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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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Thesaurus of English Words and Phrases

By Peter Mark Roget

This book "supplies a vocabulary of English words and idiomatic phrases 'arranged … according to the ideas which they express'. The thesaurus, continually expanded and updated, has always remained in print, but this reissued first edition shows the impressive breadth of Roget's own knowledge and interests."


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The Brill Dictionary of Ancient Greek

By Franco Montanari

Coming soon: The Brill Dictionary of Ancient Greek by Franco Montanari is the most comprehensive dictionary for Ancient Greek to English for the 21st Century. Order your copy now!


Academic Paper


Title: Learning Morphology with Morfette
Paper URL: http://www.lrec-conf.org/proceedings/lrec2008/pdf/594_paper.pdf
Author: Grzegorz Chrupała
Email: click here to access email
Homepage: http://www.lsv.uni-saarland.de/personalPages/gchrupala/index.html
Institution: Saarland University
Author: Georgiana Dinu
Email: click here to access email
Institution: Saarland University
Author: Josef Van Genabith
Email: click here to access email
Institution: Dublin City University
Linguistic Field: Computational Linguistics
Abstract: Morfette is a modular, data-driven, probabilistic system which learns to perform joint morphological tagging and lemmatization from morphologically annotated corpora. The system is composed of two learning modules which are trained to predict morphological tags and lemmas using the Maximum Entropy classifier. The third module dynamically combines the predictions of the Maximum-Entropy models and outputs a probability distribution over tag-lemma pair sequences. The lemmatization module exploits the idea of recasting lemmatization as a classification task by using class labels which encode mappings from word forms to lemmas. Experimental evaluation results and error analysis on three morphologically rich languages show that the system achieves high accuracy with no language-specific feature engineering or additional resources.
Type: Individual Paper
Status: Completed
Venue: LREC 2008
URL: http://www.lrec-conf.org/proceedings/lrec2008/pdf/594_paper.pdf


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