| Challenge: | predicting the location of a social media post requires discretization of the coordinates, but results in poor performance. |
| Approach: | They propose to combine two approaches to predict location using supervised models . they evaluate a multitask convolutional neural network that predicts both discrete locations and continuous coordinates . |
| Outcome: | The proposed model outperforms singletask models and prior work on one dataset and shows that correlation between labels and coordinates has a marked impact on the effectiveness of a regression task. |
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| Challenge: | Existing methods to estimate user location ignore hierarchical structure among locations. |
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Dense Node Representation for Geolocation (D19-55)
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| Challenge: | Existing methods for geolocation use sparse adjacency matrices of connections, which grow exponentially with the number of users. |
| Approach: | They propose two methods to learn continuous node representations from social media posts and textual user mentions. |
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| Challenge: | Social media user geolocation is vital to many applications such as event detection. |
| Approach: | They propose a multiview geolocation model that uses both text and network context. |
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Multi-task Learning to Enable Location Mention Identification in the Early Hours of a Crisis Event (2021.findings-emnlp)
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| Challenge: | Social media is a platform for people to share their concerns and report information as eyewitnesses of events. |
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Interpreting Twitter User Geolocation (2020.acl-main)
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| Challenge: | Existing methods for identifying user geolocation suffer from a lack of interpretability on the corresponding results. |
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| Challenge: | a new task uses explicit knowledge from human-written guidebooks to improve geolocation accuracy . a state-of-the-art image-only method is unable to predict the location of an image . |
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Geo-Seq2seq: Twitter User Geolocation on Noisy Data through Sequence to Sequence Learning (2023.findings-acl)
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| Challenge: | a new method for Twitter user geolocation rewrites noisy, multilingual location strings into structured English location names. |
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Multi-Task Learning for Sequence Tagging: An Empirical Study (C18-1)
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| Outcome: | The proposed model achieves state-of-the-art performance on multiple datasets. |
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| Challenge: | Existing systems for learning geolocation fail to cater to a significant fraction of addresses which are new in the system and have inaccurate or missing building level information. |
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