed in detail.
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Interdisciplinary Bridge
— Artificial Intelligence and Deep Learning and Natural Language Processing
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Keyword Pioneer
— entity identification
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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