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Impact of Entity Graphs on Extracting Semantic Relations
- 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.
- Subjects :
- [ INFO.INFO-TT ] Computer Science [cs]/Document and Text Processing
[ INFO ] Computer Science [cs]
business.industry
Computer science
02 engineering and technology
computer.software_genre
Relationship extraction
Graph
[INFO.INFO-TT]Computer Science [cs]/Document and Text Processing
Global information
020204 information systems
0202 electrical engineering, electronic engineering, information engineering
Open domain
[INFO]Computer Science [cs]
020201 artificial intelligence & image processing
Artificial intelligence
business
computer
Natural language processing
Sentence
Subjects
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