Large-scale Vietnamese point-of-interest classification using weak labeling

Van Trung Tran; Quang Dao Le; Bao Son Pham; Viet Hung Luu; Quang Hung Bui

doi:10.3389/frai.2022.1020532

Large-scale Vietnamese point-of-interest classification using weak labeling

Front Artif Intell. 2022 Dec 9:5:1020532. doi: 10.3389/frai.2022.1020532. eCollection 2022.

Authors

Van Trung Tran^{1

2}, Quang Dao Le^{1

2}, Bao Son Pham³, Viet Hung Luu^{1

4}, Quang Hung Bui¹

Affiliations

¹ Center of Multidisciplinary Integrated Technologies for Field Monitoring, Vietnam National University of Engineering and Technology, Hanoi, Vietnam.
² NTT Hi-Tech Institute, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam.
³ Faculty of Information Technology, VNU University of Engineering and Technology, Hanoi, Vietnam.
⁴ FIMO, Hanoi, Vietnam.

Abstract

Point-of-Interests (POIs) represent geographic location by different categories (e.g., touristic places, amenities, or shops) and play a prominent role in several location-based applications. However, the majority of POIs category labels are crowd-sourced by the community, thus often of low quality. In this paper, we introduce the first annotated dataset for the POIs categorical classification task in Vietnamese. A total of 750,000 POIs are collected from WeMap, a Vietnamese digital map. Large-scale hand-labeling is inherently time-consuming and labor-intensive, thus we have proposed a new approach using weak labeling. As a result, our dataset covers 15 categories with 275,000 weak-labeled POIs for training, and 30,000 gold-standard POIs for testing, making it the largest compared to the existing Vietnamese POIs dataset. We empirically conduct POI categorical classification experiments using a strong baseline (BERT-based fine-tuning) on our dataset and find that our approach shows high efficiency and is applicable on a large scale. The proposed baseline gives an F1 score of 90% on the test dataset, and significantly improves the accuracy of WeMap POI data by a margin of 37% (from 56 to 93%).

Keywords: BERT-based; crowd-sourcing; point-of-interest; snorkel; weak labeling.