Learning Semantic Hierarchies via Word Embeddings

Ruiji Fu, Jiang Guo, Bing Qin, Wanxiang Che, Haifeng Wang, Ting Liu

Abstract: Semantic hierarchy construction aims to build structures of concepts linked by hypernym‐hyponym (“is-a”) relations. A major challenge for this task is the automatic discovery of such relations. This paper proposes a novel and effective method for the construction of semantic hierarchies based on word embeddings, which can be used to measure the semantic relationship between words. We identify whether a candidate word pair has hypernym‐hyponym relation by using the word-embedding-based semantic projections between words and their hypernyms. Our result, an F-score of 73.74%, outperforms the state-of-theart methods on a manually labeled test dataset. Moreover, combining our method with a previous manually-built hierarchy extension method can further improve Fscore to 80.29%.