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Natural language processing and early-modern dirty data: applying IBM Languageware to the 1641 depositions.

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Title: Natural language processing and early-modern dirty data: applying IBM Languageware to the 1641 depositions.
Authors: Sweetnam, Mark S.1, Fennell, Barbara A.2
Source: Literary & Linguistic Computing. Apr2012, Vol. 27 Issue 1, p39-54. 16p. 2 Diagrams, 1 Chart.
Subjects: Electronic data processing, IBM software, Linguistic analysis, Programming languages, Natural language processing
Abstract: This article provides an account of the steps involved in adapting IBM's Languageware natural language processing software to a large corpus of highly non-standard 17th century documents. It examines the challenges encountered as part of this process, and outlines the approach adopted to provide a robust and reusable tool for the linguistic analysis of early modern source texts. [ABSTRACT FROM AUTHOR]
Copyright of Literary & Linguistic Computing is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: This article provides an account of the steps involved in adapting IBM's Languageware natural language processing software to a large corpus of highly non-standard 17th century documents. It examines the challenges encountered as part of this process, and outlines the approach adopted to provide a robust and reusable tool for the linguistic analysis of early modern source texts. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Literary & Linguistic Computing is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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