A representation learning method is considered stable if it consistently generates similar representation of the given data across multiple runs.
Further, to address the second question, we canonicalize, filter, and combine the identified relations from the three resources to construct a taxonomical hierarchy.
Fine-grained Entity Recognition (FgER) is the task of detecting and classifying entity mentions to a large set of types spanning diverse domains such as biomedical, finance and sports.
The CLF first creates a unified hierarchical label set (UHLS) and a label mapping by aggregating label information from all available datasets.
Fine-grained entity type classification (FETC) is the task of classifying an entity mention to a broad set of types.