SeCoDa: Sense Complexity Dataset
David Strohmaier, Sian Gooding, Shiva Taslimipoor, Ekaterina Kochmar
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.- Anthology ID:
- 2020.lrec-1.730
- Volume:
- Proceedings of the Twelfth Language Resources and Evaluation Conference
- Month:
- May
- Year:
- 2020
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 5962–5967
- Language:
- English
- URL:
- https://aclanthology.org/2020.lrec-1.730
- DOI:
- Bibkey:
- Cite (ACL):
- David Strohmaier, Sian Gooding, Shiva Taslimipoor, and Ekaterina Kochmar. 2020. SeCoDa: Sense Complexity Dataset. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 5962–5967, Marseille, France. European Language Resources Association.
- Cite (Informal):
- SeCoDa: Sense Complexity Dataset (Strohmaier et al., LREC 2020)
- Copy Citation:
- PDF:
- https://aclanthology.org/2020.lrec-1.730.pdf
Export citation
@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",
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Twelfth 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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%0 Conference Proceedings %T SeCoDa: Sense Complexity Dataset %A Strohmaier, David %A Gooding, Sian %A Taslimipoor, Shiva %A Kochmar, Ekaterina %Y Calzolari, Nicoletta %Y Béchet, Frédéric %Y Blache, Philippe %Y Choukri, Khalid %Y Cieri, Christopher %Y Declerck, Thierry %Y Goggi, Sara %Y Isahara, Hitoshi %Y Maegaard, Bente %Y Mariani, Joseph %Y Mazo, Hélène %Y Moreno, Asuncion %Y Odijk, Jan %Y Piperidis, Stelios %S Proceedings of the Twelfth 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)
- SeCoDa: Sense Complexity Dataset (Strohmaier et al., LREC 2020)
ACL
- David Strohmaier, Sian Gooding, Shiva Taslimipoor, and Ekaterina Kochmar. 2020. SeCoDa: Sense Complexity Dataset. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 5962–5967, Marseille, France. European Language Resources Association.