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pro vyhledávání: '"Fowlie, Meaghan"'
We present the Granular AMR Parsing Evaluation Suite (GrAPES), a challenge set for Abstract Meaning Representation (AMR) parsing with accompanying evaluation metrics. AMR parsers now obtain high scores on the standard AMR evaluation metric Smatch, cl
Externí odkaz:
http://arxiv.org/abs/2312.03480
AM dependency parsing is a method for neural semantic graph parsing that exploits the principle of compositionality. While AM dependency parsers have been shown to be fast and accurate across several graphbanks, they require explicit annotations of t
Externí odkaz:
http://arxiv.org/abs/2106.04398
Publikováno v:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2018
We present a semantic parser for Abstract Meaning Representations which learns to parse strings into tree representations of the compositional structure of an AMR graph. This allows us to use standard neural techniques for supertagging and dependency
Externí odkaz:
http://arxiv.org/abs/1805.11465
Publikováno v:
Proceedings of the 5th Workshop on Structured Prediction for NLP, 22
STARTPAGE=22;TITLE=Proceedings of the 5th Workshop on Structured Prediction for NLP
STARTPAGE=22;TITLE=Proceedings of the 5th Workshop on Structured Prediction for NLP
AM dependency parsing is a method for neural semantic graph parsing that exploits the principle of compositionality. While AM dependency parsers have been shown to be fast and accurate across several graphbanks, they require explicit annotations of t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::01e650e93b232159e17a4469e043f3ee
https://dspace.library.uu.nl/handle/1874/415006
https://dspace.library.uu.nl/handle/1874/415006
Autor:
Donatelli, Lucia, Fowlie, Meaghan, Groschwitz, Jonas, Koller, Alexander, Lindemann, Matthias, Mina, Mario, Weißenhorn, Pia, Oepen, Stephan, Abend, Omri, Hajic, Jan, Hershcovic, Daniel, Kuhlmann, Marco, O'Gorman, Tim, Xue, Nianwen
We describe the Saarland University submission to the shared task on Cross-Framework Meaning Representation Parsing (MRP) at the 2019 Conference on Computational Natural Language Learning (CoNLL).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______101::1d3f5bd2bac02675ec3b0fb99e237704
https://dspace.library.uu.nl/handle/1874/392340
https://dspace.library.uu.nl/handle/1874/392340
Autor:
Fowlie, Meaghan
Publikováno v:
Fowlie, Meaghan. (2017). Slaying the Great Green Dragon: Learning and modelling iterable ordered optional adjuncts. UCLA: Linguistics 0510. Retrieved from: http://www.escholarship.org/uc/item/640605fb
Adjuncts and arguments exhibit different syntactic behaviours, but modelling this difference in minimalist syntax is challenging: on the one hand, adjuncts differ from arguments in that they are optional, transparent, and iterable, but on the other h
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______325::170448a23d386676deb55303a671258b
http://www.escholarship.org/uc/item/640605fb
http://www.escholarship.org/uc/item/640605fb
Autor:
Fowlie, Meaghan
Publikováno v:
Formal Grammar: 19th International Conference, FG 2014, Tübingen, Germany, August 16-17, 2014. Proceedings; 2014, p34-51, 18p
