Integrating Probabilistic Extraction Models and Data Mining to Discover Relations and Patterns in Text

In order for relation extraction systems to obtain human-level performance, they must be able to incorporate relational patterns inherent in the data (for example, that one's sister is likely one's mother's daughter, or that children are likely to attend the same college as their parents). Hand-coding such knowledge can be time-consuming and inadequate. Additionally, there may exist many interesting, unknown relational patterns that both improve extraction performance and provide insight into text. We describe a probabilistic extraction model that provides mutual benefits to both "top-down" relational pattern discovery and "bottom-up" relation extraction.

• Publication year

  2006

• Venue

  North American Chapter of the Association for Computational Linguistics

• Publication date

  2006-06-04

• Fields of study

  Computer Science

• Identifiers
  DOI 10.3115/1220835.1220873
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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