Fine-Grained and Complex Food Entity Recognition Benchmark for Ingredient Substitution
[ 1 ] Instytut Informatyki, Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ P ] pracownik
2023
rozdział w monografii naukowej / referat
angielski
- semistructured data
- named entity recognition
- information extraction
- food computing
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.
05.12.2023
25 - 29
12th Knowledge Capture Conference K-CAP '23, 5-7.12.2023, Pensacola, USA
20
70