Aligning knowledge and text embeddings by entity descriptions

Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing

Volumn , Issue , 2015, Pages 267-272

  Zhong, Huaping ^a   Zhang, Jianwen ^b   Wang, Zhen ^a   Wan, Hai ^a   Chen, Zheng ^b  

^a SUN YAT SEN UNIVERSITY   (China)

^b MICROSOFT RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANCHORS; EMBEDDINGS; KNOWLEDGE BASED SYSTEMS; VECTOR SPACES;

KEY ISSUES; KNOWLEDGE BASE; MODEL-BASED OPC; MODEL-MAKING; TEXT CORPORA; WIKIPEDIA;

NATURAL LANGUAGE PROCESSING SYSTEMS;

EID: 84959918380     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.18653/v1/d15-1031     Document Type: Conference Paper

References (14)

1. Antoine Bordes, Jason Weston, Ronan Collobert, and Yoshua Bengio. 2011. Learning structured embeddings of knowledge bases. In Proceedings of the 25th AAAI Conference on Artificial Intelligence.
2. Antoine Bordes, Xavier Glorot, Jason Weston, and Yoshua Bengio. 2012. A semantic matching energy function for learning with multi-relational data. Machine Learning, pages 1-27.
3. Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. 2013. Translating embeddings for modeling multirelational data. In Advances in Neural Information Processing Systems, pages 2787-2795.
4. Kai-Wei Chang, Wen-tau Yih, and Christopher Meek. 2013. Multi-relational latent semantic analysis. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pages 1602-1612, Seattle, Washington, USA, October. Association for Computational Linguistics.
5. Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, and Zheng Chen. 2015. Learning entity and relation embeddings for knowledge graph completion. In Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, pages 2181-2187.
6. Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013a. Efficient estimation of word representations in vector space. arXiv preprint arX-iv:1301.3781.
7. Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013b. Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems, pages 3111-3119.
8. Mike Mintz, Steven Bills, Rion Snow, and Dan Jurafsky. 2009. Distant supervision for relation extraction without labeled data. In Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2-Volume 2, pages 1003-1011. Association for Computational Linguistics.
9. Sebastian Riedel, Limin Yao, and Andrew McCallum. 2010. Modeling relations and their mentions without labeled text. In Machine Learning and Knowledge Discovery in Databases, pages 148-163. Springer.
10. Richard Socher, Danqi Chen, Christopher D Manning, and Andrew Ng. 2013. Reasoning with neural tensor networks for knowledge base completion. In Advances in Neural Information Processing Systems, pages 926-934.
11. Mihai Surdeanu, Julie Tibshirani, Ramesh Nallapati, and Christopher D Manning. 2012. Multi-instance multi-label learning for relation extraction. In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pages 455-465. Association for Computational Linguistics.
12. Zhen Wang, Jianwen Zhang, Jianlin Feng, and Zheng Chen. 2014a. Knowledge graph and text jointly embedding. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, pages 1591-1601.
13. Zhen Wang, Jianwen Zhang, Jianlin Feng, and Zheng Chen. 2014b. Knowledge graph embedding by translating on hyperplanes. In Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, pages 1112-1119.
14. Jason Weston, Antoine Bordes, Oksana Yakhnenko, and Nicolas Usunier. 2013. Connecting language and knowledge bases with embedding models for relation extraction. arXiv preprint arXiv: 1307.7973.