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Experiments in 17th century English: manual versus automatic conceptual history

Authors: Stephen Pumfrey; Paul Rayson; John A. Mariani;

Experiments in 17th century English: manual versus automatic conceptual history

Abstract

Previous research in conceptual history, the study of change over time of key terms and value systems, has been carried out manually using a restricted number of pre-identified texts. We propose that a method combining techniques from corpus and computational linguistics can be exploited to support conceptual history with semantic searches on a vast sample of texts. To exemplify this method, we focus on a fundamental concept in modern science, the experimental method, in order to trace how the pre-existing and primarily religious concept of experiment (or experience) took on its modern, scientific meaning. We contrast a manual approach using the existing Early English Books Online search interface with an automatic method using corpus linguistics software and methods to turn the transcribed portion of the same dataset into a corpus. Both approaches allow us to separate the religious and scientific senses and plot their change over time. We observe a rapid change in the meaning of experimental from overwhelmingly religious to largely scientific within the 1660s. However, the automatic corpus method is much more efficient and will support future scholars in carrying out iterative studies in a matter of minutes rather than through weeks of painstaking work. Such methodological innovation has the potential to support the formation of new research questions which could not have been considered previously.

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United Kingdom
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Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
7
Average
Top 10%
Average
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