TEXT AND DATA MINING FOR BIOMEDICAL DISCOVERY- SESSION INTRODUCTION

The existing public access policy for our three journals—The Journal of Cell Biology, The Journal of Experimental Medicine, and The Journal of General Physiology—is fully compliant with new policies from the Research Councils UK (RCUK) and the Wellcome Trust. In addition to mandating public access, the new policies specify licensing terms for reuse of content by third parties, in particular for text and data mining. We question the need for these specific terms, and we have added a statement to our licensing policy stipulating that anyone, including commercial entities, is permitted to mine our published text and data.

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Text and Data Mining to Detect Phishing Websites and Spam Emails

Swarm, Evolutionary, and Memetic Computing - Lecture Notes in Computer Science ◽

10.1007/978-3-319-03756-1_50 ◽

2013 ◽

pp. 559-573 ◽

Cited By ~ 8

Author(s):

Mayank Pandey ◽

Vadlamani Ravi

Keyword(s):

Data Mining ◽

Text And Data Mining

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Non-Contractual Use of Data - Focusing on Limitations to Copyright for Text and Data Mining -

kangwon Law Review ◽

10.18215/kwlr.2021.65..1 ◽

2021 ◽

Vol 65 ◽

pp. 1-54

Author(s):

Sang Yong Lee

Keyword(s):

Data Mining ◽

Use Of Data ◽

Text And Data Mining

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Computational Legal Methods: Text and Data Mining in Intellectual Property Research

10.1093/oso/9780198826743.003.0032 ◽

2021 ◽

pp. 487-505

Author(s):

Thomas Margoni

Keyword(s):

Data Mining ◽

Intellectual Property ◽

Law School ◽

Human Observer ◽

Systematic Classification ◽

The Arts ◽

High Quality Information ◽

Recent Project ◽

Analytical Tools ◽

Text And Data Mining

Text and Data Mining (TDM) can generally be defined as the process of deriving high-quality information from text and data by using digital analytical tools . The impact that TDM may have on science, humanities, and the arts is invaluable. This is because by identifying the correlations and patterns that are often concealed to the eye of a human observer TDM allows for the discovery of knowledge that would have otherwise remained hidden. After a brief introduction, Section II of this chapter illustrates the state of the art in the nascent field of TDM applied to intellectual property (IP) research. It formulates some proposals of systematic classification in an area that suffers from a degree of terminological vagueness. In particular, the chapter argues that TDM, together with other types of data-driven analytical tools, should be autonomously classified as ‘computational legal methods’. Section III of the chapter offers concrete examples of the application of these methods in IP research. This is achieved by discussing a recent project on TDM, which required the development of dedicated approaches in order to address certain problems that emerged during the project’s execution.. The discussion identifies some of the most promising advances in terms of automation and predictive analysis that the use of TDM in intellectual property research could enable. At the same time, the partial success of the experiment shows that there are a number of training and skill-related issues that legal researchers and practitioners interested in the use of TDM should consider. Accordingly, the second argument advanced in this chapter is that law school programmes should include mandatory courses in computational legal methods in order to equip future lawyers with the skillsets needed in the digital (legal) environment.

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