  Most of the past work in relation extraction deals with relations occurring
within a sentence and having only two entity arguments. We propose a new
formulation of the relation extraction task where the relations are more
general than intra-sentence relations in the sense that they may span multiple
sentences and may have more than two arguments. Moreover, the relations are
more specific than corpus-level relations in the sense that their scope is
limited only within a document and not valid globally throughout the corpus. We
propose a novel sequence representation to characterize instances of such
relations. We then explore various classifiers whose features are derived from
this sequence representation. For SVM classifier, we design a Constrained
Subsequence Kernel which is a variant of Generalized Subsequence Kernel. We
evaluate our approach on three datasets across two domains: biomedical and
general domain.
