Learning Semantic Hierarchies: A Continuous Vector Space Approach

Abstract—Semantic hierarchy construction aims to build struc-tures 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 continuous vector representation of words, named word embeddings, which can be used tomeasure the semantic relationship betweenwords.We iden-tify whether a candidate word pair has hypernym–hyponym re-lation 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-the-art methods on a manually labeled test dataset. Moreover, combining our method with a pre-vious manually built hierarchy extension method can further im-prove F-score to 80.29%. Index Terms—Piecewise linear projections, semantic hierarchy, word embedding. I