A Unified Labeling Approach by Pooling Diverse Datasets for Entity Typing

Evolution of entity typing (ET) has led to the generation of multiple datasets. These datasets span from being coarse-grained to fine-grained encompassing numerous domains. Existing works primarily focus on improving the performance of a model on an individual dataset, independently. This narrowly focused view of ET causes two issues: 1) type assignment when information about the test data domain or target label set is not available; 2) fine-grained type prediction when there is no dataset in the same domain with finer-type annotations. Our goal is to shift the focus from individual domain-specific datasets to all the datasets available for ET. In our proposed approach, we convert the label set of all datasets to a unified hierarchical label set while preserving the semantic properties of the individual labels. Then utilizing a partial label loss, we train a single neural network based classifier using every available dataset for the ET task. We empirically evaluate the effectiveness of our approach on seven real-world diverse ET datasets. The results convey that the combined training on multiple datasets helps the model to generalize better and to predict fine-types across all domains without relying on a specific domain or label set information during evaluation.