An Investigation on Word Embedding Offset Clustering as Relationship
  Classification
Didier Gohourou Kazuhiro Kuwabara
Vector representations obtained from word embedding are the source of many
groundbreaking advances in natural language processing. They yield word
representations that are capable of capturing semantics and analogies of words
within a text corpus. This study is an investigation in an attempt to elicit a
vector representation of relationships between pairs of word vectors. We use
six pooling strategies to represent vector relationships. Different types of
clustering models are applied to analyze which one correctly groups
relationship types. Subtraction pooling coupled with a centroid based
clustering mechanism shows better performances in our experimental setup. This
work aims to provide directions for a word embedding based unsupervised method
to identify the nature of a relationship represented by a pair of words.