Paper: Looking for Hyponyms in Vector Space

ACL ID W14-1608
Title Looking for Hyponyms in Vector Space
Venue International Conference on Computational Natural Language Learning
Year 2014

The task of detecting and generating hy- ponyms is at the core of semantic under- standing of language, and has numerous practical applications. We investigate how neural network embeddings perform on this task, compared to dependency-based vector space models, and evaluate a range of similarity measures on hyponym gener- ation. A new asymmetric similarity mea- sure and a combination approach are de- scribed, both of which significantly im- prove precision. We release three new datasets of lexical vector representations trained on the BNC and our evaluation dataset for hyponym generation.