Deep Multi-Task Learning for Aspect Term Extraction with Memory Interaction
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Cites background or methods from "Deep Multi-Task Learning for Aspect..."
...In recent DNN models, word embeddings are typically pre-trained but not task-specific data so that the learning word vectors can capture general syntactical and semantic information (T. Chen, Xu, He, & Wang, 2017; P. Liu et al., 2015; Poria, Cambria, et al., 2016)....
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...An example comes from Xu, Liu, Wang, & Yin, (2018) who attempted to approach three ABSA tasks with CNN models but achieved lower outcomes than with the SVM approach. Yuan et al. (2017) found that a purely window-based neural network produces outcomes that are comparable to an LSTM-RNN approach, and concluded that local context rather than longterm dependencies were important for aspect extraction....
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...Sentiment polarity 3 Tang, Qin, Feng, & Liu (2015) Twitter data Dong et al....
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...The most extensively used classifiers in recent years include Support Vector Machine (SVM) and Conditional Random Fields (CRF) classifiers, with examples in ABSA tasks as CRF in T. Chen et al. (2017), Xu, Lin, Wang, Yin and Wang (2017), Mai & Le (2018), or SMV in Akhtar, Kumar, Ekbal and Bhattacharyya (2016), and Dong et al. (2014)....
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...2 Li and Lam (2017) English Memory Interaction Network (MIN) based on LSTM with extended memory 73....
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Cites methods from "Deep Multi-Task Learning for Aspect..."
...This co-extraction strategy can also be adopted in neural networks with multi-task learning (Wang et al., 2016, 2017; Li and Lam, 2017)....
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References
111,197 citations
"Deep Multi-Task Learning for Aspect..." refers background in this paper
...Liu et al. (2014) modeled relation between aspects and opinions by constructing a bipartite heterogenous graph....
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24,012 citations
"Deep Multi-Task Learning for Aspect..." refers methods in this paper
...For datasets in the restaurant domain, we train word embeddings of dimension 200 with word2vec (Mikolov et al., 2013) on Yelp reviews5....
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