Title
Fine-Grained and Complex Food Entity Recognition Benchmark for Ingredient Substitution
Authors
[ 1 ] Instytut Informatyki, Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ P ] employee
Scientific discipline (Law 2.0)
Year of publication
2023
Chapter type
chapter in monograph / paper
Publication language
english
Keywords
EN
- semistructured data
- named entity recognition
- information extraction
- food computing
Abstract
EN Food computing is currently fast-growing into an innovative area of knowledge extraction. However, benchmarks for information extraction from semi-structured data, especially when dealing with more complex relations, are scarce in this domain. In this paper, we introduce a benchmark aimed at information extraction of complex entities to support ingredient substitution tasks. Firstly, we present a new dataset – called TASTEset – for fine-grained recognition of food entities in culinary recipes. Secondly, we provide complex entity annotations for substitution on top of the fine-grained entity mentions, which we carefully prepared. We share the dataset and the tasks to encourage progress on more in-depth and complex information extraction from recipes.
Date of online publication
05.12.2023
Pages (from - to)
25 - 29
Ministry points / chapter
20
Ministry points / conference (CORE)
70
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