Annotations for Exploring Food Tweets From Multiple Aspects
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917862747144192 |
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| author | Rikters, Matīss Marrese-Taylor, Edison Vīksna, Rinalds |
| author_facet | Rikters, Matīss Marrese-Taylor, Edison Vīksna, Rinalds |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2412_06179 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Annotations for Exploring Food Tweets From Multiple Aspects Rikters, Matīss Marrese-Taylor, Edison Vīksna, Rinalds Computation and Language Artificial Intelligence 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. |
| title | Annotations for Exploring Food Tweets From Multiple Aspects |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2412.06179 |