TakeLab at SemEval-2018 Task 7: Combining Sparse and Dense Features for Relation Classification in Scientific Texts
Autor: | Abbas Akkasi, Jan Šnajder, Martin Gluhak, Maria Pia di Buono |
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Jazyk: | angličtina |
Rok vydání: | 2018 |
Předmět: |
Computer science
business.industry semantic relation classification machine learning 02 engineering and technology 010501 environmental sciences computer.software_genre 01 natural sciences SemEval Task (project management) Support vector machine Relation classification Ranking 020204 information systems 0202 electrical engineering electronic engineering information engineering Word2vec Artificial intelligence business computer Natural language processing 0105 earth and related environmental sciences |
Zdroj: | SemEval@NAACL-HLT |
Popis: | 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. |
Databáze: | OpenAIRE |
Externí odkaz: |