7 research outputs found
UG18 at SemEval-2018 Task 1: Generating Additional Training Data for Predicting Emotion Intensity in Spanish
The present study describes our submission to SemEval 2018 Task 1: Affect in
Tweets. Our Spanish-only approach aimed to demonstrate that it is beneficial to
automatically generate additional training data by (i) translating training
data from other languages and (ii) applying a semi-supervised learning method.
We find strong support for both approaches, with those models outperforming our
regular models in all subtasks. However, creating a stepwise ensemble of
different models as opposed to simply averaging did not result in an increase
in performance. We placed second (EI-Reg), second (EI-Oc), fourth (V-Reg) and
fifth (V-Oc) in the four Spanish subtasks we participated in.Comment: Accepted at SemEval 201
UG18 at SemEval-2018 Task 1:Generating Additional Training Data for Predicting Emotion Intensity in Spanish
UG18 at SemEval-2018 Task 1:Generating Additional Training Data for Predicting Emotion Intensity in Spanish
UG18 at SemEval-2018 Task 1:Generating Additional Training Data for Predicting Emotion Intensity in Spanish
UG18 at SemEval-2018 Task 1: Generating Additional Training Data for Predicting Emotion Intensity in Spanish
The present study describes our submission to SemEval 2018 Task 1: Affect in Tweets. Our Spanish-only approach aimed to demonstrate that it is beneficial to automatically generate additional training data by (i) translating training data from other languages and (ii) applying a semi-supervised learning method. We find strong support for both approaches, with those models outperforming our regular models in all subtasks. However, creating a stepwise ensemble of different models as opposed to simply averaging did not result in an increase in performance. We placed second (EI-Reg), second (EI-Oc), fourth (V-Reg) and fifth (V-Oc) in the four Spanish subtasks we participated in
Carrier Wave Signals Interfering with Loran-C
Electrical Engineering, Mathematics and Computer Scienc