@inproceedings{yamaguchi-etal-2020-non,
title = "Non-ingredient Detection in User-generated Recipes using the Sequence Tagging Approach",
author = "Yamaguchi, Yasuhiro and
Inuzuka, Shintaro and
Hiramatsu, Makoto and
Harashima, Jun",
editor = "Xu, Wei and
Ritter, Alan and
Baldwin, Tim and
Rahimi, Afshin",
booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wnut-1.11",
doi = "10.18653/v1/2020.wnut-1.11",
pages = "76--80",
abstract = "Recently, the number of user-generated recipes on the Internet has increased. In such recipes, users are generally supposed to write a title, an ingredient list, and steps to create a dish. However, some items in an ingredient list in a user-generated recipe are not actually edible ingredients. For example, headings, comments, and kitchenware sometimes appear in an ingredient list because users can freely write the list in their recipes. Such noise makes it difficult for computers to use recipes for a variety of tasks, such as calorie estimation. To address this issue, we propose a non-ingredient detection method inspired by a neural sequence tagging model. In our experiment, we annotated 6,675 ingredients in 600 user-generated recipes and showed that our proposed method achieved a 93.3 F1 score.",
}
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<abstract>Recently, the number of user-generated recipes on the Internet has increased. In such recipes, users are generally supposed to write a title, an ingredient list, and steps to create a dish. However, some items in an ingredient list in a user-generated recipe are not actually edible ingredients. For example, headings, comments, and kitchenware sometimes appear in an ingredient list because users can freely write the list in their recipes. Such noise makes it difficult for computers to use recipes for a variety of tasks, such as calorie estimation. To address this issue, we propose a non-ingredient detection method inspired by a neural sequence tagging model. In our experiment, we annotated 6,675 ingredients in 600 user-generated recipes and showed that our proposed method achieved a 93.3 F1 score.</abstract>
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%0 Conference Proceedings
%T Non-ingredient Detection in User-generated Recipes using the Sequence Tagging Approach
%A Yamaguchi, Yasuhiro
%A Inuzuka, Shintaro
%A Hiramatsu, Makoto
%A Harashima, Jun
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%Y Rahimi, Afshin
%S Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F yamaguchi-etal-2020-non
%X Recently, the number of user-generated recipes on the Internet has increased. In such recipes, users are generally supposed to write a title, an ingredient list, and steps to create a dish. However, some items in an ingredient list in a user-generated recipe are not actually edible ingredients. For example, headings, comments, and kitchenware sometimes appear in an ingredient list because users can freely write the list in their recipes. Such noise makes it difficult for computers to use recipes for a variety of tasks, such as calorie estimation. To address this issue, we propose a non-ingredient detection method inspired by a neural sequence tagging model. In our experiment, we annotated 6,675 ingredients in 600 user-generated recipes and showed that our proposed method achieved a 93.3 F1 score.
%R 10.18653/v1/2020.wnut-1.11
%U https://aclanthology.org/2020.wnut-1.11
%U https://doi.org/10.18653/v1/2020.wnut-1.11
%P 76-80
Markdown (Informal)
[Non-ingredient Detection in User-generated Recipes using the Sequence Tagging Approach](https://aclanthology.org/2020.wnut-1.11) (Yamaguchi et al., WNUT 2020)
ACL