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Words in Time and Place: Exploring Language Through the Historical Thesaurus of the Oxford English Dictionary

By David Crystal

Offers a unique view of the English language and its development, and includes witty commentary and anecdotes along the way.


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The Indo-European Controversy: Facts and Fallacies in Historical Linguistics

By Asya Pereltsvaig and Martin W. Lewis

This book "asserts that the origin and spread of languages must be examined primarily through the time-tested techniques of linguistic analysis, rather than those of evolutionary biology" and "defends traditional practices in historical linguistics while remaining open to new techniques, including computational methods" and "will appeal to readers interested in world history and world geography."


Academic Paper


Title: Automated unsupervised authorship analysis using evidence accumulation clustering
Author: Robert Layton
Institution: University of Sheffield
Author: Paul Watters
Homepage: http://www.comp.mq.edu.au/~pwatters
Institution: University of Sheffield
Author: Richard Dazeley
Institution: The University of Ballarat
Linguistic Field: Computational Linguistics; Text/Corpus Linguistics
Abstract: Authorship Analysis aims to extract information about the authorship of documents from features within those documents. Typically, this is performed as a classification task with the aim of identifying the author of a document, given a set of documents of known authorship. Alternatively, unsupervised methods have been developed primarily as visualisation tools to assist the manual discovery of clusters of authorship within a corpus by analysts. However, there is a need in many fields for more sophisticated unsupervised methods to automate the discovery, profiling and organisation of related information through clustering of documents by authorship. An automated and unsupervised methodology for clustering documents by authorship is proposed in this paper. The methodology is named NUANCE, for n-gram Unsupervised Automated Natural Cluster Ensemble. Testing indicates that the derived clusters have a strong correlation to the true authorship of unseen documents.

CUP AT LINGUIST

This article appears IN Natural Language Engineering Vol. 19, Issue 1, which you can READ on Cambridge's site or on LINGUIST .



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