Annotating Entities with Fine-Grained Types in Austrian Court Decisions

Abstract

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 of a large corpus of Austrian court decisions from a small manually annotated training data set. We apply a general purpose Named Entity Recognition model to produce annotations of common coarse-grained types. Next, a small sample of these annotations are manually inspected by domain experts to produce an initial fine-grained training data set. To efficiently use the small manually annotated data set we formulate the task of named entity typing as a binary classification task – for each originally annotated occurrence of an entity, and for each fine-grained type we verify if the entity belongs to it. For this purpose we train a transformer-based classifier. We randomly sample 547 predictions and evaluate them manually. The incorrect predictions are used to improve the performance of the classifier – the corrected annotations are added to the training set. The experiments show that re-training with even a very small number (5 or 10) of originally incorrect predictions can significantly improve the classifier performance. We finally train the classifier on all available data and re-annotate the whole data set.

Learning Ontology Classes from Text by Clustering Lexical Substitutes Derived from Language Models1

“…The resulting ontologies can be useful for creating data models or powering search applications, among myriad other applications. In particular, the use of domain-specific ontologies in enabling knowledge-based transfer learning in information extraction systems (e.g., [14,27]) is a promising method for industrial applications. For these and other applications, ontology learning has been approached from different angles, as reviewed in Section 2.…”