TY  - BOOK
AB  - 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.
DA  - 2018
KW  - Annotation Tool
KW  - Information Extraction
KW  - Slot Filling
LA  - eng
PY  - 2018
TI  - SANTO: A Web-based Annotation Tool for Ontology-driven Slot Filling
UR  - https://nbn-resolving.org/urn:nbn:de:0070-pub-29199239
Y2  - 2024-11-21T18:00:49
ER  -