Near-duplicate documents exacerbate the problem of information overload. Research in detecting near-duplicates has attracted a lot of attention from both industry and academia. In this paper, we focus on addressing this problem for Vietnamese documents which, to the best of our knowledge, has not been done before. Most of the current algorithms have been designed for English which are not directly applicable to Vietnamese -a monosyllabic language. We propose to combine Charikar's algorithm [2] with a "weighting scheme" and Vietnamese specific features to address the language intricacy. Experimental results indicate that our scheme is effective for detecting near-duplicates in a corpus of Vietnamese documents.

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%).