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- W2892046289 abstract "Conventional word embedding models do not leverage information from document meta-data, and they do not model uncertainty. We address these concerns with a model that incorporates document covariates to estimate conditional word embedding distributions. Our model allows for (a) hypothesis tests about the meanings of terms, (b) assessments as to whether a word is near or far from another conditioned on different covariate values, and (c) assessments as to whether estimated differences are statistically significant." @default.
- W2892046289 created "2018-09-27" @default.
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- W2892046289 date "2018-01-01" @default.
- W2892046289 modified "2023-10-11" @default.
- W2892046289 title "Conditional Word Embedding and Hypothesis Testing via Bayes-by-Backprop" @default.
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- W2892046289 doi "https://doi.org/10.18653/v1/d18-1527" @default.
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