Efficient Analogy Completion with Word Embedding Clusters
Lance De Vine, Shlomo Geva, Peter Bruza · 2017
Word embeddings have attracted much attention in recent years and have been heavily applied to many tasks in information retrieval, natural language processing and knowledge base construction. One of the most well noted aspects of word embeddings is their ability to capture relations between terms via simple vector offsets. This ability is often examined via the use of proportional analogy completion tasks. This task requires that the correct single term be returned by a system when prompted with the three other terms of a proportional analogy. This task usually involves a scan of all stored word embeddings which may be a relatively expensive operation when used as part of a larger system.