Mar 21, 2016 · In this paper, we propose a scalable Bayesian neural word embedding algorithm. The algorithm relies on a Variational Bayes solution for the Skip ...
In this paper, we propose a scala- ble Bayesian neural word embedding algorithm. The algo- rithm relies on a Variational Bayes solution for the Skip-. Gram ...
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Experimental results demonstrate the performance of the proposed scalable Bayesian neural word embedding algorithm for word analogy and similarity tasks on ...
Modern neural word embedding models learn a low ... Bayesian word embeddings (Rudolph et al., 2016;. Barkan, 2017 ... Bayesian Word Embedding model, with context.
In this paper, we propose a scalable Bayesian neural word embedding algorithm. The algorithm relies on a Variational Bayes solution for the Skip-Gram objective ...
Apr 24, 2022 · A new method for computational cultural cartography: From neural word embeddings to transformers and Bayesian mixture models. Can Rev Sociol.
Feb 4, 2017 · In this paper, we propose a scalable Bayesian neural word embedding algorithm. The algorithm relies on a Variational Bayes solution for the Skip ...
Conventional word embedding models do not leverage information from document meta- data, and they do not model uncertainty. We address these concerns with a ...
Jan 7, 2024 · Multiple measures, such as WEAT or MAC, attempt to quantify the magnitude of bias present in word embeddings in terms of a single-number ...
The word embedding models have achieved state-of-the-art results in a vari- ety of natural language processing tasks. Whereas, current word embedding.