Learning Semantic Hierarchies via Word Embeddings
Ruiji Fu, Jiang Hong Guo, Bing Qin, Wanxiang Che, Haifeng Wang, Ting Liu · 2014
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%.