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Impact of Entity Graphs on Extracting Semantic Relations

Authors :
Rashedur Rahman
Brigitte Grau
Sophie Rosset
Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur ( LIMSI )
Université Paris-Sud - Paris 11 ( UP11 ) -Centre National de la Recherche Scientifique ( CNRS )
IRT SystemX ( IRT SystemX )
Springer
Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur (LIMSI)
Université Paris Saclay (COmUE)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université - UFR d'Ingénierie (UFR 919)
Sorbonne Université (SU)-Sorbonne Université (SU)-Université Paris-Saclay-Université Paris-Sud - Paris 11 (UP11)
IRT SystemX (IRT SystemX)
Source :
Information Management and Big Data ISBN: 9783319905952, SIMBig (Revised Selected Papers), Communications in Computer and Information Science, Springer. Communications in Computer and Information Science, pp.31-47, 2018, Information Management and Big Data. Revised Selected Papers of SIMBig 2017, Information Management and Big Data.4th Annual International Symposium, SIMBig 2017, Lima, Peru, September 4-6, 2017, Revised Selected Papers, Springer. Information Management and Big Data.4th Annual International Symposium, SIMBig 2017, Lima, Peru, September 4-6, 2017, Revised Selected Papers, pp.31-47, 2018, Communications in Computer and Information Science, ⟨10.1007/978-3-319-90596-9_3⟩
Publication Year :
2018
Publisher :
Springer International Publishing, 2018.

Abstract

International audience; Relation extraction (RE) between a pair of entity mentions from text is an important and challenging task specially for open domain relations. Generally, relations are extracted based on the lexical and syntactical information at the sentence level. However, global information about known entities has not been explored yet for RE task. In this paper, we propose to extract a graph of entities from the overall corpus and to compute features on this graph that are able to capture some evidences of holding relationships between a pair of entities. The proposed features boost the RE performance significantly when these are combined with some linguistic features.

Details

ISBN :
978-3-319-90595-2
ISBNs :
9783319905952
Database :
OpenAIRE
Journal :
Information Management and Big Data ISBN: 9783319905952, SIMBig (Revised Selected Papers), Communications in Computer and Information Science, Springer. Communications in Computer and Information Science, pp.31-47, 2018, Information Management and Big Data. Revised Selected Papers of SIMBig 2017, Information Management and Big Data.4th Annual International Symposium, SIMBig 2017, Lima, Peru, September 4-6, 2017, Revised Selected Papers, Springer. Information Management and Big Data.4th Annual International Symposium, SIMBig 2017, Lima, Peru, September 4-6, 2017, Revised Selected Papers, pp.31-47, 2018, Communications in Computer and Information Science, ⟨10.1007/978-3-319-90596-9_3⟩
Accession number :
edsair.doi.dedup.....7e248ec0d4200e6c9724e33fa1bfe497
Full Text :
https://doi.org/10.1007/978-3-319-90596-9_3