Sadhana Kumaravel
2022
DocAMR: Multi-Sentence AMR Representation and Evaluation
Tahira Naseem
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Austin Blodgett
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Sadhana Kumaravel
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Tim O’Gorman
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Young-Suk Lee
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Jeffrey Flanigan
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Ramón Astudillo
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Radu Florian
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Salim Roukos
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Nathan Schneider
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Despite extensive research on parsing of English sentences into Abstract Meaning Representation (AMR) graphs, which are compared to gold graphs via the Smatch metric, full-document parsing into a unified graph representation lacks well-defined representation and evaluation. Taking advantage of a super-sentential level of coreference annotation from previous work, we introduce a simple algorithm for deriving a unified graph representation, avoiding the pitfalls of information loss from over-merging and lack of coherence from under merging. Next, we describe improvements to the Smatch metric to make it tractable for comparing document-level graphs and use it to re-evaluate the best published document-level AMR parser. We also present a pipeline approach combining the top-performing AMR parser and coreference resolution systems, providing a strong baseline for future research.
2016
Cross Sentence Inference for Process Knowledge
Samuel Louvan
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Chetan Naik
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Sadhana Kumaravel
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Heeyoung Kwon
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Niranjan Balasubramanian
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Peter Clark
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing