Taxonomic relation identification aims to recognize the 'is-a' relation between two terms. Previous works on identifying taxonomic relations are mostly based on statistical and linguistic approaches, but the accuracy of these approaches is far from satisfactory. In this paper, we propose a novel supervised learning approach for identifying taxonomic relations using term embeddings. For this purpose, we first design a dynamic weighting neural network to learn term embeddings based on not only the hypernym and hyponym terms, but also the contextual information between them. We then apply such embeddings as features to identify taxonomic relations using a supervised method. The experimental results show that our proposed approach significantly outperforms other state-of-the-art methods by 9% to 13% in terms of accuracy for both general and specific domain datasets.
Tuan et al. REF further proposed a dynamic weighting neural model to learn term embeddings.
Learning Term Embeddings for Taxonomic Relation Identification Using Dynamic Weighting Neural Network
EMNLP
Computer Science