Relation extraction with weakly supervised learning based on process-structure-property-performance reciprocity

References

• Augenstein I , Das M , Riedel S , et al. SemEval 2017 Task 10: ScienceIE -Extracting Keyphrases and Relations from Scientific Publications. In: Proceedings of the 11th International Workshop on Semantic Evaluations (SemEval-2017); Vancouver (Canada); 2017. p. 546–555.
   Google Scholar
• Cohen M. Unknowables in the essence of materials science and engineering. Mater Sci Eng. 1976;25:3–4.
   Google Scholar
• Olson GB . Genomic materials design: the ferrous frontier. Acta Mater. 2013;61(3):771–781.
   Web of Science ®Google Scholar
• Olson GB . Designing a new material world. Science. 2000;288(5468):993–998.
   Web of Science ®Google Scholar
• Xiong W , Olson GB . Cybermaterials: Materials by design and accelerated insertion of materials. npj Computational Materials. 2016;2:15009 .
   Web of Science ®Google Scholar
• Xu Y , Yamazaki M , Villars P . Inorganic materials database for exploring the nature of material. Jpn J Appl Phys. 2011;50(11S):11RH02.
   Google Scholar
• Mintz M , Bills S , Snow R , et al. 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; Suntec (Singapore); 2009. p. 1003–1011.
   Google Scholar
• Hoffmann R , Zhang C , Ling X , et al. Knowledge-based weak supervision for information extraction of overlapping relations. In: Proceedings of 49th Annual Meeting of the Association for Computational Linguistics; Portland, OR; 2011. p. 541–550.
   Google Scholar
• Surdeanu M , Tibshirani J , Nallapati R , et al. 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; Jeju Island (Korea); 2011. p. 455–465.
   Google Scholar
• Fan M , Zhao D , Zhou Q , et al. Distant supervision for relation extraction with matrix completion. In: Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics; Baltimore, MD; 2014. p. 839–849.
   Google Scholar
• Riedel S , Yao L , McCallum A . Modeling relations and their mentions without labeled text. In: Proceedings of the 2010 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases; Catalonia (Spain); 2010. p. 148–163.
   Google Scholar
• Huang YY , Wang WY. Deep residual learning for weakly-supervised relation extraction. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing; Copenhagen (Denmark); 2017. p. 1803–1807.
   Google Scholar
• Zeng D , Liu K , Chen Y , et al. Distant supervision for relation extraction via piecewise convolutional neural networks. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing; Lisbon (Portugal); 2015. p. 1753–1762.
   Google Scholar
• Lin Y , Shen S , Liu Z , et al. Neural relation extraction with selective attention over instances. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics; Berlin (Germany); 2016. p. 2124–2133.
   Google Scholar
• Ji G , Liu K , He S , et al. Distant supervision for relation extraction with sentence-level attention and entity descriptions. In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence; San Francisco (CA); 2017.
   Google Scholar
• Liu T , Wang K , Chang B , et al. A soft-label method for noise-tolerant distantly supervised relation extraction. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing; Copenhagen (Denmark); 2017. p. 1791–1796.
   Google Scholar
• Manning CD, Surdeanu M , Bauer J , et al. The Stanford CoreNLP Natural Language Processing Toolkit. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations; Baltimore (MD); 2014. p. 55–60.
   Google Scholar
• Pennington J , Socher R , Manning CD. GloVe: Global Vectors for Word Representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing; Doha (Qatar); 2014. p. 1532–1543.
   Google Scholar