TakeLab at SemEval-2018 Task 7: Combining Sparse and Dense Features for Relation Classification in Scientific Texts (CROSBI ID 702519)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Gluhak, Martin ; Pia di Buono, Maria ; Akkasi, Abbas ; Šnajder, Jan
engleski
TakeLab at SemEval-2018 Task 7: Combining Sparse and Dense Features for Relation Classification in Scientific Texts
We describe two systems for semantic relation classification with which we participated in the SemEval 2018 Task 7, subtask 1 on semantic relation classification: an SVM model and a CNN model. Both models combine dense pretrained word2vec features and hancrafted sparse features. For training the models, we combine the two datasets provided for the subtasks in order to balance the under-represented classes. The SVM model performed better than CNN, achieving a F1-macro score of 69.98% on subtask 1.1 and 75.69% on subtask 1.2. The system ranked 7th on among 28 submissions on subtask 1.1 and 7th among 20 submissions on subtask 1.2.
semantic relation classification ; machine learning
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Podaci o prilogu
842-847.
2018.
objavljeno
Podaci o matičnoj publikaciji
Podaci o skupu
The 12th International Workshop on Semantic Evaluation
predavanje
05.06.2018-06.06.2018
New Orleans (LA), Sjedinjene Američke Države