Negotiating a Text Mining License for Faculty Researchers

Williams, Leslie A.; Fox, Lynne M.; Roeder, Christophe; Hunter, Lawrence

doi:10.6017/ital.v33i3.5485

Cited by 7 publications

(2 citation statements)

References 12 publications

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“…Publishers of non-open access journals usually require TM researchers to negotiate a licence agreement for every research project and impose several restrictions, e.g. non-commercial use (110). This has been alleviated by exceptions for TM introduced in several countries, which allow researchers to automatically mine the content they have lawful access to.…”

Section: Discussionmentioning

confidence: 99%

Text mining resources for the life sciences

Przybyła¹,

Shardlow²,

Aubin³

et al. 2016

Database

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Text mining is a powerful technology for quickly distilling key information from vast quantities of biomedical literature. However, to harness this power the researcher must be well versed in the availability, suitability, adaptability, interoperability and comparative accuracy of current text mining resources. In this survey, we give an overview of the text mining resources that exist in the life sciences to help researchers, especially those employed in biocuration, to engage with text mining in their own work. We categorize the various resources under three sections: Content Discovery looks at where and how to find biomedical publications for text mining; Knowledge Encoding describes the formats used to represent the different levels of information associated with content that enable text mining, including those formats used to carry such information between processes; Tools and Services gives an overview of workflow management systems that can be used to rapidly configure and compare domain- and task-specific processes, via access to a wide range of pre-built tools. We also provide links to relevant repositories in each section to enable the reader to find resources relevant to their own area of interest. Throughout this work we give a special focus to resources that are interoperable—those that have the crucial ability to share information, enabling smooth integration and reusability.

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Section: Discussionmentioning

confidence: 99%

Text mining resources for the life sciences

Przybyła¹,

Shardlow²,

Aubin³

et al. 2016

Database

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“…Robots may or may not be allowed to search open access databases. Scholars and libraries are pressing for greater mining privileges of journals, books, and other intellectual resources (Lammey, 2014;Senseney, Dickson, et al, 2018;Van de Sompel, 2013;Van de Sompel, Rosenthal, & Nelson, 2016;Williams, Fox, et al, 2014).…”

Section: Open Data Closed Data and Minable Datamentioning

confidence: 99%

Whose text, whose mining, and to whose benefit?

Borgman

2020

Quantitative Science Studies

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Scholarly content has become more difficult to find as information retrieval has devolved from bespoke systems that exploit disciplinary ontologies to keyword search on generic search engines. In parallel, more scholarly content is available through open access mechanisms. These trends have failed to converge in ways that would facilitate text data mining, both for information retrieval and as a research method for the quantitative social sciences. Scholarly content has become open to read without becoming open to mine, due both to constraints by publishers and to lack of attention in scholarly communication. The quantity of available text has grown faster than has the quality. Academic dossier systems are among the means to acquire more quality data for mining. Universities, publishers, and private enterprise may be able to mine these data for strategic purposes, however. On the positive front, changes in copyright may allow more data mining. Privacy, intellectual freedom, and access to knowledge are at stake. The next frontier of activism in open access scholarship is control over content for mining as a means to democratize knowledge.

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The Colorado Richly Annotated Full Text (CRAFT) Corpus: Multi-Model Annotation in the Biomedical Domain

Cohen

Verspoor

Fort

et al. 2017

Handbook of Linguistic Annotation

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A major question in linguistics is whether theoretical accounts of the general language work for specific domains. Similarly, in natural language processing, it is clear that general-domain solutions often fail when applied to specialized domains. One such specialized domain, which is increasingly seen as crucial to understanding human biology and disease, is the biomedical domain. For this reason, biomedical corpus construction has been an area of considerable activity in recent years-for example, just in the past five years: (ordered by year of publication and then by first author's last name),

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Negotiating a Text Mining License for Faculty Researchers

Abstract: ABSTRACT

Cited by 7 publications

References 12 publications

Text mining resources for the life sciences

Text mining resources for the life sciences

Whose text, whose mining, and to whose benefit?

The Colorado Richly Annotated Full Text (CRAFT) Corpus: Multi-Model Annotation in the Biomedical Domain

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