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Most of the past work in relation extraction deals with relations occurringwithin a sentence and having only two entity arguments. We propose a newformulation of the relation extraction task where the relations are moregeneral than intra-sentence relations in the sense that they may span multiplesentences and may have more than two arguments. Moreover, the relations aremore specific than corpus-level relations in the sense that their scope islimited only within a document and not valid globally throughout the corpus. Wepropose a novel sequence representation to characterize instances of suchrelations. We then explore various classifiers whose features are derived fromthis sequence representation. For SVM classifier, we design a ConstrainedSubsequence Kernel which is a variant of Generalized Subsequence Kernel. Weevaluate our approach on three datasets across two domains: biomedical andgeneral domain.

Extracting N-ary Cross-sentence Relations using Constrained Subsequence Kernel