A Multilingual Benchmark to Capture Olfactory Situations over Time

Menini, Stefano; Paccosi, Teresa; Tonelli, Sara; Erp, Marieke van; Leemans, Inger; Lisena, Pasquale; Troncy, Raphaël; Tullett, William; Hürriyetoğlu, Ali; Dijkstra, Ger; Gordijn, F.; Jürgens, Elias; Koopman, Josephine; Ouwerkerk, Aron; Steen, Sanne; Novalija, Inna; Brank, Janez; Mladenić, Dunja; Zidar, Anja

doi:10.18653/v1/2022.lchange-1.1

Cited by 6 publications

(6 citation statements)

References 7 publications

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“…Cultural Heritage, such as the study literature, is a knowledge-intensive domain. Datasets that focus on literature typically require expert or near-expert annotators (e.g., graduate students and their advisors) [14,15], or at least, more substantial efforts beyond simple crowdsourcing. This often results in small, imbalanced datasets [16].…”

Section: Motivationmentioning

confidence: 99%

Detecting Fine-Grained Emotions in Literature

Rei

Mladenić

2023

Applied Sciences

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Emotion detection in text is a fundamental aspect of affective computing and is closely linked to natural language processing. Its applications span various domains, from interactive chatbots to marketing and customer service. This research specifically focuses on its significance in literature analysis and understanding. To facilitate this, we present a novel approach that involves creating a multi-label fine-grained emotion detection dataset, derived from literary sources. Our methodology employs a simple yet effective semi-supervised technique. We leverage textual entailment classification to perform emotion-specific weak-labeling, selecting examples with the highest and lowest scores from a large corpus. Utilizing these emotion-specific datasets, we train binary pseudo-labeling classifiers for each individual emotion. By applying this process to the selected examples, we construct a multi-label dataset. Using this dataset, we train models and evaluate their performance within a traditional supervised setting. Our model achieves an F1 score of 0.59 on our labeled gold set, showcasing its ability to effectively detect fine-grained emotions. Furthermore, we conduct evaluations of the model’s performance in zero- and few-shot transfer scenarios using benchmark datasets. Notably, our results indicate that the knowledge learned from our dataset exhibits transferability across diverse data domains, demonstrating its potential for broader applications beyond emotion detection in literature. Our contribution thus includes a multi-label fine-grained emotion detection dataset built from literature, the semi-supervised approach used to create it, as well as the models trained on it. This work provides a solid foundation for advancing emotion detection techniques and their utilization in various scenarios, especially within the cultural heritage analysis.

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Section: Motivationmentioning

confidence: 99%

Detecting Fine-Grained Emotions in Literature

Rei

Mladenić

2023

Applied Sciences

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“…As regards the development of structured re-sources to investigate the evolution of sensory language, Menini et al (2022a) present a multilingual taxonomy for olfactory-related terms, which was created semi-automatically, with the goal to describe the evolution of odours and smell sources' descriptions. Furthermore, in Menini et al (2022b), the authors present a multilingual benchmark, manually annotated with smell-related information, to support the development of olfactory information extraction systems.…”

Section: Related Workmentioning

confidence: 99%

Proceedings of the 4th Workshop on Computational Approaches to Historical Language Change

2023

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Welcome to the 4th International Workshop on Computational Approaches to Historical Language Change (LChange'23) co-located with EMNLP 2023. LChange is held on December 6th, 2023, as a hybrid event with participation possible both virtually and on-site in Singapore.Characterizing the time-varying nature of language will have broad implications and applications in multiple fields including linguistics, artificial intelligence, digital humanities, computational cognitive and social sciences. In this workshop, we bring together the world's pioneers and experts in computational approaches to historical language change with a focus on digital text corpora. In doing so, this workshop carries out the triple goals of disseminating state-of-the-art research on diachronic modeling of language change, fostering cross-disciplinary collaborations, and exploring the fundamental theoretical and methodological challenges in this growing niche of computational linguistic research.In response to the call, we received 28 submissions. Each of them was carefully evaluated by at least two members of the Program Committee, whom we believed to be most appropriate for each paper. Based on the reviewers' feedback we accepted 17 full and short papers as oral or poster presentations. We had two distinguished keynote presentations: the first by Gemma Boleda (Research Professor in the Department of Translation and Language Sciences of the Universitat Pompeu Fabra, Spain) who presented a talk entitled "What does semantic change have to do with Hello Kitty? Referring as the source of change", and the second by Mario Giulianelli (a postdoctoral fellow at ETH Zurich) with the talk "Neural language models for word usage representation and analysis". Finally, we invited five EMNLP'23 Findings papers to be presented as posters, which are not included in the workshop proceedings.To further support the community, we offered five student scholarships to cover registration fees. We also offered mentoring for four young researchers on their research topic in the field of language change, either during the workshop or virtually.We hope that you will find the workshop papers insightful and inspiring. We would like to thank the keynote speakers for their stimulating talks, the authors of all papers for their interesting contributions, and the members of the Program Committee for their insightful reviews. Our special thanks go to the emergency reviewers who stepped in to provide their expertise. We also express our gratitude to the EMNLP 2023 workshop chairs for their kind assistance during the organization process. Finally, our thanks go to our gold sponsor iguanodon.ai, as well as the research project "Towards Computational Lexical Semantic Change Detection" (Swedish Research Council, contract 2018-01184) and the research program "Change is Key!" (Riksbankens Jubileumsfond, contract M21-0021).

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“…The need for an ontology that can enable us to be consistent in the annotation of olfactory information across studies was reported by Tonelli and Menini et al (2021) [89] and [90]. On the basis of this ontology, the multilingual Odeuropa benchmark dataset was released [91]. The Odeuropa benchmark dataset is multilingual and consists of historical texts.…”

Section: Computational Linguistics: Extracting Emotions and Smellsmentioning

confidence: 99%

“…For smell extraction, we utilized the English part of the Odeuropa benchmark [91,106] to create a machine learning-based text classification model that predicts whether a sentence is smell-related or not. Although the Odeuropa is annotated at token level, we converted the sentences that contain any smell event annotation to be smell-related and remaining sentences as not-smell-related.…”

Section: Implementation Detailsmentioning

confidence: 99%

Harvesting Context and Mining Emotions Related to Olfactory Cultural Heritage

Massri

Novalija

Mladenić

et al. 2022

MTI

Self Cite

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This paper presents an Artificial Intelligence approach to mining context and emotions related to olfactory cultural heritage narratives, particularly to fairy tales. We provide an overview of the role of smell and emotions in literature, as well as highlight the importance of olfactory experience and emotions from psychology and linguistic perspectives. We introduce a methodology for extracting smells and emotions from text, as well as demonstrate the context-based visualizations related to smells and emotions implemented in a novel smell tracker tool. The evaluation is performed using a collection of fairy tales from Grimm and Andersen. We find out that fairy tales often connect smell with the emotional charge of situations. The experimental results show that we can detect smells and emotions in fairy tales with an F1 score of 91.62 and 79.2, respectively.

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A Multilingual Benchmark to Capture Olfactory Situations over Time

Cited by 6 publications

References 7 publications

Detecting Fine-Grained Emotions in Literature

Detecting Fine-Grained Emotions in Literature

Proceedings of the 4th Workshop on Computational Approaches to Historical Language Change

Harvesting Context and Mining Emotions Related to Olfactory Cultural Heritage

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