The Impact of Semantic Linguistic Features in Relation Extraction: A Logical Relational Learning Approach

Rinaldo Lima, Bernard Espinasse, Frederico Freitas


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).
Anthology ID:
R19-1076
Volume:
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
Month:
September
Year:
2019
Address:
Varna, Bulgaria
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
648–654
Language:
URL:
https://aclanthology.org/R19-1076
DOI:
10.26615/978-954-452-056-4_076
Bibkey:
Cite (ACL):
Rinaldo Lima, Bernard Espinasse, and Frederico Freitas. 2019. The Impact of Semantic Linguistic Features in Relation Extraction: A Logical Relational Learning Approach. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 648–654, Varna, Bulgaria. INCOMA Ltd..
Cite (Informal):
The Impact of Semantic Linguistic Features in Relation Extraction: A Logical Relational Learning Approach (Lima et al., RANLP 2019)
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PDF:
https://aclanthology.org/R19-1076.pdf