TakeLab at SemEval-2018 Task 7: Combining Sparse and Dense Features for Relation Classification in Scientific Texts

Martin Gluhak, Maria Pia di Buono, Abbas Akkasi, Jan Šnajder


Abstract
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.
Anthology ID:
S18-1135
Volume:
Proceedings of The 12th International Workshop on Semantic Evaluation
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
842–847
Language:
URL:
https://www.aclweb.org/anthology/S18-1135
DOI:
10.18653/v1/S18-1135
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PDF:
http://aclanthology.lst.uni-saarland.de/S18-1135.pdf