SANTO: A Web-based Annotation Tool for Ontology-driven Slot Filling

Matthias Hartung, Hendrik ter Horst, Frank Grimm, Tim Diekmann, Roman Klinger, Philipp Cimiano


Abstract
Supervised machine learning algorithms require training data whose generation for complex relation extraction tasks tends to be difficult. Being optimized for relation extraction at sentence level, many annotation tools lack in facilitating the annotation of relational structures that are widely spread across the text. This leads to non-intuitive and cumbersome visualizations, making the annotation process unnecessarily time-consuming. We propose SANTO, an easy-to-use, domain-adaptive annotation tool specialized for complex slot filling tasks which may involve problems of cardinality and referential grounding. The web-based architecture enables fast and clearly structured annotation for multiple users in parallel. Relational structures are formulated as templates following the conceptualization of an underlying ontology. Further, import and export procedures of standard formats enable interoperability with external sources and tools.
Anthology ID:
P18-4012
Volume:
Proceedings of ACL 2018, System Demonstrations
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
68–73
URL:
https://www.aclweb.org/anthology/P18-4012
DOI:
10.18653/v1/P18-4012
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PDF:
https://www.aclweb.org/anthology/P18-4012.pdf