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

• URL: http://arxiv.org/abs/2006.08185v1
• Date: Mon, 15 Jun 2020 07:23:58 GMT
• Title: Extracting N-ary Cross-sentence Relations using Constrained Subsequence Kernel
• Authors: Sachin Pawar, Pushpak Bhattacharyya, Girish K. Palshikar
• Abstract summary: We propose a new formulation of the relation extraction task where the relations are more general than intra-sentence relations. We propose a novel sequence representation to characterize instances of such relations. We evaluate our approach on three datasets across two domains: biomedical and general domain.
• Score: 36.86738081453646
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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