This paper presents an efficient method of extracting n-ary relations from multiple sentences which is called Entity-path and Discourse relation-centric Relation Extractor (EDCRE). Unlike previous approaches, the proposed method focuses on an entity link, which consists of dependency edges between entities, and discourse relations between sentences. Specifically, the proposed model consists of two main sub-models. The first one encodes sentences with a higher weight on the entity link while considering the other edges with an attention mechanism. To consider various latent discourse relations between sentences, the second sub-model encodes discourse relations between adjacent sentences considering the contents of each sentence. Experiment results on the cross-sentence relation extraction dataset, PubMed, and the document-level relation extraction dataset, DocRED, show that the proposed model outperforms state-of-the-art methods of extracting relations across sentences. Furthermore, ablation study proves that both the two main sub-models have noticeable effect on the relation extraction task.
|Title of host publication||CIKM 2020 - Proceedings of the 29th ACM International Conference on Information and Knowledge Management|
|Publisher||Association for Computing Machinery|
|Number of pages||10|
|Publication status||Published - 2020 Oct 19|
|Event||29th ACM International Conference on Information and Knowledge Management, CIKM 2020 - Virtual, Online, Ireland|
Duration: 2020 Oct 19 → 2020 Oct 23
|Name||International Conference on Information and Knowledge Management, Proceedings|
|Conference||29th ACM International Conference on Information and Knowledge Management, CIKM 2020|
|Period||20/10/19 → 20/10/23|
Bibliographical noteFunding Information:
This work was supported by NCSOFT NLP Center. Kyong-Ho Lee is the corresponding author.
© 2020 ACM.
All Science Journal Classification (ASJC) codes
- Business, Management and Accounting(all)
- Decision Sciences(all)