Abstract:Word embeddings are used as building blocks for a wide range of natural language processing and information retrieval tasks. These embeddings are usually represented as continuous vectors, requiring significant memory capacity and computationally expensive similarity measures. In this study, we introduce a novel method for semantic hashing continuous vector representations into lowerdimensional Hamming space while explicitly preserving semantic information between words. This is achieved by introducing a Siame… Show more
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