Abstract Meaning Representation Parsing using LSTM Recurrent Neural Networks
William Foland, James Martin · 2017
We present a system which parses sentences into Abstract Meaning Representations, improving state-of-the-art results for this task by more than 5%.AMR graphs represent semantic content using linguistic properties such as semantic roles, coreference, negation, and more.The AMR parser does not rely on a syntactic preparse, or heavily engineered features, and uses five recurrent neural networks as the key architectural components for inferring AMR graphs.