COCO-EX: A Tool for Linking Concepts from Texts to ConceptNet

Maria Becker, Katharina Korfhage, Anette Frank


Abstract
In this paper we present COCO-EX, a tool for Extracting Concepts from texts and linking them to the ConceptNet knowledge graph. COCO-EX extracts meaningful concepts from natural language texts and maps them to conjunct concept nodes in ConceptNet, utilizing the maximum of relational information stored in the ConceptNet knowledge graph. COCOEX takes into account the challenging characteristics of ConceptNet, namely that – unlike conventional knowledge graphs – nodes are represented as non-canonicalized, free-form text. This means that i) concepts are not normalized; ii) they often consist of several different, nested phrase types; and iii) many of them are uninformative, over-specific, or misspelled. A commonly used shortcut to circumvent these problems is to apply string matching. We compare COCO-EX to this method and show that COCO-EX enables the extraction of meaningful, important rather than overspecific or uninformative concepts, and allows to assess more relational information stored in the knowledge graph.
Anthology ID:
2021.eacl-demos.15
Volume:
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations
Month:
April
Year:
2021
Address:
Online
Editors:
Dimitra Gkatzia, Djamé Seddah
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
119–126
Language:
URL:
https://aclanthology.org/2021.eacl-demos.15
DOI:
10.18653/v1/2021.eacl-demos.15
Bibkey:
Cite (ACL):
Maria Becker, Katharina Korfhage, and Anette Frank. 2021. COCO-EX: A Tool for Linking Concepts from Texts to ConceptNet. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations, pages 119–126, Online. Association for Computational Linguistics.
Cite (Informal):
COCO-EX: A Tool for Linking Concepts from Texts to ConceptNet (Becker et al., EACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.eacl-demos.15.pdf
Data
CommonsenseQAConceptNetOpenBookQA