@inproceedings{yamamoto-etal-2025-graph,
title = "Graph-Structured Trajectory Extraction from Travelogues",
author = "Yamamoto, Aitaro and
Otomo, Hiroyuki and
Ouchi, Hiroki and
Higashiyama, Shohei and
Teranishi, Hiroki and
Shindo, Hiroyuki and
Watanabe, Taro",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.690/",
doi = "10.18653/v1/2025.acl-long.690",
pages = "14116--14132",
ISBN = "979-8-89176-251-0",
abstract = "Human traveling trajectories play a central role in characterizing each travelogue, and automatic trajectory extraction from travelogues is highly desired for tourism services, such as travel planning and recommendation. This work addresses the extraction of human traveling trajectories from travelogues. Previous work treated each trajectory as a sequence of visited locations, although locations with different granularity levels, e.g., ``Kyoto City'' and ``Kyoto Station,'' should not be lined up in a sequence. In this work, we propose to represent the trajectory as a graph that can capture the hierarchy as well as the visiting order, and construct a benchmark dataset for the trajectory extraction. The experiments using this dataset show that even naive baseline systems can accurately predict visited locations and the visiting order between them, while it is more challenging to predict the hierarchical relations."
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<abstract>Human traveling trajectories play a central role in characterizing each travelogue, and automatic trajectory extraction from travelogues is highly desired for tourism services, such as travel planning and recommendation. This work addresses the extraction of human traveling trajectories from travelogues. Previous work treated each trajectory as a sequence of visited locations, although locations with different granularity levels, e.g., “Kyoto City” and “Kyoto Station,” should not be lined up in a sequence. In this work, we propose to represent the trajectory as a graph that can capture the hierarchy as well as the visiting order, and construct a benchmark dataset for the trajectory extraction. The experiments using this dataset show that even naive baseline systems can accurately predict visited locations and the visiting order between them, while it is more challenging to predict the hierarchical relations.</abstract>
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%0 Conference Proceedings
%T Graph-Structured Trajectory Extraction from Travelogues
%A Yamamoto, Aitaro
%A Otomo, Hiroyuki
%A Ouchi, Hiroki
%A Higashiyama, Shohei
%A Teranishi, Hiroki
%A Shindo, Hiroyuki
%A Watanabe, Taro
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F yamamoto-etal-2025-graph
%X Human traveling trajectories play a central role in characterizing each travelogue, and automatic trajectory extraction from travelogues is highly desired for tourism services, such as travel planning and recommendation. This work addresses the extraction of human traveling trajectories from travelogues. Previous work treated each trajectory as a sequence of visited locations, although locations with different granularity levels, e.g., “Kyoto City” and “Kyoto Station,” should not be lined up in a sequence. In this work, we propose to represent the trajectory as a graph that can capture the hierarchy as well as the visiting order, and construct a benchmark dataset for the trajectory extraction. The experiments using this dataset show that even naive baseline systems can accurately predict visited locations and the visiting order between them, while it is more challenging to predict the hierarchical relations.
%R 10.18653/v1/2025.acl-long.690
%U https://aclanthology.org/2025.acl-long.690/
%U https://doi.org/10.18653/v1/2025.acl-long.690
%P 14116-14132
Markdown (Informal)
[Graph-Structured Trajectory Extraction from Travelogues](https://aclanthology.org/2025.acl-long.690/) (Yamamoto et al., ACL 2025)
ACL
- Aitaro Yamamoto, Hiroyuki Otomo, Hiroki Ouchi, Shohei Higashiyama, Hiroki Teranishi, Hiroyuki Shindo, and Taro Watanabe. 2025. Graph-Structured Trajectory Extraction from Travelogues. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 14116–14132, Vienna, Austria. Association for Computational Linguistics.