Annotated Bibliography – Artificial Intelligence and Language


Annotated Bibliography – Artificial Intelligence and Language

Nadkarni, P. M., Ohno-Machado, L., & Chapman, W. W. (2011). Natural language processing: an introduction. Journal of the American Medical Informatics Association, 18(5), 544-551.

This article is written with the aim of offering an overview of natural language processing (NLP) for medical professionals. The particular focus is to round up information pointing at the state-of-the-art in the design of NLP systems as well as the principles behind the subject. The article is in the form of an academic article published in a journal. The authors are renown academicians affiliated with major academic institutions including Yale. In order to achieve their objective, the authors first offer a historical perspective of NLP including the problems that have been encountered during its development.

In addition, the authors highlight the extent of use of NLP in the medical profession and potential advances in the same area. The authors further discuss the modern architecture used in the design of NLP systems. In a technical approach, the authors also describe the Apache Foundation’s Unstructured Information Management Architecture as a tool for designing NLP. The article then ends with a section on the future of NLP and impact that IBM would have on such a future.

Pedro, V., 2016. Techcrunch Is Now A Part Of Verizon Media. [online] Available at: <> [Accessed 20 October 2020].

This article is written by Vasco Pedro, the founder of Ubabel which is a company that offers translation solutions powered by artificial intelligence (AI). Pedro is a renown figure in the application of AI in corporate-level translation solutions with a list of clients including Microsoft, Facebook, among others. The article is written as a discussion on the current state of AI, language, and usability in daily lives. The article first highlights the historical era where AI and its components were just ideas depicted in films.

The discussion then switches to a more in-depth look at the different types of AI and what each means to human. Further, the author describes the direction of designing machines, and letting machines design themselves to do things that human beings cannot do. This discussion is solidified with an example of Facebook’s new M service product that integrates humans and AI in serving customers in such a way that the AI learns from humans and improves on itself.

Eggers, W. D., Malik, N., & Gracie, M. (2019). Using AI to unleash the power of unstructured government data. Deloitte Insights.

This article is written by employees of Deloitte, a reputable consulting firm with a global reach. The objective of the writing is to make a case for the use of natural language processing (NLP) as under AI to structure and interpret the rather enormous and unstructured data handled by the government. The article starts by highlighting a case where the Department of Defense used a Deep Exploration and Filtering of Text program (that is part of NLP) to capture, analyze, and deduce insights from data. The authors then offer a history of NLP including how it has evolved to a conversational format being used in modern contexts.            

In addition, the article covers the technical capabilities of NLP with an elaboration of each technical aspect and its importance in general. These technical capabilities are then later tied together under a section addressing the usefulness of NLP in handling unstructured government data. The article also captures the different application domains and the first steps towards adopting NLP.

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