DLocRL: A Deep Learning Pipeline for Fine-Grained Location Recognition and Linking in Tweets
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Abstract (arXiv)
In recent years, with the prevalence of social media and smart devices, people causally reveal their locations such as shops, hotels, and restaurants in their tweets. Recognizing and linking such fine-grained location mentions to well-defined location profiles are beneficial for retrieval and recommendation systems. In this paper, we propose DLocRL, a new deep learning pipeline for fine-grained location recognition and linking in tweets, and verify its effectiveness on a real-world Twitter dataset.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Jan 2019.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 0; 2024: 1; 2025: 0; 2026: 1.
5 papers.
- 2022Location reference recognition from texts: A survey and comparisoncited by 17
- 2021GEDIT: Geographic-Enhanced and Dependency-Guided Tagging for Joint POI and Accessibility Extraction at Baidu Mapscited by 4
- 2024ArabicNLU 2024: The First Arabic Natural Language Understanding Shared Taskcited by 2
- 2021Identification of Fine-Grained Location Mentions in Crisis Tweetscited by 1
- 2026DuIVRS-2: An LLM-based Interactive Voice Response System for Large-scale POI Attribute Acquisitioncited by 0
- 2026DuIVRS-2: An LLM-based Interactive Voice Response System for Large-scale POI Attribute Acquisition18 May 2026
- 2024ArabicNLU 2024: The First Arabic Natural Language Understanding Shared Task30 Jul 2024
- 2022Location reference recognition from texts: A survey and comparison4 Jul 2022
- 2021Identification of Fine-Grained Location Mentions in Crisis Tweets11 Nov 2021
- 2021GEDIT: Geographic-Enhanced and Dependency-Guided Tagging for Joint POI and Accessibility Extraction at Baidu Maps20 Aug 2021