A Chinese Corpus for Fine-grained Entity Typing

TL;DR

This paper introduces a new Chinese dataset for fine-grained entity typing, including 4,800 manually labeled mentions and a categorization into 10 general types, enabling better NLP applications in Chinese.

Contribution

The paper provides the first Chinese fine-grained entity typing dataset with manual annotations and explores neural models and cross-lingual transfer learning for improved performance.

Findings

Neural models achieve promising results on the dataset.

Cross-lingual transfer learning enhances Chinese entity typing.

The dataset facilitates future research in Chinese NLP.

Abstract

Fine-grained entity typing is a challenging task with wide applications. However, most existing datasets for this task are in English. In this paper, we introduce a corpus for Chinese fine-grained entity typing that contains 4,800 mentions manually labeled through crowdsourcing. Each mention is annotated with free-form entity types. To make our dataset useful in more possible scenarios, we also categorize all the fine-grained types into 10 general types. Finally, we conduct experiments with some neural models whose structures are typical in fine-grained entity typing and show how well they perform on our dataset. We also show the possibility of improving Chinese fine-grained entity typing through cross-lingual transfer learning.