Convolutional Neural Networks for Sentence Classification
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Cites background or methods from "Convolutional Neural Networks for S..."
...CNN-word Word based CNN models like that of (Kim, 2014) are used....
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...Although neural-network–based approaches to text classification have been quite effective (Kim, 2014; Zhang et al., 2015; Johnson and Zhang, 2014; Tang et al., 2015), in this paper we test the hypothesis that better representations can be obtained by incorporating knowledge of document structure in the model architecture....
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References
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"Convolutional Neural Networks for S..." refers background or methods in this paper
...It is not clear whether this is due to Mikolov et al. (2013)’s architecture or the 100 billion word Google News dataset....
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...These vectors were trained by Mikolov et al. (2013) on 100 billion words of Google News, and are publicly available.1 We initially keep the word vectors static and learn only the other parameters of the model....
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...…much of the work with deep learning methods has involved learning word vector representations through neural language models (Bengio et al., 2003; Yih et al., 2011; Mikolov et al., 2013) and performing composition over the learned word vectors for classification (Collobert et al., 2011)....
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...Within natural language processing, much of the work with deep learning methods has involved learning word vector representations through neural language models (Bengio et al., 2003; Yih et al., 2011; Mikolov et al., 2013) and performing composition over the learned word vectors for classification (Collobert et al....
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...The vectors have dimensionality of 300 and were trained using the continuous bag-of-words architecture (Mikolov et al., 2013)....
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7,330 citations
Additional excerpts
...Task is to predict positive/negative reviews (Hu and Liu, 2004)....
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7,316 citations
"Convolutional Neural Networks for S..." refers background in this paper
..., 2012) and speech recognition (Graves et al., 2013) in recent years....
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...Deep learning models have achieved remarkable results in computer vision (Krizhevsky et al., 2012) and speech recognition (Graves et al., 2013) in recent years....
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