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arXiv · 2006.08185

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

Sachin Pawar·Pushpak Bhattacharyya·Girish K. Palshikar
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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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Sachin Pawar, Pushpak Bhattacharyya, Girish K. Palshikar. 2020-06-15. Extracting N-ary Cross-sentence Relations using Constrained Subsequence Kernel. https://arxiv.org/abs/2006.08185

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