Skip to Main Content (Press Enter)

Logo UNIOR
  • ×
  • Home
  • Corsi
  • Insegnamenti
  • Persone
  • Strutture
  • Attrezzature

UNIFIND
Logo UNIOR

|

UNIFIND

unior.it
  • ×
  • Home
  • Corsi
  • Insegnamenti
  • Persone
  • Strutture
  • Attrezzature

Edition 1.2 of the PARSEME Shared Task on Semi-supervised Identification of Verbal Multiword Expressions

Contributo in Atti di convegno
Data di Pubblicazione:
2020
Abstract:
We present edition 1.2 of the PARSEME shared task on identification of verbal multiword expressions (VMWEs). Lessons learned from previous editions indicate that VMWEs have low ambiguity, and that the major challenge lies in identifying test instances never seen in the training data. Therefore, this edition focuses on unseen VMWEs. We have split annotated corpora so that the test corpora contain around 300 unseen VMWEs, and we provide non-annotated raw corpora to be used by complementary discovery methods. We released annotated and raw corpora in 14 languages, and this semi-supervised challenge attracted 7 teams who submitted 9 system results. This paper describes the effort of corpus creation, the task design, and the results obtained by the participating systems, especially their performance on unseen expressions.
Tipologia CRIS:
4.1 Contributo in Atti di convegno
Keywords:
verbal multiword expressions, corpora, annotated corpora, raw corpora, PARSEME, shared task
Elenco autori:
Ramisch, Carlos; Savary, Agata; Guillaume, Bruno; Waszczuk, Jakub; Candito, Marie; Vaidya, Ashwini; Barbu Mititelu, Verginica; Bhatia, Archna; IƱurrieta, Uxoa; Giouli, Voula; Gungor, Tunga; Jiang, Menghan; Lichte, Timm; Liebeskind, Chaya; Monti, Johanna; Ramisch, Renata; Stymne, Sara; Walsh, Abigail; Xu, Hongzhi
Autori di Ateneo:
MONTI JOHANNA
Link alla scheda completa:
https://unora.unior.it/handle/11574/194985
Link al Full Text:
https://unora.unior.it//retrieve/handle/11574/194985/76867/2020.mwe-1.14.pdf
Titolo del libro:
Proceedings of the Joint Workshop on Multiword Expressions and Electronic Lexicons
  • Dati Generali

Dati Generali

URL

https://www.aclweb.org/anthology/2020.mwe-1.14
  • Utilizzo dei cookie

Realizzato con VIVO | Designed by Cineca | 26.10.0.0