Unsupervised Sense-Aware Hypernymy Extraction
This work addresses hypernymy extraction for natural language processing, offering a novel method that is incremental over existing techniques.
The paper tackled the problem of hypernymy extraction by using unsupervised sense representations to improve recognition of disambiguated hypernymy relationships, achieving success on English and Russian datasets where standard methods failed.
In this paper, we show how unsupervised sense representations can be used to improve hypernymy extraction. We present a method for extracting disambiguated hypernymy relationships that propagates hypernyms to sets of synonyms (synsets), constructs embeddings for these sets, and establishes sense-aware relationships between matching synsets. Evaluation on two gold standard datasets for English and Russian shows that the method successfully recognizes hypernymy relationships that cannot be found with standard Hearst patterns and Wiktionary datasets for the respective languages.