Convolution Neural Network for Relation Extraction

By Chunyang Liu, Wenbo Sun, Wen-Han Chao, Wanxiang Che. 2013

172 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Continual Few-shot Relation Learning via Embedding Space Regularization and Data Augmentation (2022, methodology): “…to existing116 ones and analyse our results thoroughly.117 2 Related Work118 Conventional RE methods include supervised (Ze-119 lenko et al., 2002; Liu et al., 2013; Zeng et al., 2014;120 Miwa and Bansal, 2016), semi-supervised (Chen121 et al., 2006; Sun et al., 2011; Hu et al., 2020) and122…”
Curriculum-Meta Learning for Order-Robust Continual Relation Extraction (2021, methodology): “The conventional relation extraction methods could be categorized into three domains by the way data is used: supervised methods (Zelenko, Aone, and Richardella 2002; Liu et al. 2013; Zeng et al. 2014; Lin et al. 2016; Miwa and Bansal 2016), semi-supervised methods (Chen et al.”
Continual Relation Learning via Episodic Memory Activation and Reconsolidation (2020, methodology): “The conventional RE work, including both supervised RE models (Zelenko et al., 2003; Zhou et al., 2005; Gormley et al., 2015; Socher et al., 2012; Liu et al., 2013; Zeng et al., 2014; Nguyen and Grishman, 2015; dos Santos et al., 2015; Xu et al., 2015; Liu et al., 2015; Miwa and Bansal, 2016) and…”
Neural Snowball for Few-Shot Relation Learning (2019, methodology): “Recently, neural models like RNN and CNN have been proposed to extract better features from word sequences (Socher et al., 2012; Liu et al., 2013; Zeng et al., 2014; Nguyen and Grishman, 2015; dos Santos et al., 2015).”
Relation Extraction: Perspective from Convolutional Neural Networks (2015, methodology): “Compared to the models in Liu et al. (2013) and Zeng et al. (2014) for relation classiﬁcation that apply a single window size, our model for relation extraction incorporates various window sizes for convolutional ﬁlters, allowing the network to capture wider ranges of n -grams to be helpful for…”
More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction (2020, background): “, 2012; Miwa and Bansal, 2016) that learn compositional representations for sentences recursively, convolutional neural networks (CNNs) (Liu et al., 2013; Zeng et al., 2014; Santos et al., 2015; Nguyen and Grishman, 2015b; Zeng et al., 2015; Huang and Wang, 2017) that effectively model local textual”
Quantifying Similarity between Relations with Fact Distribution (2019, background): “…and Hovy, 2002; Banko et al., 2007; Zhu et al., 2009; Etzioni et al., 2011; Saha et al., 2017) and relation extraction (Riedel et al., 2013; Liu et al., 2013; Zeng et al., 2014; Santos et al., 2015; Zeng et al., 2015; Lin et al., 2016), and relation prediction (Bordes et al., 2013; Wang et…”
Prototypical Representation Learning for Relation Extraction (2021, methodology): “As deep neural networks have gained much attention then, a series of neural supervised models have been proposed (Liu et al., 2013; Zeng et al., 2014; Xu et al., 2015; Santos et al., 2015; Zhang & Wang, 2015; Verga et al., 2016; Verga & McCallum, 2016; Li et al., 2019; Distiawan et al., 2019; Ding e”