Paper: Learning Semantic Hierarchies via Word Embeddings

ACL ID P14-1113
Title Learning Semantic Hierarchies via Word Embeddings
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 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 effec- tive method for the construction of se- mantic hierarchies based on word em- beddings, which can be used to mea- sure the semantic relationship between words. We identify whether a candidate word pair has hypernym?hyponym rela- tion 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 previous manually-built hierarchy extension method can further improve F- score to 80.29%.