Relation Classification via Convolutional Deep Neural Network

By Daojian Zeng, Kang Liu, Siwei Lai, Guangyou Zhou, Jun Zhao. 2014

1,761 citations recorded by the source corpus.

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Citations in the corpus

Challenges in Data-to-Document Generation (2017, background): “Indeed, many relation extraction systems reduce relation extraction to multi-class classiﬁcation precisely in this way (Zhang, 2004; Zhou et al., 2008; Zeng et al., 2014; dos Santos et al., 2015).”
Position-aware Attention and Supervised Data Improve Slot Filling (2017, methodology): “This can be explained by noting that the ﬁlters in CNNs are essentially a form of “fuzzy n-gram patterns”.”
Continual Few-shot Relation Learning via Embedding Space Regularization and Data Augmentation (2022, methodology): “Indeed, in relation to CFRL,087 Zhang et al. (2021), Zhu et al. (2021) and Chen and088 Lee (2021) recently introduce methods for incre-089 mental few-shot learning in Computer Vision.090 Based on the observation that the learning of091 emerging few-shot tasks may result in distorted fea-092 ture dis”
Incremental Few-shot Text Classification with Multi-round New Classes: Formulation, Dataset and System (2021, neutral): “The other is relation classification which needs to determine the correct relation between two entities in a given sentence (Zeng et al., 2014).”
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.”
A Two-phase Prototypical Network Model for Incremental Few-shot Relation Classification (2020, background): “Currently, conventional deep supervised (Zeng et al., 2014; Gormley et al., 2015) and distantly supervised (Mintz et al.”
Continual Relation Learning via Episodic Memory Activation and Reconsolidation (2020, methodology): “…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 distantly…”
FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation (2018, methodology): “Under this setting, we evaluate different few-shot training strategies and state-of-the-art few-shot learning methods built upon two widely used instance encoders, CNN (Zeng et al., 2014) and PCNN (Zeng et al., 2015).”
Matching the Blanks: Distributional Similarity for Relation Learning (2019, background): “In a first group, supervised (Kambhatla, 2004; GuoDong et al., 2005; Zeng et al., 2014), or distantly supervised relation extractors (Mintz et al.”
Text Classification Improved by Integrating Bidirectional LSTM with Two-dimensional Max Pooling (2016, methodology): “The output of the i th word is shown in the following equation: Here, element-wise sum is used to combine the forward and backward pass outputs.”
Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Paths (2015, background): “Zeng et al. (2014) treat a sentence as sequential data and exploit the convolutional neural network (CNN); they also integrate word position information into their model.”
A Primer on Neural Network Models for Natural Language Processing (2015, methodology): “other feature types: each bin is associated with a d-dimensional vector, and these distanceembedding vectors are then trained as regular parameters in the network (Zeng et al., 2014; dos Santos et al., 2015; Zhu et al., 2015a; Nguyen & Grishman, 2015).”
Representing Text for Joint Embedding of Text and Knowledge Bases (2015, background): “performance (Zeng et al., 2014; Gormley et al., 2015).”
Improved Neural Relation Detection for Knowledge Base Question Answering (2017, background): “, 2015) to deep networks like CNNs and LSTMs (Zeng et al., 2014; dos Santos et al., 2015; Vu et al., 2016) and attention models (Zhou et al.”
Graph Convolution over Pruned Dependency Trees Improves Relation Extraction (2018, methodology): “Zeng et al. (2014) ﬁrst applied a one-dimensional convolutional neural network (CNN) with manual features to encode relations.”
Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification (2016, methodology): “Recently, deep learning methods provide an effective way of reducing the number of handcrafted features (Socher et al., 2012; Zeng et al., 2014).”
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; Zeng et al. 2014).”
A Multi-Channel Deep Neural Network for Relation Extraction (2020, methodology): “[28] used a convolutional neural network, which inputs concatenated word and position embeddings.”
Chemical-induced disease relation extraction via convolutional neural network (2017, result): “Similar to other CNN-based models (25, 26), we also”