@inproceedings{lee-etal-2020-chinese,
title = "A {C}hinese Corpus for Fine-grained Entity Typing",
author = "Lee, Chin and
Dai, Hongliang and
Song, Yangqiu and
Li, Xin",
booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.548",
pages = "4451--4457",
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.",
language = "English",
ISBN = "979-10-95546-34-4",
}
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<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.</abstract>
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%0 Conference Proceedings
%T A Chinese Corpus for Fine-grained Entity Typing
%A Lee, Chin
%A Dai, Hongliang
%A Song, Yangqiu
%A Li, Xin
%S Proceedings of the 12th Language Resources and Evaluation Conference
%D 2020
%8 may
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-34-4
%G English
%F lee-etal-2020-chinese
%X 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.
%U https://aclanthology.org/2020.lrec-1.548
%P 4451-4457
Markdown (Informal)
[A Chinese Corpus for Fine-grained Entity Typing](https://aclanthology.org/2020.lrec-1.548) (Lee et al., LREC 2020)
ACL
- Chin Lee, Hongliang Dai, Yangqiu Song, and Xin Li. 2020. A Chinese Corpus for Fine-grained Entity Typing. In Proceedings of the 12th Language Resources and Evaluation Conference, pages 4451–4457, Marseille, France. European Language Resources Association.