カンファレンス (国際) On Approximately Searching for Similar Word Embeddings
the annual meeting of the Association for Computational Linguistics (ACL2016)
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 graph- based 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.
Paper : On Approximately Searching for Similar Word Embeddings （外部サイト）