Paper: Integrating Probabilistic Extraction Models And Data Mining To Discover Relations And Patterns In Text

ACL ID N06-1038
Title Integrating Probabilistic Extraction Models And Data Mining To Discover Relations And Patterns In Text
Venue Human Language Technologies
Session Main Conference
Year 2006
Authors

In order for relation extraction systems to obtain human-level performance, they must be able to incorporate relational pat- terns 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 par- ents). Hand-coding such knowledge can be time-consuming and inadequate. Addi- tionally, there may exist many interesting, unknown relational patterns that both im- prove extraction performance and provide insight into text. We describe a probabilis- tic extraction model that provides mutual benefits to both “top-down” relational pat- tern discovery and “bottom-up” relation extraction.