Corpus ID: 7558249

Collective Cross-Document Relation Extraction Without Labelled Data

  title={Collective Cross-Document Relation Extraction Without Labelled Data},
  author={Limin Yao and S. Riedel and A. McCallum},
  • Limin Yao, S. Riedel, A. McCallum
  • Published in EMNLP 2010
  • Computer Science
  • We present a novel approach to relation extraction that integrates information across documents, performs global inference and requires no labelled text. In particular, we tackle relation extraction and entity identification jointly. We use distant supervision to train a factor graph model for relation extraction based on an existing knowledge base (Freebase, derived in parts from Wikipedia). For inference we run an efficient Gibbs sampler that leads to linear time joint inference. We evaluate… CONTINUE READING
    137 Citations
    Joint inference of entities, relations, and coreference
    • 91
    • PDF
    Distant Supervised Relation Extraction with Wikipedia and Freebase
    • Highly Influenced
    • PDF
    Joint Inference for Knowledge Base Population
    • 4
    • PDF
    Reducing Wrong Labels in Distant Supervision for Relation Extraction
    • 181
    • PDF
    Global Distant Supervision for Relation Extraction
    • 36
    • PDF
    Feature-based models for improving the quality of noisy training data for relation extraction
    • 13
    Web relation extraction with distant supervision
    • 10
    • PDF
    Encoding Relation Requirements for Relation Extraction via Joint Inference
    • 7
    • PDF


    Modeling Relations and Their Mentions without Labeled Text
    • 816
    • PDF
    Distant supervision for relation extraction without labeled data
    • 2,060
    • Highly Influential
    • PDF
    Bi-directional Joint Inference for Entity Resolution and Segmentation Using Imperatively-Defined Factor Graphs
    • 40
    • PDF
    1 Global Inference for Entity and Relation Identification via a Linear Programming Formulation
    • 134
    • PDF
    Learning 5000 Relational Extractors
    • 150
    • PDF
    Scaling Textual Inference to the Web
    • 63
    • PDF
    Dependency Tree Kernels for Relation Extraction
    • 820
    • PDF