Improving neural fine-grained entity typing with knowledge attention

32nd AAAI Conference on Artificial Intelligence, AAAI 2018

Volumn , Issue , 2018, Pages 5997-6004

  Xin, Ji ^a   Lin, Yankai ^a   Liu, Zhiyuan ^a   Sun, Maosong ^a  

^a TSINGHUA UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; SEMANTICS;

BACKGROUND INFORMATION; CASE-STUDIES; KNOWLEDGE BASIS (KBS); MODEL COMPLEXES; REAL-WORLD DATASETS; SEMANTIC TYPES; STATE-OF-THE-ART METHODS; TYPE CLASSIFICATIONS;

NATURAL LANGUAGE PROCESSING SYSTEMS;

EID: 85060479874     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (36)

1. Bordes, A.; Weston, J.; Collobert, R.; and Bengio, Y. 2011. Learning structured embeddings of knowledge bases. In Proceedings of AAAI.
2. Bordes, A.; Usunier, N.; Garcia-Duran, A.; Weston, J.; and Yakhnenko, O. 2013. Translating embeddings for modeling multi-relational data. In Proceedings of NIPS.
3. Bordes, A.; Weston, J.; and Usunier, N. 2014. Open question answering with weakly supervised embedding models. In Proceedings of ECML-PKDD.
4. Carlson, A.; Betteridge, J.; Wang, R. C.; Hruschka Jr, E. R.; and Mitchell, T. M. 2010. Coupled semi-supervised learning for information extraction. In Proceedings of WSDM.
5. Chabchoub, M.; Gagnon, M.; and Zouaq, A. 2016. Collective disambiguation and semantic annotation for entity linking and typing. In Proceedings of SWEC.
6. Collins, M., and Singer, Y. 1999. Unsupervised models for named entity classification. In Proceedings of EMNLP.
7. Del Corro, L.; Abujabal, A.; Gemulla, R.; and Weikum, G. 2015. Finet: Context-aware fine-grained named entity typing. In Proceedings of EMNLP.
8. Dong, L.; Wei, F.; Sun, H.; Zhou, M.; and Xu, K. 2015. A hybrid neural model for type classification of entity mentions. In Proceedings of IJCAI.
9. Gillick, D.; Lazic, N.; Ganchev, K.; Kirchner, J.; and Huynh, D. 2014. Context-dependent fine-grained entity type tagging. arXiv preprint arXiv:1412.1820.
10. He, S.; Liu, K.; Ji, G.; and Zhao, J. 2015. Learning to represent knowledge graphs with gaussian embedding. In Proceedings of CIKM.
11. Ji, G.; He, S.; Xu, L.; Liu, K.; and Zhao, J. 2015. Knowledge graph embedding via dynamic mapping matrix. In Proceedings of ACL.
12. Ji, G.; Liu, K.; He, S.; and Zhao, J. 2016. Knowledge graph completion with adaptive sparse transfer matrix. Proceedings of AAAI.
13. Jiang, J., and Zhai, C. 2006. Exploiting domain structure for named entity recognition. In Proceedings of NAACL.
14. Kingma, D., and Ba, J. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
15. Kozareva, Z. 2006. Bootstrapping named entity recognition with automatically generated gazetteer lists. In Proceedings of EACL.
16. Lin, Y.; Liu, Z.; Sun, M.; Liu, Y.; and Zhu, X. 2015. Learning entity and relation embeddings for knowledge graph completion. In Proceedings of AAAI.
17. Lin, Y.; Liu, Z.; and Sun, M. 2015. Modeling relation paths for representation learning of knowledge bases. Proceedings of EMNLP.
18. Ling, X., and Weld, D. S. 2012. Fine-grained entity recognition. In Proceedings of AAAI.
19. Liu, Y.; Liu, K.; Xu, L.; and Zhao, J. 2014. Exploring fine-grained entity type constraints for distantly supervised relation extraction. In Proceedings of COLING.
20. Mintz, M.; Bills, S.; Snow, R.; and Jurafsky, D. 2009. Distant supervision for relation extraction without labeled data. In Proceedings of ACL.
21. Nickel, M.; Rosasco, L.; and Poggio, T. 2016. Holographic embeddings of knowledge graphs. In Proceedings of AAAI.
22. Pennington, J.; Socher, R.; and Manning, C. D. 2014. Glove: Global vectors for word representation. In Proceedings of EMNLP, volume 14.
23. Ratinov, L., and Roth, D. 2009. Design challenges and misconceptions in named entity recognition. In Proceedings of CoNLL.
24. Ren, X.; He, W.; Qu, M.; Huang, L.; Ji, H.; and Han, J. 2016a. Afet: Automatic fine-grained entity typing by hierarchical partial-label embedding. In Proceedings of EMNLP.
25. Ren, X.; He, W.; Qu, M.; Voss, C. R.; Ji, H.; and Han, J. 2016b. Label noise reduction in entity typing by heterogeneous partial-label embedding. In Proceedings of KDD.
26. Shimaoka, S.; Stenetorp, P.; Inui, K.; and Riedel, S. 2016. An attentive neural architecture for fine-grained entity type classification. arXiv preprint arXiv:1604.05525.
27. Shimaoka, S.; Stenetorp, P.; Inui, K.; and Riedel, S. 2017. Neural architectures for fine-grained entity type classification. In Proceedings of EACL.
28. Srivastava, N.; Hinton, G. E.; Krizhevsky, A.; Sutskever, I.; and Salakhutdinov, R. 2014. Dropout: a simple way to prevent neural networks from overfitting. JMLR 15(1).
29. Tjong Kim Sang, E. F., and De Meulder, F. 2003. Introduction to the conll-2003 shared task: Language-independent named entity recognition. In Proceedings of HLT-NAACL.
30. Wang, Z.; Zhang, J.; Feng, J.; and Chen, Z. 2014. Knowledge graph embedding by translating on hyperplanes. In Proceedings of AAAI.
31. Yaghoobzadeh, Y.; Adel, H.; and Schütze, H. 2017. Noise mitigation for neural entity typing and relation extraction. In Proceedings of EACL.
32. Yaghoobzadeh, Y., and Schütze, H. 2015. Corpus-level fine-grained entity typing using contextual information. In Proceedings of EMNLP.
33. Yaghoobzadeh, Y., and Schütze, H. 2017. Multi-level representations for fine-grained typing of knowledge base entities. In Proceedings of EACL.
34. Yahya, M.; Berberich, K.; Elbassuoni, S.; and Weikum, G. 2013. Robust question answering over the web of linked data. In Proceedings of CIKM.
35. Yogatama, D.; Gillick, D.; and Lazic, N. 2015. Embedding methods for fine grained entity type classification. In Proceedings of ACL.
36. Yosef, M. A.; Bauer, S.; Hoffart, J.; Spaniol, M.; and Weikum, G. 2012. HYENA: Hierarchical type classification for entity names. In Proceedings of COLING.