The usage of Named Entity Recognition tools on domain specific corpora is often hampered by insufficient training data. We investigate an approach to produce fine-grained named entity annotations from a small training dataset to later apply it on a large corpus of Austrian court decisions. We apply general purpose Named Entity Recognition model to produce annotations of common coarse-grained types. Next, a small sample of these annotations are manually annotated by domain experts to produce initial fine-grained training datset.
In purpose of Carefree real property (Crp) a prototype has been created which is used as a proof of execution demonstrating implementation approach to software vendors working for brokers, mortgage lenders and other Crp-participants.
The prototype gathers information from authoritative sources and generates a legal document about the purchase of the real estate. By embedding the original information, including provenance information and trust (signing) data, an innovated solution has been created for the digital processing of the legal documents.