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

Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pages 584–591, Prague, Czech Republic, June 2007. c⃝2007 Association for Computational Linguistics A Seed-driven Bottom-up Machine Learning Framework for Extracting Relations of Various Complexity Feiyu Xu, Hans Uszkoreit and Hong Li Language Technology Lab, DFKI GmbH Stuhlsatzenhausweg 3, D-66123 Saarbruecken {feiyu,uszkoreit,hongli}@dfki.de Abstract 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 awards and management succession events. Perfor- mance for the new Nobel Prize task is strong. For the management succession task the results compare favorably with those of existing pattern acquisition ap- proaches. 1 Introduction Information extraction (IE) has the task to discover n-tuples of relevant items (entities) belonging to an n-ary relation in natural language documents. One of the central goals of the ACE program1 is to de- velop a more systematically grounded approach to IE starting from elementary entities, binary rela- 1 http://projects.ldc.upenn.edu/ace/ tions to n-ary relations such as events. Current semi- or unsupervised approaches to automatic pattern acquisition are either limited to a certain linguistic representation (e.g., subject-verb-object), or only deal with binary relations, or cannot assign slot filler roles to the extracted arguments, or do not have good selection and filtering methods to handle the large number of tree patterns (Riloff, 1996; Agichtein and Gravano, 2000; Yangarber, 2003; Sudo et al., 2003; Greenwood and Stevenson, 2006; Stevenson and Greenwood, 2006). Most of these approaches do not consider the linguistic in- teraction between relations and their projections on k dimensional subspaces where 1≤k<n, which is important for scalability and reusability of rules. Stevenson and Greenwood (2006) present a sys- tematic investigation of the pattern representation models and point out that substructures of the lin- guistic representation and the access to the embed- ded structures are important for obtaining a good coverage of the pattern acquisition. However, all considered representation models (subject-verb- object, chain model, linked chain model and sub- tree model) are verb-centered. Relations embedded in non-verb constructions such as a compound noun cannot be discovered: (1) the 2005 Nobel Peace Prize (1) describes a ternary relation referring to three properties of a prize: year, area and prize name. We also observe that the automatically acquired patterns in Riloff (1996), Yangarber (2003), Sudo et al. (2003), Greenwood and Stevenson (2006) cannot be directly used as relation extraction rules because the …

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