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

13 years 6 months ago

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A minimally supervised machine learning framework is described for extracting relations of various complexity. Bootstrapping starts from a small set of n-ary relation instances as “seeds”, in order to automatically 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 construction is supported by a bottom-up pattern extraction method. In comparison to other automatic approaches, our rules cannot only localize relation arguments but also assign their exact target argument roles. The method is evaluated in two tasks: the extraction of Nobel Prize awards and management succession events. Performance for the new Nobel Prize task is strong. For the management succession task the results compare favorably with those of existing pattern acquisition approache...

Feiyu Xu, Hans Uszkoreit, Hong Li

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