Constructing Semantic Hierarchies via Fusion Learning Architecture.

Semantic hierarchies construction means to build structure of concepts linked by hypernym-hyponym (“is-a”) relations. A major challenge for this task is the automatic discovery of hypernym-hyponym (“is-a”) relations. We propose a fusion learning architecture based on word embeddings for constructing semantic hierarchies, composed of discriminative generative fusion architecture and a very simple lexical structure rule for assisting, getting an F1-score of 74.20% with 91.60% precision-value, outperforming the state-of-the-art methods on a manually labeled test dataset. Subsequently, combining our method with manually-built hierarchies can further improve F1-score to 82.01%. Besides, the fusion learning architecture is language-independent.