Investigative Data Journalism Conference

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Natural language processing

Dr. Tamaro Green

Natural language processing

2020-11-12

Natural language processing may provide tools to support machine learning and artificial intelligence (Alarie, Niblett, & Yoon, 2018).  Alarie et al. (2018) explain how natural language processing can be combined with machine learning to assist lawyers in the legal profession.  Li et al. (2016) describes the challenges in connecting tools for data processing as inefficient data transfer, data representation differences, and data privacy.

Natural language process can also be applied for digital forensics (Portnoff et al., 2017).  Portnoff et al. (2017) apply natural language processing and machine learning to digital forensics. Athamnah, Alazzawe, and Kant (2018) explain that natural language processing is implemented based on the analysis of features. Wang et al. (2016) explains how natural language processing requires large amounts of data processing for deep learning.  Fill and Cudré-Mauroux (2017) explain how natural language processing can be implemented for building recommendation algorithms.

Schildt (2016) explains that as natural language processing advances, there will be more tools for collaboration and sharing knowledge across organizations. Lettieri et al. (2018) share the example of natural language processing being combined with network analysis and artificial intelligence to build a knowledge management system.

 

Alarie, B., Niblett, A., & Yoon, A. H. (2018). How artificial intelligence will affect the practice of law. University of Toronto Law Journal, 68(supplement 1), 106-124. doi:10.3138/utlj.2017-0052

Athamnah, M., Alazzawe, A., & Kant, K. (2018). Collaborative similarity search across multi-party repositories. Paper presented at the Proceedings of the 19th International Conference on Distributed Computing and Networking - ICDCN '18.

Fill, H.-G., & Cudré-Mauroux, P. (2017). SeMFIS: A flexible engineering platform for semantic annotations of conceptual models. Semantic Web, 8(5), 747-763. doi:10.3233/sw-160235

Lettieri, N., Altamura, A., Giugno, R., Guarino, A., Malandrino, D., Pulvirenti, A., . . . Zaccagnino, R. (2018). Ex machina: Analytical platforms, law and the challenges of computational legal science. Future Internet, 10(5). doi:10.3390/fi10050037

Li, K., Zhou, X., Wang, D. Z., Grant, C., Dobra, A., & Dudley, C. (2016). In-database batch and query-time inference over probabilistic graphical models using UDA–GIST. The VLDB Journal, 26(2), 177-201. doi:10.1007/s00778-016-0446-1

Portnoff, R. S., Afroz, S., Durrett, G., Kummerfeld, J. K., Berg-Kirkpatrick, T., McCoy, D., . . . Paxson, V. (2017). Tools for automated analysis of cybercriminal markets. Paper presented at the Proceedings of the 26th International Conference on World Wide Web - WWW '17.

Schildt, H. (2016). Big data and organizational design – the brave new world of algorithmic management and computer augmented transparency. Innovation, 19(1), 23-30. doi:10.1080/14479338.2016.1252043

Wang, S., Liao, X., Fan, X., Jin, H., Yao, Q., & Zhang, Y. (2016). Automatically setting parameter-exchanging interval for deep learning. Mobile Networks and Applications, 22(2), 186-194. doi:10.1007/s11036-016-0740-6



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IDJC 2020

International Data Journalism Conference

IDJC 2020

International Data Journalism Conference

IDJC 2020

International Data Journalism Conference