We propose a new approach to the task of fine grained entity type classifications based on label embeddings that allows for information sharing among related labels. Specifically, we learn an embedding for each label and each feature such that labels which frequently co-occur are close in the embedded space. We show that it outperforms state-of-the-art methods on two fine grained entity-classification benchmarks and that the model can exploit the finer-grained labels to improve classification of standard coarse types.
REF proposed an embedding representation for userdefined features and labels, which facilitates information sharing among labels and reduces the dependence on the labels observed in the training set.
Embedding Methods for Fine Grained Entity Type Classification
ACL
Computer Science