Arukikata Travelogue Dataset with Geographic Entity Mention, Coreference, and Link Annotation
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 11 of its 52 references that are in the map. First posted on arXiv 23 May 2023.
Builds on
11 of 52 references.
References · 11
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2020LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attentioncited by 326
- 2017End-to-end Neural Coreference Resolutioncited by 240
- 2019BERT for Coreference Resolution: Baselines and Analysiscited by 125
- 2021mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Modelscited by 19
- 2018A Pragmatic Guide to Geoparsing Evaluationcited by 17
- 2022Location reference recognition from texts: A survey and comparisoncited by 17
- 2018A Coherent Unsupervised Model for Toponym Resolutioncited by 8
- 2023Improving Toponym Resolution with Better Candidate Generation, Transformer-based Reranking, and Two-Stage Resolutioncited by 7
- 2021Towards Consistent Document-level Entity Linking: Joint Models for Entity Linking and Coreference Resolutioncited by 6
- 2023NAIST Academic Travelogue Datasetcited by 1
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Citing papers per year: 2024: 1; 2025: 0; 2026: 1.
2 papers.