Kovalchuk, M. A.; Filatova, A.; Korneev, A.; Koreneva, M.; Voskresenskii, A.; Nasonov, D.; Boukhanovsky, A. SemConvTree: semantic convolutional quadtrees for multi-scale event detection in Smart City. Preprints2024, 2024071247. https://doi.org/10.20944/preprints202407.1247.v1
APA Style
Kovalchuk, M. A., Filatova, A., Korneev, A., Koreneva, M., Voskresenskii, A., Nasonov, D., & Boukhanovsky, A. (2024). SemConvTree: semantic convolutional quadtrees for multi-scale event detection in Smart City. Preprints. https://doi.org/10.20944/preprints202407.1247.v1
Chicago/Turabian Style
Kovalchuk, M. A., Denis Nasonov and Alexander Boukhanovsky. 2024 "SemConvTree: semantic convolutional quadtrees for multi-scale event detection in Smart City" Preprints. https://doi.org/10.20944/preprints202407.1247.v1
Abstract
The digital world is increasingly invading our reality, which leads to the formation of a significant reflection of the processes and activities taking place in the smart city. Such activities include well-known urban events, celebrations, and those with a very local character. Due to the mass occurrence, events have a comparable influence on the formation of the spirit and the urban atmosphere. This work presents an enhanced semantic version of the ConvTree algorithm - SemConvTree. It allows considering the semantic component of the data obtained by using semi-supervised learning of topic modeling ensemble (consisting of improved models BERTopic, TSB-ARTM, SBert-Zero-Shot). We also present an improved event search algorithm based on both statistical evaluations and semantic analysis of posts. This algorithm allows fine-tuning the mechanism of discovering the required entities with the specified particularity (such as a particular topic). Experimental studies were conducted within the area of New York City. They showed an improvement in the detection of posts devoted to events (about 40% higher f1-score) due to the accurate handling of events of different scales. These results lead in the long term to talk about the potential perspective in creating a semantic platform for the analysis and monitoring of urban events in the future.
Keywords
Event Detection; Geo Gridsp; Natural Language Processing; Information Retrieval; Neural Networks
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
