التفاصيل البيبلوغرافية
| العنوان: |
Annotations for Exploring Food Tweets From Multiple Aspects |
| المؤلفون: |
Rikters, Matīss, Marrese-Taylor, Edison, Vīksna, Rinalds |
| المصدر: |
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) |
| سنة النشر: |
2024 |
| المجموعة: |
Computer Science |
| مصطلحات موضوعية: |
Computer Science - Computation and Language, Computer Science - Artificial Intelligence |
| الوصف: |
This research builds upon the Latvian Twitter Eater Corpus (LTEC), which is focused on the narrow domain of tweets related to food, drinks, eating and drinking. LTEC has been collected for more than 12 years and reaching almost 3 million tweets with the basic information as well as extended automatically and manually annotated metadata. In this paper we supplement the LTEC with manually annotated subsets of evaluation data for machine translation, named entity recognition, timeline-balanced sentiment analysis, and text-image relation classification. We experiment with each of the data sets using baseline models and highlight future challenges for various modelling approaches. |
| نوع الوثيقة: |
Working Paper |
| URL الوصول: |
http://arxiv.org/abs/2412.06179 |
| رقم الانضمام: |
edsarx.2412.06179 |
| قاعدة البيانات: |
arXiv |