2016/06/24 by Jeroen B. P. Vuurens, Vuurens, Jeroen B. P., Carsten Eickhoff +3
Computer Science · #Computation and Language (cs.CL) #Distributed #FOS: Computer and information sciences #Natural Language Processing Techniques #Parallel #Text and Document Classification Technologies #Topic Modeling #and Cluster Computing (cs.DC) #cs.CL #cs.DC
paper · pdf · doi:10.48550/arxiv.1606.07822
ICML 2016 Machine Learning workshop
arxiv created 2016/06/24 · openalex publication_date 2016/06/24 · arxiv updated 2016/06/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Since its introduction, Word2Vec and its variants are widely used to learn semantics-preserving representations of words or entities in an embedding space, which can be used to produce state-of-art results for various Natural Language Processing tasks. Existing implementations aim to learn efficiently by running multiple threads in parallel while operating on a single model in shared memory, ignoring incidental memory update collisions. We show that these collisions can degrade the efficiency of parallel learning, and propose a straightforward caching strategy that improves the efficiency by a factor of 4.