Abstract
This paper introduces the problem of determining whether people are located in the places they mention in their tweets. In particular, we investigate the role of text and images to solve this challenging problem. We present a new corpus of tweets that contain both text and images. Our analyses show that this problem is multimodal at its core: human judgments depend on whether annotators have access to the text, the image, or both. Experimental results show that a neural architecture that combines both modalities yields better results. We also conduct an error analysis to provide insights into why and when each modality is beneficial.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 2561-2571 |
| Number of pages | 11 |
| Journal | Proceedings - International Conference on Computational Linguistics, COLING |
| Volume | 29 |
| Issue number | 1 |
| State | Published - 2022 |
| Externally published | Yes |
| Event | 29th International Conference on Computational Linguistics, COLING 2022 - Gyeongju, Korea, Republic of Duration: Oct 12 2022 → Oct 17 2022 |
ASJC Scopus subject areas
- Computational Theory and Mathematics
- Computer Science Applications
- Theoretical Computer Science
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