CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases

@article{Ren2017CoTypeJE,
  title={CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases},
  author={Xiang Ren and Zeqiu Wu and W. He and Meng Qu and Clare R. Voss and Heng Ji and T. Abdelzaher and Jiawei Han},
  journal={Proceedings of the 26th International Conference on World Wide Web},
  year={2017}
}
  • Xiang Ren, Zeqiu Wu, +5 authors Jiawei Han
  • Published 2017
  • Computer Science
  • Proceedings of the 26th International Conference on World Wide Web
Extracting entities and relations for types of interest from text is important for understanding massive text corpora. Traditionally, systems of entity relation extraction have relied on human-annotated corpora for training and adopted an incremental pipeline. Such systems require additional human expertise to be ported to a new domain, and are vulnerable to errors cascading down the pipeline. In this paper, we investigate joint extraction of typed entities and relations with labeled data… Expand
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References

SHOWING 1-10 OF 15 REFERENCES
Modeling Relations and Their Mentions without Labeled Text
  • 836
  • Highly Influential
  • PDF
Fine-Grained Entity Recognition
  • 343
  • Highly Influential
  • PDF
Knowledge-Based Weak Supervision for Information Extraction of Overlapping Relations
  • 753
  • Highly Influential
  • PDF
Context-Dependent Fine-Grained Entity Type Tagging
  • 83
  • Highly Influential
  • PDF
Multi-instance Multi-label Learning for Relation Extraction
  • 602
  • Highly Influential
  • PDF
Exploring Various Knowledge in Relation Extraction
  • 640
  • Highly Influential
  • PDF
Improved Relation Extraction with Feature-Rich Compositional Embedding Models
  • 98
  • Highly Influential
  • PDF
BioInfer: a corpus for information extraction in the biomedical domain
  • 441
  • Highly Influential
Translating Embeddings for Modeling Multi-relational Data
  • 2,737
  • Highly Influential
  • PDF
Linguistic Resources for 2013 Knowledge Base Population Evaluations
  • 31
  • Highly Influential
...
1
2
...