@inproceedings{higashiyama-etal-2024-arukikata,
title = "Arukikata Travelogue Dataset with Geographic Entity Mention, Coreference, and Link Annotation",
author = "Higashiyama, Shohei and
Ouchi, Hiroki and
Teranishi, Hiroki and
Otomo, Hiroyuki and
Ide, Yusuke and
Yamamoto, Aitaro and
Shindo, Hiroyuki and
Matsuda, Yuki and
Wakamiya, Shoko and
Inoue, Naoya and
Yamada, Ikuya and
Watanabe, Taro",
editor = "Graham, Yvette and
Purver, Matthew",
booktitle = "Findings of the Association for Computational Linguistics: EACL 2024",
month = mar,
year = "2024",
address = "St. Julian{'}s, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-eacl.35",
pages = "513--532",
abstract = "Geoparsing is a fundamental technique for analyzing geo-entity information in text, which is useful for geographic applications, e.g., tourist spot recommendation. We focus on document-level geoparsing that considers geographic relatedness among geo-entity mentions and present a Japanese travelogue dataset designed for training and evaluating document-level geoparsing systems. Our dataset comprises 200 travelogue documents with rich geo-entity information: 12,171 mentions, 6,339 coreference clusters, and 2,551 geo-entities linked to geo-database entries.",
}
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%0 Conference Proceedings
%T Arukikata Travelogue Dataset with Geographic Entity Mention, Coreference, and Link Annotation
%A Higashiyama, Shohei
%A Ouchi, Hiroki
%A Teranishi, Hiroki
%A Otomo, Hiroyuki
%A Ide, Yusuke
%A Yamamoto, Aitaro
%A Shindo, Hiroyuki
%A Matsuda, Yuki
%A Wakamiya, Shoko
%A Inoue, Naoya
%A Yamada, Ikuya
%A Watanabe, Taro
%Y Graham, Yvette
%Y Purver, Matthew
%S Findings of the Association for Computational Linguistics: EACL 2024
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F higashiyama-etal-2024-arukikata
%X Geoparsing is a fundamental technique for analyzing geo-entity information in text, which is useful for geographic applications, e.g., tourist spot recommendation. We focus on document-level geoparsing that considers geographic relatedness among geo-entity mentions and present a Japanese travelogue dataset designed for training and evaluating document-level geoparsing systems. Our dataset comprises 200 travelogue documents with rich geo-entity information: 12,171 mentions, 6,339 coreference clusters, and 2,551 geo-entities linked to geo-database entries.
%U https://aclanthology.org/2024.findings-eacl.35
%P 513-532
Markdown (Informal)
[Arukikata Travelogue Dataset with Geographic Entity Mention, Coreference, and Link Annotation](https://aclanthology.org/2024.findings-eacl.35) (Higashiyama et al., Findings 2024)
ACL
- Shohei Higashiyama, Hiroki Ouchi, Hiroki Teranishi, Hiroyuki Otomo, Yusuke Ide, Aitaro Yamamoto, Hiroyuki Shindo, Yuki Matsuda, Shoko Wakamiya, Naoya Inoue, Ikuya Yamada, and Taro Watanabe. 2024. Arukikata Travelogue Dataset with Geographic Entity Mention, Coreference, and Link Annotation. In Findings of the Association for Computational Linguistics: EACL 2024, pages 513–532, St. Julian’s, Malta. Association for Computational Linguistics.