@InProceedings{Venkateswaran2024_1218,
<br/>   author = {Siddarth Venkateswaran and Abdullah Al Foysal and Nazeer Basha Shaik and Ronald Böck},
<br/>   booktitle = {Studientexte zur Sprachkommunikation: Elektronische Sprachsignalverarbeitung 2024},
<br/>   title = {Is there Text in Wine? - S+U Learning-Based Named Entity Recognition and Triplet Extraction from Wine Aroma Descriptors},
<br/>   year = {2024},
<br/>   editor = {Timo Baumann},
<br/>   month = mar,
<br/>   pages = {157--164},
<br/>   publisher = {TUDpress, Dresden},
<br/>   abstract  = {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.},
<br/>   isbn = {978-3-95908-325-6},
<br/>   issn = {0940-6832},
<br/>   keywords = {Large Language Models},
<br/>   url = {https://www.essv.de/pdf/2024_157_164.pdf},
<br/>   doi = {10.35096/othr/pub-7093},
<br/>}