A Seed-driven Bottom-up Machine Learning Framework for Extracting ...
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A Seed-driven Bottom-up Machine Learning Framework for Extracting ...
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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
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 interaction between relations and their projections on k dimensional subspaces where 1≤k
