TY  - RPRT
ID  - SisLab3983
UR  - https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3983/
A1  - Can, Duy Cat
Y1  - 2020///
N2  - Background: Most previous relation extraction (RE) studies have focused on intra-sentence relations and haveignored relations that span sentences, i.e. inter-sentence relations. Such relations connect entities at thedocument level rather than as relational facts in a single sentence. Extracting facts that are expressed acrosssentences leads to some challenges and requires different approaches than those usually applied in recentintra-sentence relation extraction. Despite recent results, there are still limitations to be overcome.Results:We present a novel representation for a sequence of consecutive sentences, namely documentsub-graph, to extract inter-sentence relations. Experiments on the BioCreative V Chemical-Disease Relationcorpus demonstrate the advantages and robustness of our novel system to extract both intra- andinter-sentence relations in biomedical literature abstracts. The experimental results are comparable tostate-of-the-art approaches and show the potential by demonstrating the effectiveness of graphs, deeplearning-based model and other processing techniques. Experiments were also carried out to verify therationality and impact of various additional information and model components.Conclusions:Our proposed graph-based representation helps to extract?50% of inter-sentence relations andboosts the model performance on both precision and recall compared to the baseline model.
PB  - BMC Journal of Biomedical Semantics
M1  - technical_report
TI  - Exploiting document graphs for inter-sentence relation extraction
AV  - restricted
ER  -