Identifying the hypernym relations that hold between words is a fundamental task in NLP. Word embedding methods have recently shown some capability to encode hypernymy. However, such methods tend not to explicitly encode the hypernym hierarchy that exists between words. In this paper, we propose a method to learn a hierarchical word embedding in a speciﬁc order to capture the hypernymy. To learn the word embeddings, the proposed method considers not only the hypernym relations that exists between words on a taxonomy, but also their contextual information in a large text corpus. The experimental results on a supervised hypernymy detection and a newly-proposed hierarchical path completion tasks show the ability of the proposed method to encode the hierarchy. Moreover, the proposed method outperforms previously proposed methods for learning word and hypernym-speciﬁc word embeddings on multiple benchmarks.
We presented a method to jointly learn a Hierarchical Word Embedding (HWE) using a corpus and a taxonomy for identifying the hypernymy relations between words. The paper presents a method to jointly learn word embeddings using co-occurrence statistics as well as incorporating hierarchal information from semantic networks. This paper proposed a joint learning method of hypernym from both raw text and supervised taxonomy data.  This paper proposes adding a measure of "distributional inclusion" difference to the GloVE objective for the purpose of representing hypernym relations.