We discuss an approximate similarity search for word embeddings, which is an operation to approximately find embeddings close to a given vector. We compared several metric-based search algorithms with hash-, tree-, and graphbased indexing from different aspects. Our experimental results showed that a graph-based indexing exhibits robust performance and additionally provided useful information, e.g., vector normalization achieves an efficient search with cosine similarity.
In an approximate similarity search task for neural word embeddings, Sugawara et al. REF compared hashbased, tree-based, and graph-based algorithms.
On Approximately Searching for Similar Word Embeddings
ACL
Computer Science