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Jost Gippert: Our Featured Linguist!

"Buenos dias", "buenas noches" -- this was the first words in a foreign language I heard in my life, as a three-year old boy growing up in developing post-war Western Germany, where the first gastarbeiters had arrived from Spain. Fascinated by the strange sounds, I tried to get to know some more languages, the only opportunity being TV courses of English and French -- there was no foreign language education for pre-teen school children in Germany yet in those days. Read more



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What is English? And Why Should We Care?

By: Tim William Machan

To find some answers Tim Machan explores the language's present and past, and looks ahead to its futures among the one and a half billion people who speak it. His search is fascinating and important, for definitions of English have influenced education and law in many countries and helped shape the identities of those who live in them.


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Medical Writing in Early Modern English

Edited by Irma Taavitsainen and Paivi Pahta

This volume provides a new perspective on the evolution of the special language of medicine, based on the electronic corpus of Early Modern English Medical Texts, containing over two million words of medical writing from 1500 to 1700.


Academic Paper


Title: Recentred local profiles for authorship attribution
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 attribution methods aim to determine the author of a document, by using information gathered from a set of documents with known authors. One method of performing this task is to create profiles containing distinctive features known to be used by each author. In this paper, a new method of creating an author or document profile is presented that detects features considered distinctive, compared to normal language usage. This recentreing approach creates more accurate profiles than previous methods, as demonstrated empirically using a known corpus of authorship problems. This method, named recentred local profiles, determines authorship accurately using a simple ‘best matching author’ approach to classification, compared to other methods in the literature. The proposed method is shown to be more stable than related methods as parameter values change. Using a weighted voting scheme, recentred local profiles is shown to outperform other methods in authorship attribution, with an overall accuracy of 69.9% on the ad-hoc authorship attribution competition corpus, representing a significant improvement over related methods.

CUP at LINGUIST

This article appears in Natural Language Engineering Vol. 18, Issue 3, which you can read on Cambridge's site .



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