| 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. |
| Outcome: | The proposed methods improve performance over previous sparse graph representations. |
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| 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 . |
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Semi-supervised User Geolocation via Graph Convolutional Networks (P18-1)
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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. |
| Outcome: | The proposed model outperforms baseline models and the state-of-the-art models under minimal supervision. |
Learning from Sibling Mentions with Scalable Graph Inference in Fine-Grained Entity Typing (2022.acl-long)
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| Challenge: | Existing models struggle to handle hard mentions due to insufficient contexts, limiting their overall typing performance. |
| Approach: | They propose to exploit sibling mentions to enhance the mention representations by adding unseen test mentions as new nodes for inference. |
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You Shall Know a User by the Company It Keeps: Dynamic Representations for Social Media Users in NLP (D19-1)
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| Challenge: | Current approaches to social media modelling ignore the fact that an individual may be part of several communities which are not equally relevant in all communicative situations. |
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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. |
| Approach: | They adopt influence functions to interpret the behavior of GNN-based models by identifying the importance of training users when predicting locations. |
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Rethinking Complex Neural Network Architectures for Document Classification (N19-1)
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| Challenge: | Neural network models for many NLP tasks have grown increasingly complex in recent years . authors of recent papers question the necessity of such architectures and find them quite effective . |
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Learning Geolocations for Cold-Start and Hard-to-Resolve Addresses via Deep Metric Learning (2022.emnlp-industry)
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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. |
| Approach: | They propose a framework to resolve addresses to a shallower granularity termed neighbourhood . they propose 'deep metric learning' model to encode geospatial semantics in address embeddings . |
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Text Classification with Few Examples using Controlled Generalization (N19-1)
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Global Context-enhanced Graph Convolutional Networks for Document-level Relation Extraction (2020.coling-main)
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| Challenge: | Existing approaches to document-level relation extraction are difficult to establish direct connections between distant entity pairs. |
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Connecting the Dots: What Graph-Based Text Representations Work Best for Text Classification using Graph Neural Networks? (2023.findings-emnlp)
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| Challenge: | Graph Neural Networks have been used for text classification, but only in domains with limited data characteristics. |
| Approach: | They compare graph representation methods for text classification using different architectures and setups. |
| Outcome: | The proposed graph representation methods outperform other models in document comprehension tasks. |