| 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. |
| Outcome: | The proposed method provides meaningful explanations on prediction results and also uncovers the so-called "black-box" GNN-based models by investigating the effect of individual nodes. |
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A Hierarchical Location Prediction Neural Network for Twitter User Geolocation (D19-1)
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| Challenge: | Existing methods to estimate user location ignore hierarchical structure among locations. |
| Approach: | They propose a hierarchical location prediction neural network for Twitter user geolocation that first predicts the home country for a user, then uses the country result to guide the city-level prediction. |
| Outcome: | The proposed model can achieve state-of-the-art results over three common benchmarks under different feature settings and greatly reduces the mean error distance. |
Explaining Black Box Predictions and Unveiling Data Artifacts through Influence Functions (2020.acl-main)
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| Challenge: | Modern deep learning models for NLP are notoriously opaque, and this has motivated efforts to design example-specific approaches to interpret such models. |
| Approach: | They propose to use influence functions to explain models by highlighting important words in input text to provide models with an explanation. |
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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. |
| Approach: | They propose a sequence-to-sequence (seq2sequ) model that rewrites noisy location strings into structured English location names. |
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Geolocation with Attention-Based Multitask Learning Models (D19-55)
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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 . |
| 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. |
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. |
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| Outcome: | The proposed methods improve performance over previous sparse graph representations. |
Where are you from? Geolocating Speech and Applications to Language Identification (2024.naacl-long)
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Patrick Foley, Matthew Wiesner, Bismarck Odoom, Leibny Paola Garcia Perera, Kenton Murray, Philipp Koehn
| Challenge: | Language identification (LID) is a critical component in many modern multilingual speech technologies. |
| Approach: | They propose to use radio broadcasts with known origin to train regression models . they also propose to explore using geolocation as a proxy task for LID . |
| Outcome: | The proposed model outperforms pretrained models on the FLEURS benchmark and on the VoxLingua benchmark. |
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. |
| Approach: | They propose a model that captures the sociological phenomenon of homophily and combines it with linguistic information to make a prediction. |
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Mining Tweets that refer to TV programs with Deep Neural Networks (D19-55)
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| Challenge: | opinion mining is a popular natural language processing technique, but a problem is robustness for user-generated texts . a recent study shows that a model that handles context can extract the opinion target with 90% accuracy . |
| Approach: | They propose a model that handles context in many natural language processing areas to solve a problem of extracting opinion references from text. |
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Are People Located in the Places They Mention in Their Tweets? A Multimodal Approach (2022.coling-1)
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| Challenge: | Experimental results show that a neural architecture that combines both modalities yields better results. |
| Approach: | They propose a neural architecture that combines both modalities to solve the problem of determining whether people are located in tweets. |
| Outcome: | The proposed model combines both modalities to produce better results . |
Are We Ready for this Disaster? Towards Location Mention Recognition from Crisis Tweets (2020.coling-main)
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| Challenge: | Despite the widespread use of Twitter during emergencies, the majority of tweets do not have geoinformation. |
| Approach: | They propose to use Twitter to train location mention recognition models using different training settings. |
| Outcome: | The results show that training on near or far-away events boosts the performance compared to training on distant events. |