Paper: A Seed-driven Bottom-up Machine Learning Framework for Extracting Relations of Various Complexity

ACL ID P07-1074
Title A Seed-driven Bottom-up Machine Learning Framework for Extracting Relations of Various Complexity
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2007
Authors

A minimally supervised machine learning framework is described for extracting rela- tions of various complexity. Bootstrapping starts from a small set of n-ary relation in- stances as “seeds”, in order to automati- cally learn pattern rules from parsed data, which then can extract new instances of the relation and its projections. We propose a novel rule representation enabling the composition of n-ary relation rules on top of the rules for projections of the relation. The compositional approach to rule con- struction is supported by a bottom-up pat- tern extraction method. In comparison to other automatic approaches, our rules can- not only localize relation arguments but also assign their exact target argument roles. The method is evaluated in two tasks: the extraction of Nobel Prize...