The Impact of Semantic Linguistic Features in Relation Extraction: A Logical Relational Learning Approach
| Autor: | Bernard Espinasse, Rinaldo Lima, Frederico Luiz Gonçalves de Freitas |
|---|---|
| Přispěvatelé: | Recherche d’information et Interactions (R2I), Laboratoire d'Informatique et Systèmes (LIS), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS) |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
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 |
| Zdroj: | 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 |
| DOI: | 10.26615/978-954-452-056-4_076⟩ |
| Popis: | 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). |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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