# DLocRL: A Deep Learning Pipeline for Fine-Grained Location Recognition and Linking in Tweets

arXiv: https://arxiv.org/abs/1901.07005 · HTML: https://mlsanity.com/paper/1901.07005/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jan 2019.

## Built on by (most cited first, 5 of 5)

- [Location reference recognition from texts: A survey and comparison](https://mlsanity.com/paper/2207.01683/) (2022)
- [GEDIT: Geographic-Enhanced and Dependency-Guided Tagging for Joint POI and Accessibility Extraction at Baidu Maps](https://mlsanity.com/paper/2108.09104/) (2021)
- [ArabicNLU 2024: The First Arabic Natural Language Understanding Shared Task](https://mlsanity.com/paper/2407.20663/) (2024)
- [Identification of Fine-Grained Location Mentions in Crisis Tweets](https://mlsanity.com/paper/2111.06334/) (2021)
- [DuIVRS-2: An LLM-based Interactive Voice Response System for Large-scale POI Attribute Acquisition](https://mlsanity.com/paper/2605.17900/) (2026)

## 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.