German Research Center for Artificial Intelligence
In: Proceedings of the Third IEEE International Conference on Natural Language Processing and Knowledge Engineering. IEEE International Conference on Natural Language Processing and Knowledge Engineering Seiten 6-10 IEEE 9/2007.
We explain several bootstrapping methods, most of them starting with patterns as seeds and some with event seeds. We will also briefly describe our own approach of bootstrapping (Xu et al. 2007), an extension of Xu et al. (2006). In this approach, learning starts from a small set of n-ary relation instances as "seeds" in order to auto ma ti cally learn pattern rules from parsed data, which then can extract new instances of the n-ary relation and its projections. We then present a theory of the suitability of learning via bootstrapping with respect to tasks and data properties.