Zobrazeno 1 - 7
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pro vyhledávání: '"Melamud, Oren"'
Autor:
Melamud, Oren, Shivade, Chaitanya
Large-scale clinical data is invaluable to driving many computational scientific advances today. However, understandable concerns regarding patient privacy hinder the open dissemination of such data and give rise to suboptimal siloed research. De-ide
Externí odkaz:
http://arxiv.org/abs/1905.07002
Autor:
Goldberger, Jacob, Melamud, Oren
Self-normalizing discriminative models approximate the normalized probability of a class without having to compute the partition function. In the context of language modeling, this property is particularly appealing as it may significantly reduce run
Externí odkaz:
http://arxiv.org/abs/1806.00913
In this study, we introduce a new approach for learning language models by training them to estimate word-context pointwise mutual information (PMI), and then deriving the desired conditional probabilities from PMI at test time. Specifically, we show
Externí odkaz:
http://arxiv.org/abs/1707.05266
The negative sampling (NEG) objective function, used in word2vec, is a simplification of the Noise Contrastive Estimation (NCE) method. NEG was found to be highly effective in learning continuous word representations. However, unlike NCE, it was cons
Externí odkaz:
http://arxiv.org/abs/1609.01235
We provide the first extensive evaluation of how using different types of context to learn skip-gram word embeddings affects performance on a wide range of intrinsic and extrinsic NLP tasks. Our results suggest that while intrinsic tasks tend to exhi
Externí odkaz:
http://arxiv.org/abs/1601.00893
Akademický článek
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Publikováno v:
Proceedings of the Sixteenth Annual ACM-SIAM Symposium: Discrete Algorithms; 1/23/2005, p339-348, 10p
