@inproceedings{strohmaier-etal-2020-secoda,
title = "{S}e{C}o{D}a: Sense Complexity Dataset",
author = "Strohmaier, David and
Gooding, Sian and
Taslimipoor, Shiva and
Kochmar, Ekaterina",
booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.730",
pages = "5962--5967",
abstract = "The Sense Complexity Dataset (SeCoDa) provides a corpus that is annotated jointly for complexity and word senses. It thus provides a valuable resource for both word sense disambiguation and the task of complex word identification. The intention is that this dataset will be used to identify complexity at the level of word senses rather than word tokens. For word sense annotation SeCoDa uses a hierarchical scheme that is based on information available in the Cambridge Advanced Learner{'}s Dictionary. This way we can offer more coarse-grained senses than directly available in WordNet.",
language = "English",
ISBN = "979-10-95546-34-4",
}
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<abstract>The Sense Complexity Dataset (SeCoDa) provides a corpus that is annotated jointly for complexity and word senses. It thus provides a valuable resource for both word sense disambiguation and the task of complex word identification. The intention is that this dataset will be used to identify complexity at the level of word senses rather than word tokens. For word sense annotation SeCoDa uses a hierarchical scheme that is based on information available in the Cambridge Advanced Learner’s Dictionary. This way we can offer more coarse-grained senses than directly available in WordNet.</abstract>
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%0 Conference Proceedings
%T SeCoDa: Sense Complexity Dataset
%A Strohmaier, David
%A Gooding, Sian
%A Taslimipoor, Shiva
%A Kochmar, Ekaterina
%S Proceedings of the 12th Language Resources and Evaluation Conference
%D 2020
%8 may
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-34-4
%G English
%F strohmaier-etal-2020-secoda
%X The Sense Complexity Dataset (SeCoDa) provides a corpus that is annotated jointly for complexity and word senses. It thus provides a valuable resource for both word sense disambiguation and the task of complex word identification. The intention is that this dataset will be used to identify complexity at the level of word senses rather than word tokens. For word sense annotation SeCoDa uses a hierarchical scheme that is based on information available in the Cambridge Advanced Learner’s Dictionary. This way we can offer more coarse-grained senses than directly available in WordNet.
%U https://aclanthology.org/2020.lrec-1.730
%P 5962-5967
Markdown (Informal)
[SeCoDa: Sense Complexity Dataset](https://aclanthology.org/2020.lrec-1.730) (Strohmaier et al., LREC 2020)
ACL
- David Strohmaier, Sian Gooding, Shiva Taslimipoor, and Ekaterina Kochmar. 2020. SeCoDa: Sense Complexity Dataset. In Proceedings of the 12th Language Resources and Evaluation Conference, pages 5962–5967, Marseille, France. European Language Resources Association.