Word embeddings are used with success for a variety of tasks involving lexical semantic similarities between individual words. Using unsupervised methods and just cosine similarity, encouraging results were obtained for analogical similarities. In this paper, we explore the potential of pre-trained word embeddings to identify generic types of semantic relations in an unsupervised experiment. We propose a new relational similarity measure based on the combination of word2vec's CBOW input and output vectors which outperforms alternative vector representations, when used for unsupervised clustering on SemEval 2010 Relation Classification data.
REF explore vector spaces for semantic relations, using unsupervised clustering.
Exploring Vector Spaces for Semantic Relations
EMNLP
Computer Science