Yasuhiro Yamaguchi


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Non-ingredient Detection in User-generated Recipes using the Sequence Tagging Approach
Yasuhiro Yamaguchi | Shintaro Inuzuka | Makoto Hiramatsu | Jun Harashima
Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)

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.