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Found 2 papers, 1 papers with code
Recurrent Neural Network-Based Semantic Variational Autoencoder for Sequence-to-Sequence Learning
In this paper, we propose a new recurrent neural network (RNN)-based Seq2seq model, RNN semantic variational autoencoder (RNN--SVAE), to better capture the global latent information of a sequence of words.
Sentiment Classification with Word Attention based on Weakly Supervised Learning with a Convolutional Neural Network
In order to maximize the applicability of sentiment analysis results, it is necessary to not only classify the overall sentiment (positive/negative) of a given document but also to identify the main words that contribute to the classification.
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