UnibucKernel: Geolocating Swiss-German Jodels Using Ensemble Learning
- URL: http://arxiv.org/abs/2102.09379v2
- Date: Fri, 19 Feb 2021 08:31:31 GMT
- Title: UnibucKernel: Geolocating Swiss-German Jodels Using Ensemble Learning
- Authors: Mihaela Gaman, Sebastian Cojocariu, Radu Tudor Ionescu
- Abstract summary: We focus on the second subtask, which is based on a data set formed of approximately 30 thousand Swiss German Jodels.
The dialect identification task is about accurately predicting the latitude and longitude of test samples.
We frame the task as a double regression problem, employing an XGBoost meta-learner with the combined power of a variety of machine learning approaches.
- Score: 15.877673959068455
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this work, we describe our approach addressing the Social Media Variety
Geolocation task featured in the 2021 VarDial Evaluation Campaign. We focus on
the second subtask, which is based on a data set formed of approximately 30
thousand Swiss German Jodels. The dialect identification task is about
accurately predicting the latitude and longitude of test samples. We frame the
task as a double regression problem, employing an XGBoost meta-learner with the
combined power of a variety of machine learning approaches to predict both
latitude and longitude. The models included in our ensemble range from simple
regression techniques, such as Support Vector Regression, to deep neural
models, such as a hybrid neural network and a neural transformer. To minimize
the prediction error, we approach the problem from a few different perspectives
and consider various types of features, from low-level character n-grams to
high-level BERT embeddings. The XGBoost ensemble resulted from combining the
power of the aforementioned methods achieves a median distance of 23.6 km on
the test data, which places us on the third place in the ranking, at a
difference of 6.05 km and 2.9 km from the submissions on the first and second
places, respectively.
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