.png){kind=icon}
.png){kind=icon}
Return
Learning Semantic Hierarchies: A Continuous Vector Space Approach
.png){kind=icon}
OA
AI
DOI：10.1109/TASLP.2014.2377580.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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 continuous vector representation of words, named 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%.
Keywords:
Piecewise linear projections
semantic hierarchy
word embedding
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
I
IF:
5.1
Papers:
2.6K
Citations:
1.1W