@inproceedings{poelman-etal-2022-rug,
title = "{RUG}-1-Pegasussers at {S}em{E}val-2022 Task 3: Data Generation Methods to Improve Recognizing Appropriate Taxonomic Word Relations",
author = "van den Berg, Frank and
Danoe, Gijs and
Ploeger, Esther and
Poelman, Wessel and
Edman, Lukas and
Caselli, Tommaso",
editor = "Emerson, Guy and
Schluter, Natalie and
Stanovsky, Gabriel and
Kumar, Ritesh and
Palmer, Alexis and
Schneider, Nathan and
Singh, Siddharth and
Ratan, Shyam",
booktitle = "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.semeval-1.31",
doi = "10.18653/v1/2022.semeval-1.31",
pages = "247--254",
abstract = "This paper describes our system created for the SemEval 2022 Task 3: Presupposed Taxonomies - Evaluating Neural-network Semantics. This task is focused on correctly recognizing taxonomic word relations in English, French and Italian. We developed various datageneration techniques that expand the originally provided train set and show that all methods increase the performance of modelstrained on these expanded datasets. Our final system outperformed the baseline system from the task organizers by achieving an average macro F1 score of 79.6 on all languages, compared to the baseline{'}s 67.4.",
}
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<abstract>This paper describes our system created for the SemEval 2022 Task 3: Presupposed Taxonomies - Evaluating Neural-network Semantics. This task is focused on correctly recognizing taxonomic word relations in English, French and Italian. We developed various datageneration techniques that expand the originally provided train set and show that all methods increase the performance of modelstrained on these expanded datasets. Our final system outperformed the baseline system from the task organizers by achieving an average macro F1 score of 79.6 on all languages, compared to the baseline’s 67.4.</abstract>
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%0 Conference Proceedings
%T RUG-1-Pegasussers at SemEval-2022 Task 3: Data Generation Methods to Improve Recognizing Appropriate Taxonomic Word Relations
%A van den Berg, Frank
%A Danoe, Gijs
%A Ploeger, Esther
%A Poelman, Wessel
%A Edman, Lukas
%A Caselli, Tommaso
%Y Emerson, Guy
%Y Schluter, Natalie
%Y Stanovsky, Gabriel
%Y Kumar, Ritesh
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y Singh, Siddharth
%Y Ratan, Shyam
%S Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F poelman-etal-2022-rug
%X This paper describes our system created for the SemEval 2022 Task 3: Presupposed Taxonomies - Evaluating Neural-network Semantics. This task is focused on correctly recognizing taxonomic word relations in English, French and Italian. We developed various datageneration techniques that expand the originally provided train set and show that all methods increase the performance of modelstrained on these expanded datasets. Our final system outperformed the baseline system from the task organizers by achieving an average macro F1 score of 79.6 on all languages, compared to the baseline’s 67.4.
%R 10.18653/v1/2022.semeval-1.31
%U https://aclanthology.org/2022.semeval-1.31
%U https://doi.org/10.18653/v1/2022.semeval-1.31
%P 247-254
Markdown (Informal)
[RUG-1-Pegasussers at SemEval-2022 Task 3: Data Generation Methods to Improve Recognizing Appropriate Taxonomic Word Relations](https://aclanthology.org/2022.semeval-1.31) (van den Berg et al., SemEval 2022)
ACL