Learning Semantic Hierarchies Via Word Embeddings
PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1(2014)
摘要
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-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%.
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