The Impact of Semantic Linguistic Features in Relation Extraction: A Logical Relational Learning Approach
Rinaldo J. Lima, Bernard Espinasse, LIS UMR CNRS 7020, Aix-Marseille Université/Université de Toulon, France, Fred Freitas · 2019
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 semanticbased 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).