An attention-based Bi-GRU-CapsNet model for hypernymy detection between compound entities

by   Qi Wang, et al.

Named entities which composed of multiple continuous words frequently occur in domain-specific knowledge graphs. These entities are usually composable and extensible. Typical examples are names of symptoms and diseases in medical areas. To distinguish these entities from general words, we name them compound entities. Hypernymy detection between compound entities plays an important role in domain-specific knowledge graph construction. Traditional hypernymy detection approaches cannot perform well on compound entities due to the lack of contextual information in texts, and even the absence of compound entities in training sets, i.e. Out-Of-Vocabulary (OOV) problem. In this paper, we present a novel attention-based Bi-GRU-CapsNet model to detect hypernymy relationship between compound entities. Our model integrates several important components. English words or Chinese characters in compound entities are fed into Bidirectional Recurrent Units (Bi-GRUs) to avoid the OOV problem. An attention mechanism is then designed to focus on the differences between two compound entities. Since there are different cases in hypernymy relationship between compound entities, Capsule Network (CapsNet) is finally employed to decide whether the hypernymy relationship exists or not. Experimental results demonstrate the advantages of the proposed model over the state-of-the-art methods both on English and Chinese corpora of symptom and disease pairs.


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