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    <titleInfo>
        <title>The Impact of Named Entity Recognition on Transformer-Based Multi-Label Dietary Recipe Classification</title>
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    <name type="personal">
        <namePart type="given">Kemalcan</namePart>
        <namePart type="family">Bora</namePart>
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    <name type="personal">
        <namePart type="given">Horacio</namePart>
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            <title>Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era</title>
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        <name type="personal">
            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Maria</namePart>
            <namePart type="family">Kunilovskaya</namePart>
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        <name type="personal">
            <namePart type="given">Marie</namePart>
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        <name type="personal">
            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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            <publisher>INCOMA Ltd., Shoumen, Bulgaria</publisher>
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    <abstract>This research explores the impact of Named Entity Recognition (NER) on transformer-based models for multi-label recipe classification by dietary preference. To support this task, we introduce the NutriCuisine Index: a collection of 23,932 recipes annotated across six dietary categories (Healthy, Vegan, Gluten-Free, Low-Carb, High-Protein, Low-Sugar). Using BERT-base-uncased, RoBERTa-base, and DistilBERT-base-uncased, we evaluate how NER-based preprocessing affects the performance (F1-score, Precision, Recall, and Hamming Loss) of Transformer-based multi-label classification models. RoBERTa-base shows significant improvements with NER in F1-score (\ensuremathΔF1 = +0.0147, p \ensuremath&lt; 0.001), Precision, and Recall, while BERT and DistilBERT show no such gains. NER also leads to a slight but statistically significant increase in Hamming Loss across all models. These findings highlight the model dependent impact of NER on classification performance.</abstract>
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            <start>184</start>
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%0 Conference Proceedings
%T The Impact of Named Entity Recognition on Transformer-Based Multi-Label Dietary Recipe Classification
%A Bora, Kemalcan
%A Saggion, Horacio
%Y Angelova, Galia
%Y Kunilovskaya, Maria
%Y Escribe, Marie
%Y Mitkov, Ruslan
%S Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era
%D 2025
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F bora-saggion-2025-impact
%X This research explores the impact of Named Entity Recognition (NER) on transformer-based models for multi-label recipe classification by dietary preference. To support this task, we introduce the NutriCuisine Index: a collection of 23,932 recipes annotated across six dietary categories (Healthy, Vegan, Gluten-Free, Low-Carb, High-Protein, Low-Sugar). Using BERT-base-uncased, RoBERTa-base, and DistilBERT-base-uncased, we evaluate how NER-based preprocessing affects the performance (F1-score, Precision, Recall, and Hamming Loss) of Transformer-based multi-label classification models. RoBERTa-base shows significant improvements with NER in F1-score (\ensuremathΔF1 = +0.0147, p \ensuremath< 0.001), Precision, and Recall, while BERT and DistilBERT show no such gains. NER also leads to a slight but statistically significant increase in Hamming Loss across all models. These findings highlight the model dependent impact of NER on classification performance.
%U https://aclanthology.org/2025.ranlp-1.22/
%P 184-193

Markdown (Informal)

[The Impact of Named Entity Recognition on Transformer-Based Multi-Label Dietary Recipe Classification](https://aclanthology.org/2025.ranlp-1.22/) (Bora & Saggion, RANLP 2025)

• The Impact of Named Entity Recognition on Transformer-Based Multi-Label Dietary Recipe Classification (Bora & Saggion, RANLP 2025)

ACL

• Kemalcan Bora and Horacio Saggion. 2025. The Impact of Named Entity Recognition on Transformer-Based Multi-Label Dietary Recipe Classification. In Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era, pages 184–193, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.