Zobrazeno 1 - 10
of 273
pro vyhledávání: '"Stephan Oepen"'
Publikováno v:
ACM SIGCOMM Computer Communication Review. 51:29-32
RFC 9000, published in May 2021, marks an important milestone for the Internet's standardization body, the Internet Engineering Task Force (IETF): finally, the specification of the QUIC protocol is available. QUIC is the result of a five-year effort
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cea8faa4b9cb60a29ed44f08fbf7ea5f
https://doi.org/10.1145/3477482.3477488
https://doi.org/10.1145/3477482.3477488
Autor:
Jiarui Yao, Jayeol Chun, Nianwen Xue, Kenneth H. Lai, James Pustejovsky, Chu-Ren Huang, Tim O'Gorman, Andrew Cowell, Sarah R. Moeller, Martha Palmer, Meagan Vigus, Jens E. L. Van Gysel, William Croft, Jan Hajič, Stephan Oepen, Rosa Vallejos, James Martin
Publikováno v:
KI - Künstliche Intelligenz. 35:343-360
In this paper we present Uniform Meaning Representation (UMR), a meaning representation designed to annotate the semantic content of a text. UMR is primarily based on Abstract Meaning Representation (AMR), an annotation framework initially designed f
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9532fdf6652bef55fd0a027d01776a80
https://doi.org/10.1007/s13218-021-00722-w
https://doi.org/10.1007/s13218-021-00722-w
Autor:
Angelina Ivanova, Stephan Oepen, Rebecca Dridan, Dan Flickinger, Lilja Øvrelid, Emanuele Lapponi
Publikováno v:
Journal of Language Modelling, Vol 4, Iss 1 (2016)
We compare three different approaches to parsing into syntactic, bi- lexical dependencies for English: a ‘direct’ data-driven dependency parser, a statistical phrase structure parser, and a hybrid, ‘deep’ grammar-driven parser. The analyses f
Externí odkaz:
https://doaj.org/article/0df2c5afa1a04faba8f5434681121b52
This paper demonstrates how a graph-based semantic parser can be applied to the task of structured sentiment analysis, directly predicting sentiment graphs from text. We advance the state of the art on 4 out of 5 standard benchmark sets. We release t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::62c31b7ea7b36e3ff9d1a1aca6d8c296
We discuss methodological choices in diagnostic evaluation and error analysis in meaning representation parsing (MRP), i.e. mapping from natural language utterances to graph-based encodings of semantic structure. We expand on a pilot quantitative stu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9053b42a866784111d4b3ad98903cac8
http://hdl.handle.net/10852/101100
http://hdl.handle.net/10852/101100
Publikováno v:
ACL/IJCNLP (1)
Structured sentiment analysis attempts to extract full opinion tuples from a text, but over time this task has been subdivided into smaller and smaller sub-tasks, e,g,, target extraction or targeted polarity classification. We argue that this divisio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f8eda53e70d795c28e7766038246afa4
http://arxiv.org/abs/2105.14504
http://arxiv.org/abs/2105.14504
Autor:
Stephan Oepen, Marco Kuhlmann
Publikováno v:
Computational Linguistics. :1-13
Graphs exceeding the formal complexity of rooted trees are of growing relevance to much NLP research. Although formally well understood in graph theory, there is substantial variation in the types of linguistic graphs, as well as in the interpretatio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8e1ff5694145d3630737187da5edd802
https://doi.org/10.1162/coli_a_00280
https://doi.org/10.1162/coli_a_00280
Autor:
Nianwen Xue, Daniel Hershcovich, Stephan Oepen, Zdenka Uresova, Omri Abend, Milan Straka, Jayeol Chun, Tim O'Gorman, Jan Hajič, Marco Kuhlmann
Publikováno v:
CoNLL Shared Task
Oepen, S, Abend, O, Hajic, J, Hershcovich, D, Kuhlmann, M, O’gorman, T, Xue, N, Chun, J, Straka, M & Uresova, Z 2020, MRP 2019 : Cross-Framework Meaning Representation Parsing . in Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 Conference on Natural Language Learning . Association for Computational Linguistics, pp. 1-27, 2019 Conference on Natural Language Learning, CoNLL, Hong Kong, China, 01/11/2019 . https://doi.org/10.18653/v1/K19-2001
Oepen, S, Abend, O, Hajic, J, Hershcovich, D, Kuhlmann, M, O’gorman, T, Xue, N, Chun, J, Straka, M & Uresova, Z 2020, MRP 2019 : Cross-Framework Meaning Representation Parsing . in Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 Conference on Natural Language Learning . Association for Computational Linguistics, pp. 1-27, 2019 Conference on Natural Language Learning, CoNLL, Hong Kong, China, 01/11/2019 . https://doi.org/10.18653/v1/K19-2001
The 2019 Shared Task at the Conference for Computational Language Learning (CoNLL) was devoted to Meaning Representation Parsing (MRP) across frameworks. Five distinct approaches to the representation of sentence meaning in the form of directed graph
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::88644464af16c533504bdd9d354c8e0c
https://doi.org/10.18653/v1/k19-2001
https://doi.org/10.18653/v1/k19-2001
Autor:
Dan Flickinger, Stephan Oepen
Publikováno v:
CoNLL Shared Task
The English Resource Grammar (ERG) is a broad-coverage computational grammar of English that outputs underspecified logical-form representations of meaning in a framework dubbed English Resource Semantics (ERS). Two of the target representations in t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a90e4b14d254902cc49b20a24dafb2fe
https://doi.org/10.18653/v1/k19-2003
https://doi.org/10.18653/v1/k19-2003
Publikováno v:
ACL (4)
This tutorial is on representing and processing sentence meaning in the form of labeled directed graphs. The tutorial will (a) briefly review relevant background in formal and linguistic semantics; (b) semi-formally define a unified abstract view on
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6fad3a8b24fb50ce0673a103b37277e0
https://doi.org/10.18653/v1/p19-4002
https://doi.org/10.18653/v1/p19-4002