2017
EMNLP
EMNLP 2017
AMR Parsing using Stack-LSTMs
Abstract
AbstractWe present a transition-based AMR parser that directly generates AMR parses from plain text. We use Stack-LSTMs to represent our parser state and make decisions greedily. In our experiments, we show that our parser achieves very competitive scores on English using only AMR training data. Adding additional information, such as POS tags and dependency trees, improves the results further.
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Interdisciplinary Bridge
— Deep Learning and Natural Language Processing
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Keyword Pioneer
— stack lstm
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Hot Topic Early Bird
— semantic representation
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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