William Foland


2017

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Abstract Meaning Representation Parsing using LSTM Recurrent Neural Networks
William Foland | James H. Martin
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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 pre-parse, or heavily engineered features, and uses five recurrent neural networks as the key architectural components for inferring AMR graphs.

2016

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CU-NLP at SemEval-2016 Task 8: AMR Parsing using LSTM-based Recurrent Neural Networks
William Foland | James H. Martin
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

2015

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Dependency-Based Semantic Role Labeling using Convolutional Neural Networks
William Foland | James Martin
Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics