SeCoDa: Sense Complexity Dataset

David Strohmaier; Sian Gooding; Shiva Taslimipoor; Ekaterina Kochmar

SeCoDa: Sense Complexity Dataset

David Strohmaier, Sian Gooding, Shiva Taslimipoor, Ekaterina Kochmar

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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.

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)
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PDF:: https://aclanthology.org/2020.lrec-1.730.pdf

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@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",
    language = "eng",
    ISBN = "979-10-95546-34-4",
    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."
}

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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 eng
%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

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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.