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The Impact of Semantic Linguistic Features in Relation Extraction: A Logical Relational Learning Approach
- Source :
- Recent Advances in Natural Language Processing, Recent Advances in Natural Language Processing, 2019, varna, Bulgaria. pp.648-654, ⟨10.26615/978-954-452-056-4_076⟩, RANLP
- Publication Year :
- 2019
- Publisher :
- HAL CCSD, 2019.
-
Abstract
- Relation Extraction (RE) consists in detecting and classifying semantic relations between entities in a sentence. The vast majority of the state-of-the-art RE systems relies on morphosyntactic features and supervised machine learning algorithms. This paper tries to answer important questions concerning both the impact of semantic based features, and the integration of external linguistic knowledge resources on RE performance. For that, a RE system based on a logical and relational learning algorithm was used and evaluated on three reference datasets from two distinct domains. The yielded results confirm that the classifiers induced using the proposed richer feature set outperformed the classifiers built with morphosyntactic features in average 4% (F1-measure).
- Subjects :
- business.industry
Computer science
Statistical relational learning
computer.software_genre
Relationship extraction
Linguistics
[INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Logical conjunction
Artificial intelligence
Feature set
business
computer
Natural language processing
Sentence
ComputingMilieux_MISCELLANEOUS
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- Recent Advances in Natural Language Processing, Recent Advances in Natural Language Processing, 2019, varna, Bulgaria. pp.648-654, ⟨10.26615/978-954-452-056-4_076⟩, RANLP
- Accession number :
- edsair.doi.dedup.....e20b06985738083dff2e54f93eefc9be