Corpus ID: 6983197

The Tradeoffs Between Open and Traditional Relation Extraction

  title={The Tradeoffs Between Open and Traditional Relation Extraction},
  author={M. Banko and Oren Etzioni},
  • M. Banko, Oren Etzioni
  • Published in ACL 2008
  • Computer Science
  • Traditional Information Extraction (IE) takes a relation name and hand-tagged examples of that relation as input. [...] Key Method We then present a new model for Open IE called O-CRF and show that it achieves increased precision and nearly double the recall than the model employed by TEXTRUNNER, the previous stateof-the-art Open IE system. Second, when the number of target relations is small, and their names are known in advance, we show that O-CRF is able to match the precision of a traditional extraction…Expand Abstract
    396 Citations
    Identifying Relations for Open Information Extraction
    • 1,114
    • PDF
    Towards Large-Scale Unsupervised Relation Extraction from the Web
    • 17
    • PDF
    Adapting Open Information Extraction to Domain-Specific Relations
    • 66
    • PDF
    Improving Open Relation Extraction via Sentence Re-Structuring
    • 17
    • PDF
    An analysis of open information extraction based on semantic role labeling
    • 112
    • PDF
    On the Limits of Aligning OpenIE Extractions with Knowledge Bases
    • 2020
    Comparison of open information extraCtion for english and spanish
    • Zhila A, Gelbukh A
    • 2013
    • 16
    • PDF
    Open Relation Extraction and Grounding
    • 13


    Open Information Extraction from the Web
    • 2,085
    • PDF
    Unsupervised Resolution of Objects and Relations on the Web
    • 90
    • PDF
    TEG—a hybrid approach to information extraction
    • 47
    • PDF
    Unsupervised named-entity extraction from the Web: An experimental study
    • 1,153
    • PDF
    Automatic Discovery of Part-Whole Relations
    • 277
    • PDF
    Learning Syntactic Patterns for Automatic Hypernym Discovery
    • 749
    • PDF
    Snowball: extracting relations from large plain-text collections
    • 1,270
    • PDF
    Automatic Acquisition of Hyponyms from Large Text Corpora
    • 3,410
    • PDF
    Machine Learning for Information Extraction in Informal Domains
    • 382
    • PDF
    Integrating Probabilistic Extraction Models and Data Mining to Discover Relations and Patterns in Text
    • 190
    • PDF