Arukikata Travelogue Dataset with Geographic Entity Mention, Coreference, and Link Annotation

• URL: http://arxiv.org/abs/2305.13844v1
• Date: Tue, 23 May 2023 09:07:42 GMT
• Title: Arukikata Travelogue Dataset with Geographic Entity Mention, Coreference, and Link Annotation
• Authors: Shohei Higashiyama, Hiroki Ouchi, Hiroki Teranishi, Hiroyuki Otomo, Yusuke Ide, Aitaro Yamamoto, Hiroyuki Shindo, Yuki Matsuda, Shoko Wakamiya, Naoya Inoue, Ikuya Yamada, Taro Watanabe
• Abstract summary: We focus on document-level geoparsing, which considers geographic relatedness among geo-entity mentions. We present a Japanese travelogue dataset designed for evaluating document-level geoparsing systems.
• Score: 25.3010708569682
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Geoparsing is a fundamental technique for analyzing geo-entity information in text. We focus on document-level geoparsing, which considers geographic relatedness among geo-entity mentions, and presents a Japanese travelogue dataset designed for 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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