Relation Extraction with Multi-instance Multi-label Convolutional Neural Networks

By Xiaotian Jiang, Quan Wang, Peng Li. 2016

123 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Shirui Pan
2. Shaoxiong Ji
3. Philip S. Yu
4. Erik Cambria
5. Lingfeng Zhong
6. Qian Li
7. Hao Peng
8. Yi Wu

Citations in the corpus

• A Two-phase Prototypical Network Model for Incremental Few-shot Relation Classification (2020, background): “, 2015) and distantly supervised (Mintz et al., 2009; Jiang et al., 2016; Ye and Ling, 2019a) RC models are widely used and achieve remarkable performance.”
• A Comprehensive Survey on Automatic Knowledge Graph Construction (2023, methodology): “MIML CNN [194] uses a CNN to proceed with each sentence bag, then leverages a cross-sentence pooling operation to derive an entity-pair representation for multi-label relation modeling.”
• A Survey on Knowledge Graphs: Representation, Acquisition, and Applications (2020, methodology): “Inspired by the diachronic word embedding, Goel et al. [182] CNN + max pooling position embedding Multi CNN [137] Multi-window convolution + max pooling position embedding PCNN [138] CNN + piecewise max pooling position embedding MIMLCNN [139] CNN + piecewise and cross-sentence max pooling position ”
• Knowledge Graph Embedding: A Survey of Approaches and Applications (2017, background): “Much research has tried to leverage KGs for this task, but usually as distant supervision to automatically generate labeled data [9], [125], [126], [127].”
• Adversarial Training for Relation Extraction (2017, methodology): “, 2016), CNN with sentencewise pooling (Jiang et al., 2016) and attention CNN (Wang et al.”
• Prototypical Representation Learning for Relation Extraction (2021, unknown): “…-10.900574 32.073906 -7.5629783 36.904022 -15.117426 -47.537777 , various denoising methods have been explored to reduce noise effects for distant supervision (Zeng et al., 2015; Lin et al., 2016; Jiang et al., 2016; Han et al., 2018a; Wu et al., 2017; Qin et al., 2018a;b; Feng et al., 2018).”