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Unsupervised Low-Dimensional Vector Representations for Words, Phrases and Text that are Transparent, Scalable, and produce Similarity Metrics that are not Redundant with Neural Embeddings
Neural embeddings are a popular set of methods for representing words, phrases or text as a low dimensional vector (typically 50–500 dimensions). However, it is difficult to interpret these dimensions in a meaningful manner, and creating neural embeddings requires extensive training and tuning of mu...
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| Published in: | J Biomed Inform |
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| Main Authors: | , , |
| Format: | Artigo |
| Language: | Inglês |
| Published: |
2019
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| Subjects: | |
| Online Access: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6557457/ https://ncbi.nlm.nih.gov/pubmed/30654030 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1016/j.jbi.2019.103096 |
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