Benchmarking the Semantics of Taste: Towards the Automatic Extraction of Gustatory Language
Authors: Teresa Paccosi, Sara Tonelli (Fondazione Bruno Kessler, Università di Trento, DHLab / KNAW Humanities Cluster, 2024)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
In this paper, we present a benchmark containing texts manually annotated with gustatory semantic information. We employ a FrameNet-like approach previously tested to address olfactory language, which we adapt to capture gustatory events. We then propose an exploration of the data in the benchmark to show the possible insights brought by this type of approach, addressing the investigation of emotional valence in text genres. Eventually, we present a supervised system trained with the taste benchmark for the extraction of gustatory information from historical and contemporary texts.

Introduction
Despite the central role of nutrition in our lives, taste has been often classified as an inferior sense in the Western philosophical tradition. This downplayed role is reflected in the vocabulary used to describe the gustatory experience, which, together with smell, is characterized by a scarcity of domain-specific terms AUTHOR. The difficulty in capturing the semantics of taste could help explain why there are few works in the fields of Natural Language Processing (NLP) and Digital Humanities (DH) that deal with this sense and, in particular, the language used to describe its experience. While there has been renewed interest in the automatic extraction of nutrients and ingredients from texts for health and medicinal purpose AUTHOR, less attention has been devoted to the development of tools and models focused on capturing the semantics of sensory experiences, especially in a diachronic fashion. In this paper, we present an English benchmark for the study of gustatory language and a supervised system for the automatic extraction of taste-related events in English, which we trained using this benchmark. The benchmark was built to be a counterpart to the olfactory one presented in AUTHOR, with the idea of making the study of the language of these two senses comparable. The system is designed as a means to study the language used to describe the experience of tasting from both synchronic and diachronic perspectives. The selected formal representation for the semantics of taste is based on Frame Semantics AUTHOR, and the system is trained to identify the lexical units and the possible semantic roles contributing to the construction of a gustatory event. We present the results of the experiments and an exploration of the benchmark data, aiming to demonstrate the potential of frame-based analysis for sensory studies.

Conclusion
In this paper, we presented a benchmark for gustatory events containing manually annotated taste-related information, built as a counterpart to the one proposed in AUTHOR. The benchmark is constructed with the same approach adopting a frame-based methodological framework to analyze sensory language. We emphasized the importance of frame-based analysis to capture sensory events by exploring the characterization of positive and negative valence in the benchmarks through the analysis of taste and smell words and sources. The analysis based on frames seems to bring relevant insights into capturing sensory valence from different perspectives, likely supporting the suitability of this approach to deal with humanistic inquiries. We then presented a supervised system to automatically extract taste-related frames, trained on this benchmark. This preliminary exploration and the results obtained with our experiments seem promising for future exploration with automatically extracted data. Indeed, the limited data of the benchmark are not enough to draw relevant conclusions, and for this reason we plan to use our system to extract more data and conduct large-scale analyses of the evolution of sensory information over time. The limited number of documents is likely a contributing factor to the significant discrepancies in accuracy among the different frame elements, necessitating more instances to enable a good generalization. Future steps should involve increasing the number of documents and providing less sparse annotations, aiming for better temporal balance. The focus should be on annotating frame elements with lower scores and fewer instances in the benchmark, such as Taste\_Carrier and Location. Additionally, alternative metrics and techniques should be employed to capture and explain performance variations across different models. As a further comparison, we plan also to assess the performance of general-purpose frame semantic parsers like LOME AUTHOR on our benchmark.