@inproceedings{dasgupta-etal-2018-automatic,
title = "Automatic Curation and Visualization of Crime Related Information from Incrementally Crawled Multi-source News Reports",
author = "Dasgupta, Tirthankar and
Dey, Lipika and
Saha, Rupsa and
Naskar, Abir",
editor = "Zhao, Dongyan",
booktitle = "Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations",
month = aug,
year = "2018",
address = "Santa Fe, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/C18-2023",
pages = "103--107",
abstract = "In this paper, we demonstrate a system for the automatic extraction and curation of crime-related information from multi-source digitally published News articles collected over a period of five years. We have leveraged the use of deep convolution recurrent neural network model to analyze crime articles to extract different crime related entities and events. The proposed methods are not restricted to detecting known crimes only but contribute actively towards maintaining an updated crime ontology. We have done experiments with a collection of 5000 crime-reporting News articles span over time, and multiple sources. The end-product of our experiments is a crime-register that contains details of crime committed across geographies and time. This register can be further utilized for analytical and reporting purposes.",
}
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%0 Conference Proceedings
%T Automatic Curation and Visualization of Crime Related Information from Incrementally Crawled Multi-source News Reports
%A Dasgupta, Tirthankar
%A Dey, Lipika
%A Saha, Rupsa
%A Naskar, Abir
%Y Zhao, Dongyan
%S Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico
%F dasgupta-etal-2018-automatic
%X In this paper, we demonstrate a system for the automatic extraction and curation of crime-related information from multi-source digitally published News articles collected over a period of five years. We have leveraged the use of deep convolution recurrent neural network model to analyze crime articles to extract different crime related entities and events. The proposed methods are not restricted to detecting known crimes only but contribute actively towards maintaining an updated crime ontology. We have done experiments with a collection of 5000 crime-reporting News articles span over time, and multiple sources. The end-product of our experiments is a crime-register that contains details of crime committed across geographies and time. This register can be further utilized for analytical and reporting purposes.
%U https://aclanthology.org/C18-2023
%P 103-107
Markdown (Informal)
[Automatic Curation and Visualization of Crime Related Information from Incrementally Crawled Multi-source News Reports](https://aclanthology.org/C18-2023) (Dasgupta et al., COLING 2018)
ACL