@inproceedings{fort-etal-2018-fingers,
title = "{``}Fingers in the Nose{''}: Evaluating Speakers{'} Identification of Multi-Word Expressions Using a Slightly Gamified Crowdsourcing Platform",
author = {Fort, Kar{\"e}n and
Guillaume, Bruno and
Constant, Matthieu and
Lef{\`e}bvre, Nicolas and
Pilatte, Yann-Alan},
editor = "Savary, Agata and
Ramisch, Carlos and
Hwang, Jena D. and
Schneider, Nathan and
Andresen, Melanie and
Pradhan, Sameer and
Petruck, Miriam R. L.",
booktitle = "Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions ({LAW}-{MWE}-{C}x{G}-2018)",
month = aug,
year = "2018",
address = "Santa Fe, New Mexico, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-4923",
pages = "207--213",
abstract = "This article presents the results we obtained in crowdsourcing French speakers{'} intuition concerning multi-work expressions (MWEs). We developed a slightly gamified crowdsourcing platform, part of which is designed to test users{'} ability to identify MWEs with no prior training. The participants perform relatively well at the task, with a recall reaching 65{\%} for MWEs that do not behave as function words.",
}
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%0 Conference Proceedings
%T “Fingers in the Nose”: Evaluating Speakers’ Identification of Multi-Word Expressions Using a Slightly Gamified Crowdsourcing Platform
%A Fort, Karën
%A Guillaume, Bruno
%A Constant, Matthieu
%A Lefèbvre, Nicolas
%A Pilatte, Yann-Alan
%Y Savary, Agata
%Y Ramisch, Carlos
%Y Hwang, Jena D.
%Y Schneider, Nathan
%Y Andresen, Melanie
%Y Pradhan, Sameer
%Y Petruck, Miriam R. L.
%S Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions (LAW-MWE-CxG-2018)
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F fort-etal-2018-fingers
%X This article presents the results we obtained in crowdsourcing French speakers’ intuition concerning multi-work expressions (MWEs). We developed a slightly gamified crowdsourcing platform, part of which is designed to test users’ ability to identify MWEs with no prior training. The participants perform relatively well at the task, with a recall reaching 65% for MWEs that do not behave as function words.
%U https://aclanthology.org/W18-4923
%P 207-213
Markdown (Informal)
[“Fingers in the Nose”: Evaluating Speakers’ Identification of Multi-Word Expressions Using a Slightly Gamified Crowdsourcing Platform](https://aclanthology.org/W18-4923) (Fort et al., LAW-MWE 2018)
ACL