EXPR at SemEval-2018 Task 9: A Combined Approach for Hypernym Discovery
Ahmad Issa Alaa Aldine | Mounira Harzallah | Giuseppe Berio | Nicolas Béchet | Ahmad Faour
Proceedings of The 12th International Workshop on Semantic Evaluation
In this paper, we present our proposed system (EXPR) to participate in the hypernym discovery task of SemEval 2018. The task addresses the challenge of discovering hypernym relations from a text corpus. Our proposal is a combined approach of path-based technique and distributional technique. We use dependency parser on a corpus to extract candidate hypernyms and represent their dependency paths as a feature vector. The feature vector is concatenated with a feature vector obtained using Wikipedia pre-trained term embedding model. The concatenated feature vector fits a supervised machine learning method to learn a classifier model. This model is able to classify new candidate hypernyms as hypernym or not. Our system performs well to discover new hypernyms not defined in gold hypernyms.