mlsanityData to 7 Oct 2026

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

arXiv 2305.13844 ↗First posted 23 May 2023cs.CLcited by 2

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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

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  2. 2020LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attentioncited by 326
  3. 2017End-to-end Neural Coreference Resolutioncited by 240
  4. 2019BERT for Coreference Resolution: Baselines and Analysiscited by 125
  5. 2021mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Modelscited by 19
  6. 2018A Pragmatic Guide to Geoparsing Evaluationcited by 17
  7. 2022Location reference recognition from texts: A survey and comparisoncited by 17
  8. 2018A Coherent Unsupervised Model for Toponym Resolutioncited by 8
  9. 2023Improving Toponym Resolution with Better Candidate Generation, Transformer-based Reranking, and Two-Stage Resolutioncited by 7
  10. 2021Towards Consistent Document-level Entity Linking: Joint Models for Entity Linking and Coreference Resolutioncited by 6
  11. 2023NAIST Academic Travelogue Datasetcited by 1

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2 papers.