ROPE: Recoverable Order-Preserving Embedding of Natural Language
We present a novel Recoverable Order-Preserving Embedding (ROPE) of natural language. ROPE maps natural language passages from sparse concatenated one-hot representations to distributed vector representations of predetermined fixed length. We use Euclidean distance to return search results that are both grammatically and semantically similar. ROPE is based on a series of random projections of distributed word embeddings. We show that our technique typically forms a dictionary with sufficient incoherence such that sparse recovery of the original text is possible. We then show how our embedding allows for efficient and meaningful natural search and retrieval on Microsoft’s COCO dataset and the IMDB Movie Review dataset.
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- Technical Report
- Research Org:
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
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- Country of Publication:
- United States
- 97 MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
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