Automatic Extraction of Spatio-Temporal Information from Arabic Text Documents

Feriel, Abdelkoui; Kholladi, Mohamed-Khireddine

doi:10.5121/ijcsit.2015.7507

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…To extract location-named entities from informal texts like those seen on social media sites, many models have been developed [1], [2], [3], [4], [5]. While other languages have gotten comparatively less attention, those models were created to simply apply to the English text.…”

Section: Rezaei Et Al (2022)mentioning

confidence: 99%

“…Obtaining data on behaviors affected by shifting geographies is one promising study area, allowing researchers to make focused, well-informed judgments [1]. These unstructured Arabic text documents are a significant source of such data, and we specifically want to extract geographic information from them [2]. However, most of this data is not provided in an obvious manner and must be retrieved using different NLP techniques [3].…”

Section: Rezaei Et Al (2022)mentioning

confidence: 99%

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Arabic Location Named Entity Recognition for Tweets using a Deep Learning Approach

Alzaidi¹,

Abushark²,

Khan³

2022

IJACSA

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Social media sites like Twitter have emerged in recent years as a major data source utilized in a variety of disciplines, including economics, politics, and scientific study. To extract pertinent data for decision-making and behavioral analysis, one can use Twitter data. To extract event location names and entities from colloquial Arabic texts using deep learning techniques, this study proposed Named Entity Recognition (NER) and Linking (NEL) models. Google Maps was also used to obtain up-to-date details for each extracted site and link them to the geographical coordination. Our method was able to predict 40% and 48% of the locations of tweets at the regional and city levels, respectively, while the F-measure was able to reliably identify and detect 63% of the locations of tweets at a single Point of Interest.

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Section: Rezaei Et Al (2022)mentioning

confidence: 99%

Section: Rezaei Et Al (2022)mentioning

confidence: 99%

Arabic Location Named Entity Recognition for Tweets using a Deep Learning Approach

Alzaidi¹,

Abushark²,

Khan³

2022

IJACSA

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Automatic Classification for Representative Spatio-temporal-Based Event Document Using Machine Learning

Kim

Yang

Park

et al. 2023

Lecture Notes in Electrical Engineering

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A Convolution Neural Network-Based Representative Spatio-Temporal Documents Classification for Big Text Data

et al. 2022

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With the proliferation of mobile devices, the amount of social media users and online news articles are rapidly increasing, and text information online is accumulating as big data. As spatio-temporal information becomes more important, research on extracting spatiotemporal information from online text data and utilizing it for event analysis is being actively conducted. However, if spatiotemporal information that does not describe the core subject of a document is extracted, it is rather difficult to guarantee the accuracy of core event analysis. Therefore, it is important to extract spatiotemporal information that describes the core topic of a document. In this study, spatio-temporal information describing the core topic of a document is defined as ‘representative spatio-temporal information’, and documents containing representative spatiotemporal information are defined as ‘representative spatio-temporal documents’. We proposed a character-level Convolution Neuron Network (CNN)-based document classifier to classify representative spatio-temporal documents. To train the proposed CNN model, 7400 training data were constructed for representative spatio-temporal documents. The experimental results show that the proposed CNN model outperforms traditional machine learning classifiers and existing CNN-based classifiers.

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Automatic Extraction of Spatio-Temporal Information from Arabic Text Documents

Cited by 3 publications

References 8 publications

Arabic Location Named Entity Recognition for Tweets using a Deep Learning Approach

Arabic Location Named Entity Recognition for Tweets using a Deep Learning Approach

Automatic Classification for Representative Spatio-temporal-Based Event Document Using Machine Learning

A Convolution Neural Network-Based Representative Spatio-Temporal Documents Classification for Big Text Data

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