Transforming Wikipedia into a Large-Scale Fine-Grained Entity Type Corpus

Abstract

This paper presents WiFiNE, an English corpus annotated with fine-grained entity types. We propose simple but effective heuristics we applied to English Wikipedia to build a large, high quality, annotated corpus. We evaluate the impact of our corpus on the fine-grained entity typing system of Shimaoka et al. (2017), with 2 manually annotated benchmarks, FIGER (GOLD) and ONTONOTES . We report state-of-the-art performances, with a gain of 0.8 micro F1 score on the former dataset and a gain of 2.7 macro F1 score on the latter one, despite the fact that we employ the same quantity of training data used in previous works. We make our corpus available as a resource for future works.

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