KG-Survey

Survey on Knowledge Graph

Paper List

Entity Linking

• Entity Linking for Queries by Searching Wikipedia Sentences [EMNLP2017, cites=2]

• Entity Linking via Joint Encoding of Types , Descriptions , and Context [EMNLP2017, cites=14]

• List-only Entity Linking [ACL2017, cites=6]

• Improving Entity Linking by Modeling Latent Relations between Mentions [ACL2018]

• Hierarchical Losses and New Resources for Fine-grained Entity Typing and Linking [ACL2018]

• ELDEN : Improved Entity Linking using Densified Knowledge Graphs [NAACL2018, cites=1]

• Pangloss: Fast Entity Linking in Noisy Text Environments [KDD2018]

• Neural Collective Entity Linking [COLING2018]

• Systematic Study of Long Tail Phenomena in Entity Linking [COLING2018]

• They Exist! Introducing Plural Mentions to Coreference Resolution and Entity Linking [COLING2018]

Named Entity Recognition

• A survey of named entity recognition and classification. [cites=1691]

• A Survey on Recent Advances in Named Entity Recognition from Deep Learning models [COLING2018]

• A Study of the Importance of External Knowledge in the Named Entity Recognition Task [ACL2018]

• Exploiting Wikipedia as External Knowledge for Named Entity Recognition [EMNLP2007]

• Alleviating Poor Context with Background Knowledge for Named Entity Disambiguation. [ACL2016]

• DEEP ACTIVE LEARNING FOR NAMED ENTITY RECOGNITION [ICLR2018, cites=11]

• (Open source tool) NCRF++: An Open-source Neural Sequence Labeling Toolkit [ACL2018, cites=4]

• Empower Sequence Labeling with Task-Aware Neural Language Model [AAAI2018, cites=7]

• 1.Named Entity Recognition With Parallel Recurrent Neural Networks [NAACL2018]

• 2.Semi-supervised sequence tagging with bidirectional language models [ACL2017, cites=44]

• 3.Named Entity Recognition with Bidirectional LSTM-CNNs [axiv2015, cites=261]

• 4.Neural Architectures for Named Entity Recognition [NAACL2016, cites=560]

• 5.Named Entity Recognition for Novel Types by Transfer Learning [EMNLP2016, cites=11]

• 6.Domain Specific Named Entity Recognition Referring to the Real World by Deep Neural Networks [ACL2016, cites=6]

• 7.Segment-Level Neural Conditional Random Fields for Named Entity Recognition [IJCNLP2017, cites=2]

• 8.Multi-domain evaluation framework for named entity recognition tools [Computer Speech & Language2017, cites=2]

• 9.Deep learning with word embeddings improves biomedical named entity recognition [Bioinformatics2017, cites=40]

• 10.A combination of active learning and self-learning for named entity recognition on Twitter using conditional random fields [KBS2017, cites=4]

• 11.Leveraging Linguistic Structures for Named Entity Recognition with Bidirectional Recursive Neural Networks [EMNLP2017, cites=2]

• 12.A Local Detection Approach for Named Entity Recognition and Mention Detection [ACL2017, cites=9]

• 13.Weakly Supervised Cross-Lingual Named Entity Recognition via Effective Annotation and Representation Projection (ACL2017, cites=5)

• 14.A Neural Layered Model for Nested Named Entity Recognition [ACL2018, cites=0]

• 15.Combining rule-based and statistical mechanisms for low-resource named entity recognition [MT2018, cites=1]

• 16.Design Challenges and Misconceptions in Neural Sequence Labeling [COLING2018, cites=2]

Relation Extraction

1. Pengda Qin, Weiran XU, William Yang Wang. Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning ACL (2018).
2. Pengda Qin, Weiran XU, William Yang Wang. DSGAN: Generative Adversarial Training for Distant Supervision Relation Extraction ACL (2018).
3. Fenia Christopoulou, Makoto Miwa, Sophia Ananiadou. A Walk-based Model on Entity Graphs for Relation Extraction ACL (2018).
4. Van-Thuy Phi, Joan Santoso, Masashi Shimbo, Yuji Matsumoto. Ranking-Based Automatic Seed Selection and Noise Reduction for Weakly Supervised Relation Extraction ACL (2018).
5. Y Lin, S Shen, Z Liu, H Luan, M Sun. Neural Relation Extraction with Selective Attention over Instances. ACL (2016).
6. X Zeng, S He, K Liu, J Zhao. Large Scaled Relation Extraction with Reinforcement Learning. AAAI (2018).
7. Yatian Shen, Xuanjing Huang. Attention-Based Convolutional Neural Network for Semantic Relation Extraction COLING (2016).
8. D Zeng, K Liu, S Lai, G Zhou, J Zhao. Relation classification via convolutional deep neural networks. COLING (2014).
9. D Zeng, K Liu, Y Chen, J Zhao. Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks. ACL (2015).
10. G Ji, K Liu, S He, J Zhao. Distant Supervision for Relation Extraction with Sentence-Level Attention and Entity Descriptions. AAAI (2017).
11. TH Nguyen, R Grishman. Relation Extraction：Perspective from Convolutional Neural Networks.
12. D Zhang, D Wang. Relation Classification via Recurrent Neural Network. 2015
13. TH Nguyen, R Grishman. Combining Neural Networks and Log-linear Models to Improve Relation Extraction. 2015
14. MR Gormley, M Yu, M Dredze. Improved Relation Extraction with Feature-Rich Compositional Embedding Models. 2015
15. Xiang Ren, Zeqiu Wu, Wenqi He, Meng Qu, Clare R. Voss, Heng Ji, Tarek F. Abdelzaher, Jiawei Han. CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases. WWW (2017).
16. Suncong Zheng, Feng Wang, Hongyun Bao, Yuexing Hao,Peng Zhou, Bo Xu. Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme.
17. Y Lin, Z Liu, M Sun. Neural Relation Extraction with Multi-lingual Attention. ACL (2017).
18. Bingfeng Luo, Yansong Feng, Zheng Wang, Zhanxing Zhu, Songfang Huang, Rui Yan, Dongyan Zhao. Learning with noise: Enhance distantly supervised relation extraction with dynamic transition matrix. ACL (2017).
19. J Tourille, O Ferret, A Neveol, X Tannier. Neural Architecture for Temporal Relation Extraction: A Bi-LSTM Approach for Detecting Narrative Containers. ACL (2017).
20. N Peng, H Poon, C Quirk, K Toutanova, et al. Cross-Sentence N-ary Relation Extraction with Graph LSTMs ACL (2017).
21. A Abad, M Nabi, A Moschitti. Self-Crowdsourcing Training for Relation Extraction. ACL (2017).
22. M Miwa, M Bansal. End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures
23. JP. CND Santos, B Xiang, B Zhou. Classifying relations by ranking with convolutional neural networks [cites=119, ACL'2015].
24. JP. X Jiang, W Quan, L Peng, B Wang. Relation Extraction with Multi-instance Multi-label Convolutional Neural Networks [cites=11 ,COLING'2016]
25. Adversarial Training for Relation Extraction [EMNLP 2017]
26. Open Relation Extraction and Grounding [IJCNLP 2017]
27. Global Relation Embedding for Relation Extraction [NAACL 2018]
28. Incorporating Relation Paths in Neural Relation Extraction [EMNLP 2017]
29. Distant Supervision for Relation Extraction beyond the Sentence Boundary [EACL 2017]
30. Relation extraction pattern ranking using word similarity [NAACL 2015]
31. Reinforcement Learning for Relation Classification from Noisy Data

Common Paper

• Conditional random fields: Probabilistic models for segmenting and labeling sequence data [2001, cites=11655]
• Long short-term memory [Neural computation1991, cites=12498]
• Imagenet classification with deep convolutional neural networks [NIPS2012, cites=28920]