Automatic Extraction of Spatio-Temporal Information from Arabic Text Documents

2015 ◽  
Vol 7 (5) ◽  
pp. 97-107
Author(s):  
Abdelkoui Feriel ◽  
Kholladi Mohamed Khireddine
Keyword(s):  
Temporal Information ◽  
Automatic Extraction ◽  
Arabic Text ◽  
Text Documents ◽  
Spatio Temporal

scholarly journals PhenoRice: A method for automatic extraction of spatio-temporal information on rice crops using satellite data time series

2017 ◽  
Vol 194 ◽  
pp. 347-365 ◽  
Author(s):  
Mirco Boschetti ◽  
Lorenzo Busetto ◽  
Giacinto Manfron ◽  
Alice Laborte ◽  
Sonia Asilo ◽  
...  
Keyword(s):  
Time Series ◽  
Satellite Data ◽  
Temporal Information ◽  
Automatic Extraction ◽  
Spatio Temporal

scholarly journals The Role of Spatio-Temporal Information to Govern the COVID-19 Pandemic: A European Perspective

2021 ◽  
Vol 10 (3) ◽  
pp. 166
Author(s):  
Hartmut Müller ◽  
Marije Louwsma
Keyword(s):  
European Union ◽  
Spatial Distribution ◽  
Spatial Information ◽  
National Level ◽  
Temporal Information ◽  
The European Union ◽  
Member States ◽  
Heavy Burden ◽  
Spatio Temporal

The Covid-19 pandemic put a heavy burden on member states in the European Union. To govern the pandemic, having access to reliable geo-information is key for monitoring the spatial distribution of the outbreak over time. This study aims to analyze the role of spatio-temporal information in governing the pandemic in the European Union and its member states. The European Nomenclature of Territorial Units for Statistics (NUTS) system and selected national dashboards from member states were assessed to analyze which spatio-temporal information was used, how the information was visualized and whether this changed over the course of the pandemic. Initially, member states focused on their own jurisdiction by creating national dashboards to monitor the pandemic. Information between member states was not aligned. Producing reliable data and timeliness reporting was problematic, just like selecting indictors to monitor the spatial distribution and intensity of the outbreak. Over the course of the pandemic, with more knowledge about the virus and its characteristics, interventions of member states to govern the outbreak were better aligned at the European level. However, further integration and alignment of public health data, statistical data and spatio-temporal data could provide even better information for governments and actors involved in managing the outbreak, both at national and supra-national level. The Infrastructure for Spatial Information in Europe (INSPIRE) initiative and the NUTS system provide a framework to guide future integration and extension of existing systems.

MLS: An MAE-aware LiDAR sampling framework for On-road Environments using Spatio-Temporal Information

2021 ◽  
pp. 1-1
Author(s):  
Quan-Dung Pham ◽  
Xuan Truong Nguyen ◽  
Khac-Thai Nguyen ◽  
Hyun Kim ◽  
Hyuk-Jae Lee
Keyword(s):  
Temporal Information ◽  
Spatio Temporal

Latent Topic Model for Indexing Arabic Documents

2014 ◽  
Vol 4 (1) ◽  
pp. 29-45 ◽  
Author(s):  
Rami Ayadi ◽  
Mohsen Maraoui ◽  
Mounir Zrigui
Keyword(s):  
Topic Model ◽  
Inflectional Morphology ◽  
Arabic Text ◽  
Text Representation ◽  
Text Documents ◽  
Latent Topic ◽  
Latent Topics ◽  
F Measure

In this paper, the authors present latent topic model to index and represent the Arabic text documents reflecting more semantics. Text representation in a language with high inflectional morphology such as Arabic is not a trivial task and requires some special treatments. The authors describe our approach for analyzing and preprocessing Arabic text then we describe the stemming process. Finally, the latent model (LDA) is adapted to extract Arabic latent topics, the authors extracted significant topics of all texts, each theme is described by a particular distribution of descriptors then each text is represented on the vectors of these topics. The experiment of classification is conducted on in house corpus; latent topics are learned with LDA for different topic numbers K (25, 50, 75, and 100) then the authors compare this result with classification in the full words space. The results show that performances, in terms of precision, recall and f-measure, of classification in the reduced topics space outperform classification in full words space and when using LSI reduction.

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