English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper explores new frontiers in agricultural natural language processingby investigating the effectiveness of using food-related text corpora forpretraining transformer-based language models. In particular, we focus on thetask of semantic matching, which involves establishing mappings between fooddescriptions and nutrition data. To accomplish this, we fine-tune a pre-trainedtransformer-based language model, AgriBERT, on this task, utilizing an externalsource of knowledge, such as the FoodOn ontology. To advance the field ofagricultural NLP, we propose two new avenues of exploration: (1) utilizingGPT-based models as a baseline and (2) leveraging ChatGPT as an external sourceof knowledge. ChatGPT has shown to be a strong baseline in many NLP tasks, andwe believe it has the potential to improve our model in the task of semanticmatching and enhance our model's understanding of food-related concepts andrelationships. Additionally, we experiment with other applications, such ascuisine prediction based on food ingredients, and expand the scope of ourresearch to include other NLP tasks beyond semantic matching. Overall, thispaper provides promising avenues for future research in this field, withpotential implications for improving the performance of agricultural NLPapplications.

Exploring New Frontiers in Agricultural NLP: Investigating the Potential of Large Language Models for Food Applications