Encoding Syntactic Knowledge in Neural Networks for Sentiment Classification

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Learned in Translation: Contextualized Word Vectors (2017, neutral): “…et al., 2011] 89.0 DMN [Kumar et al., 2016] 52.1 SNoW [Li and Roth, 2006] 89.3 LSTM-CNN [Zhou et al., 2016] 52.4 BCN+Char+CoVe (Ours) 90.2 TE-LSTM [Huang et al., 2017] 52.6 RulesUHC [da Silva et al., 2011] 90.8 NTI [Munkhdalai and Yu, 2016b] 53 [Dai and Le, 2015] 92.8 NTI [Munkhdalai and Yu,…”
Deep learning for sentiment analysis: A survey (2018, background): “Huang et al. 60 proposed to encode the syntactic knowledge (e.g., part-of-speech tags) in a tree-structured LSTM to enhance phrase and sentence representation.”
SentiLARE: Linguistic Knowledge Enhanced Language Representation for Sentiment Analysis (2019, background): “For instance, existing work has shown that linguistic knowledge including part-of-speech tag (Qian et al., 2015; Huang et al., 2017) and word-level sentiment polarity (Qian et al., 2017) is closely related to the sentiment of longer texts.”
Bidirectional LSTM with attention mechanism and convolutional layer for text classification (2019, background): “Huang et al. [54] discovered that encoding syntactic knowledge (part-of-speech tag) in neural networks can enhance sentence/phrase representation.”