An exploration of ethnic minorities' needs for multilingual information access of public digital cultural services

Wu, Dan; Fan, Shu; Yao, Shengyi; Xu, Shuang

doi:10.1108/jd-12-2021-0238

Cited by 5 publications

(1 citation statement)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Comparing these two NER models, the XLMR model performed better on the criminal, victim, date/time, action, and root cause labels, while the WanchanBERTa model outperformed the other on the police, location, and worth labels in terms of F1-score. Since XLMR has a marginally better performance than WanchanBERTa and can support diverse languages due to being cross-lingual, facilitating applications that need multilingual information access and processing [61], it was chosen for integration into the system. It is interesting to note that while BiLSTM-CRF has been a popular choice for NER tasks in low-resource language settings [62,63,64], this has been proved otherwise for the Thai language, especially in our case study.…”

Section: ) Model Selectionmentioning

confidence: 99%

CAMELON: A System for Crime Metadata Extraction and Spatiotemporal Visualization From Online News Articles

Pongpaichet,

Sukosit,

Duangtanawat

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Crimes result in not only loss to individuals but also hinder national economic growth. While crime rates have been reported to decrease in developed countries, underdeveloped and developing nations still suffer from prevalent crimes, especially those undergoing rapid expansion of urbanization. The ability to monitor and assess trends of different types of crimes at both regional and national levels could assist local police and national-level policymakers in proactively devising means to prevent and address the root causes of criminal incidents. Furthermore, such a system could prove useful to individuals seeking to evaluate criminal activity for purposes of travel, investment, and relocation decisions. Recent literature has opted for utilizing online news articles as a reliable and timely source for crime activity information. However, most of the crime monitoring systems fueled by such news sources merely classified crimes into different types and visualized individual crimes on the map using extracted geolocations, lacking crucial information for stakeholders to make relevant, informed decisions. To better serve the unique needs of the target user groups, this paper proposes a novel comprehensive crime visualization system that mines relevant information from large-scale online news articles. The system features automatic crime-type classification and metadata extraction from news articles. The crime classification and metadata schemes are designed to serve the need for information from law enforcement and policymakers, as well as general users. Novel interactive spatiotemporal designs are integrated into the system with the ability to assess the severity and intensity of crimes in each region through the novel Criminometer index. The system is designed to be generalized for implementation in different countries with diverse prevalent crime types and languages composing the news articles, owing to the use of deep learning cross-lingual language models. The experiment results reveal that the proposed system yielded 86%, 51%, and 67% F1 in crime type classification, metadata extraction, and closed-form metadata extraction tasks, respectively. Additionally, the results of the system usability tests indicated a notable level of contentment among the target user groups. The findings not only offer insights into the possible applications of interactive spatiotemporal crime visualization tools for proactive policymaking and predictive policing but also serve as a foundation for future research that utilizes online news articles for intelligent monitoring of real-world phenomena.

show abstract