@inproceedings{menini-etal-2023-scent,
title = "Scent Mining: Extracting Olfactory Events, Smell Sources and Qualities",
author = "Menini, Stefano and
Paccosi, Teresa and
Tekiro{\u{g}}lu, Serra Sinem and
Tonelli, Sara",
editor = "Degaetano-Ortlieb, Stefania and
Kazantseva, Anna and
Reiter, Nils and
Szpakowicz, Stan",
booktitle = "Proceedings of the 7th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.latechclfl-1.15",
doi = "10.18653/v1/2023.latechclfl-1.15",
pages = "135--140",
abstract = "Olfaction is a rather understudied sense compared to the other senses. In NLP, however, there have been recent attempts to develop taxonomies and benchmarks specifically designed to capture smell-related information. In this work, we further extend this research line by presenting a supervised system for olfactory information extraction in English. We cast this problem as a token classification task and build a system that identifies smell words, smell sources and qualities. The classifier is then applied to a set of English historical corpora, covering different domains and written in a time period between the 15th and the 20th Century. A qualitative analysis of the extracted data shows that they can be used to infer interesting information about smelly items such as tea and tobacco from a diachronical perspective, supporting historical investigation with corpus-based evidence.",
}
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<abstract>Olfaction is a rather understudied sense compared to the other senses. In NLP, however, there have been recent attempts to develop taxonomies and benchmarks specifically designed to capture smell-related information. In this work, we further extend this research line by presenting a supervised system for olfactory information extraction in English. We cast this problem as a token classification task and build a system that identifies smell words, smell sources and qualities. The classifier is then applied to a set of English historical corpora, covering different domains and written in a time period between the 15th and the 20th Century. A qualitative analysis of the extracted data shows that they can be used to infer interesting information about smelly items such as tea and tobacco from a diachronical perspective, supporting historical investigation with corpus-based evidence.</abstract>
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%0 Conference Proceedings
%T Scent Mining: Extracting Olfactory Events, Smell Sources and Qualities
%A Menini, Stefano
%A Paccosi, Teresa
%A Tekiroğlu, Serra Sinem
%A Tonelli, Sara
%Y Degaetano-Ortlieb, Stefania
%Y Kazantseva, Anna
%Y Reiter, Nils
%Y Szpakowicz, Stan
%S Proceedings of the 7th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F menini-etal-2023-scent
%X Olfaction is a rather understudied sense compared to the other senses. In NLP, however, there have been recent attempts to develop taxonomies and benchmarks specifically designed to capture smell-related information. In this work, we further extend this research line by presenting a supervised system for olfactory information extraction in English. We cast this problem as a token classification task and build a system that identifies smell words, smell sources and qualities. The classifier is then applied to a set of English historical corpora, covering different domains and written in a time period between the 15th and the 20th Century. A qualitative analysis of the extracted data shows that they can be used to infer interesting information about smelly items such as tea and tobacco from a diachronical perspective, supporting historical investigation with corpus-based evidence.
%R 10.18653/v1/2023.latechclfl-1.15
%U https://aclanthology.org/2023.latechclfl-1.15
%U https://doi.org/10.18653/v1/2023.latechclfl-1.15
%P 135-140
Markdown (Informal)
[Scent Mining: Extracting Olfactory Events, Smell Sources and Qualities](https://aclanthology.org/2023.latechclfl-1.15) (Menini et al., LaTeCHCLfL 2023)
ACL
- Stefano Menini, Teresa Paccosi, Serra Sinem Tekiroğlu, and Sara Tonelli. 2023. Scent Mining: Extracting Olfactory Events, Smell Sources and Qualities. In Proceedings of the 7th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, pages 135–140, Dubrovnik, Croatia. Association for Computational Linguistics.