Edition 1.1 of the PARSEME shared task
on automatic identification of verbal multiword expressions
Parra Escartín, CarlaORCID: 0000-0002-8412-1525 and Walsh, Abigail
(2018)
Edition 1.1 of the PARSEME shared task
on automatic identification of verbal multiword expressions.
In: Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions (LAW-MWE-CxG-2018), 25-26 Aug 2018, Santa Fe, NM, USA.
This paper describes the PARSEME Shared Task 1.1 on automatic identification of verbal multiword expressions. We present the annotation methodology, focusing on changes from last year’s
shared task. Novel aspects include enhanced annotation guidelines, additional annotated data for
most languages, corpora for some new languages, and new evaluation settings. Corpora were
created for 20 languages, which are also briefly discussed. We report organizational principles
behind the shared task and the evaluation metrics employed for ranking. The 17 participating
systems, their methods and obtained results are also presented and analysed.
Savary, Agata, Ramisch, Carlos and Hwang, Jena D., (eds.)
Proceedings of the Joint Workshop on , Linguistic Annotation, Multiword Expressions and Constructions (LAW-MWE-CxG-2018).
.
Association for Computational Linguistics (ACL).
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Funders:
IC1207 PARSEME COST action, LDPARSEME (LD14117) Czech Republic, and PARSEME-FR25 (ANR-14-CERA-0001) in France, European Union’s Horizon 2020 programme under the Marie Skłodowska-Curie grant agreement No 713567, Science Foundation Ireland in the ADAPT Centre (Grant 13/RC/2106) at Dublin City University, Deutsche Forschungsgemeinschaft (DFG) within the CRC 991 “The Structure of Representations in Language, Cognition, and Science”, UNKP-17-4 New National Excellence Program of the Ministry of Human Capacities, Hungary., Slovenian Research Agency via New grammar of contemporary standard Slovene: sources and methods (J6-8256 project)., Ashwini Vaidya was supported by the DST-CSRI (Dept of Science and Technology, Govt. of India Cognitive Science Research Initiative), Bogaziçi University Research Fund Grant Number 14420
ID Code:
23376
Deposited On:
29 May 2019 11:18 by
Thomas Murtagh
. Last Modified 29 May 2019 11:18