This article investigates the use of Transformation-Based Error-Driven learning for resolving part-of-speech ambiguity in the Greek language. The aim is not only to study the performance, but also to examine its dependence on different thematic domains. Results are presented here for two different test cases: a corpus on "management succession events" and a general-theme corpus. The two experiments show that the performance of this method does not depend on the thematic domain of the corpus, and its accuracy for the Greek language is around 95\%.
%0 Book Section
%1 HCI1999-Petasis
%A Petasis, Georgios
%A Paliouras, Georgios
%A Karkaletsis, Vangelis
%A Spyropoulos, Constantine D.
%A Androutsopoulos, Ion
%B ADVANCES IN INFORMATICS: Proceedings of the 7th Hellenic Conference on Informatics (HCI '99)
%D 2000
%E Fotiadis, Dimitrios I.
%E Nikolopoulos, Stavros D.
%I World Scientific
%K imported
%P 273--281
%T Using Machine Learning Techniques for Part-Of-Speech Tagging in the Greek Language
%U http://www.ellogon.org/petasis/bibliography/HCI1999/EPY99.pdf
%X This article investigates the use of Transformation-Based Error-Driven learning for resolving part-of-speech ambiguity in the Greek language. The aim is not only to study the performance, but also to examine its dependence on different thematic domains. Results are presented here for two different test cases: a corpus on "management succession events" and a general-theme corpus. The two experiments show that the performance of this method does not depend on the thematic domain of the corpus, and its accuracy for the Greek language is around 95\%.
%@ 978-981-02-4192-6
@inbook{HCI1999-Petasis,
abstract = {This article investigates the use of Transformation-Based Error-Driven learning for resolving part-of-speech ambiguity in the Greek language. The aim is not only to study the performance, but also to examine its dependence on different thematic domains. Results are presented here for two different test cases: a corpus on {"}management succession events{"} and a general-theme corpus. The two experiments show that the performance of this method does not depend on the thematic domain of the corpus, and its accuracy for the Greek language is around 95{\%}.},
added-at = {2011-08-10T12:37:26.000+0200},
author = {Petasis, Georgios and Paliouras, Georgios and Karkaletsis, Vangelis and Spyropoulos, Constantine D. and Androutsopoulos, Ion},
biburl = {https://www.bibsonomy.org/bibtex/2d7eefe8ab8164270b0ff964aed271d6a/petasis},
booktitle = {ADVANCES IN INFORMATICS: Proceedings of the 7th Hellenic Conference on Informatics (HCI '99)},
editor = {Fotiadis, Dimitrios I. and Nikolopoulos, Stavros D.},
interhash = {85eff392318dadab1474b12a6756c46e},
intrahash = {d7eefe8ab8164270b0ff964aed271d6a},
isbn = {978-981-02-4192-6},
keywords = {imported},
month = May,
note = {http://www.worldscibooks.com/compsci/4320.html},
pages = {273--281},
publisher = {World Scientific},
timestamp = {2011-08-10T12:37:27.000+0200},
title = {{U}sing {M}achine {L}earning {T}echniques for {P}art-{O}f-{S}peech {T}agging in the {G}reek {L}anguage},
url = {http://www.ellogon.org/petasis/bibliography/HCI1999/EPY99.pdf},
year = 2000
}