TY - CONF A1 - Venkateswaran, Siddarth A1 - Al Foysal, Abdullah A1 - Shaik, Nazeer Basha A1 - Böck, Ronald A2 - Baumann, Timo T1 - Is there Text in Wine? – S+U Learning-based Named Entity Recognition and Triplet Extraction from Wine Aroma Descriptors T2 - Elektronische Sprachsignalverarbeitung 2024, Tagungsband der 35. Konferenz, Regensburg, 6.-8. März 2024 N2 - Wine making is usually considered a domain being far off the processing of speech and language. But in a particular aspect, the domains of speech processing and wine making are related, namely, in the description of wine aromas. These descriptors are used for creating wine expertise as well as more general (advertisement-like) textual representations. In the current paper, we use Natural Language Processing techniques, especially Named Entity Recognition, to identify Aspects and Opinions, reflecting wine characteristics. These are combined with analyses of respective relations (triplet extraction) building Aspect-Opinion-Pairs to establish indicative aroma descriptors, also trying to approach the complex interplay amongst these individual statements. In our experiments, we rely on the Falstaff corpus comprising a huge set of wine descriptions. This results in an average F1 score of around 0.85 for Aspect-Opinion classification. For triplet generation multiple strategies were compared, resulting in an average F1 score of 0.67 in this challenging task. For both tasks we rely only on a handful of manually annotated samples, applying pseudo-labeling methods from seed data to achieve automatic labeling. Y1 - 2024 UR - https://opus4.kobv.de/opus4-oth-regensburg/frontdoor/index/index/docId/7093 UR - https://nbn-resolving.org/urn:nbn:de:bvb:898-opus4-70931 SN - 978-3-95908-325-6 SP - 157 EP - 164 PB - TUDpress CY - Dresden ER -