%0 Conference Proceedings %T Cross-Sentence N-ary Relation Extraction using Lower-Arity Universal Schemas %A Akimoto, Kosuke %A Hiraoka, Takuya %A Sadamasa, Kunihiko %A Niepert, Mathias %Y Inui, Kentaro %Y Jiang, Jing %Y Ng, Vincent %Y Wan, Xiaojun %S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) %D 2019 %8 November %I Association for Computational Linguistics %C Hong Kong, China %F akimoto-etal-2019-cross %X Most existing relation extraction approaches exclusively target binary relations, and n-ary relation extraction is relatively unexplored. Current state-of-the-art n-ary relation extraction method is based on a supervised learning approach and, therefore, may suffer from the lack of sufficient relation labels. In this paper, we propose a novel approach to cross-sentence n-ary relation extraction based on universal schemas. To alleviate the sparsity problem and to leverage inherent decomposability of n-ary relations, we propose to learn relation representations of lower-arity facts that result from decomposing higher-arity facts. The proposed method computes a score of a new n-ary fact by aggregating scores of its decomposed lower-arity facts. We conduct experiments with datasets for ternary relation extraction and empirically show that our method improves the n-ary relation extraction performance compared to previous methods. %R 10.18653/v1/D19-1645 %U https://aclanthology.org/D19-1645 %U https://doi.org/10.18653/v1/D19-1645 %P 6225-6231