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        <title>Greek Named Entity Recognition using Support Vector Machines, Maximum Entropy and Onetime</title>
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    <abstract>We describe our work on Greek Named Entity Recognition using comparatively three different machine learning techniques: (i) Support Vector Machines (SVM), (ii) Maximum Entropy and (iii) Onetime, a shortcut method based on previous work of one of the authors. The majority of our system’s features use linguistic knowledge provided by: morphology, punctuation, position of the lexical units within a sentence and within a text, electronic dictionaries, and the outputs of external tools (a tokenizer, a sentence splitter, and a Hellenic version of Brill’s Part of Speech Tagger). After testing we observed that the application of a few simple Post Testing Classification Correction (PTCC) rules created after the observation of output errors, improved the results of the SVM and the Maximum Entropy systems output. We achieved very good results with the three methods. Our best configurations (Support Vector Machines with a second degree polynomial kernel and Maximum Entropy) achieved both after the application of PTCC rules an overall F-measure of 91.06.</abstract>
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%0 Conference Proceedings
%T Greek Named Entity Recognition using Support Vector Machines, Maximum Entropy and Onetime
%A Michailidis, Ionas
%A Diamantaras, Konstantinos
%A Vasileiadis, Spiros
%A Frère, Yannick
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Gangemi, Aldo
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Odijk, Jan
%Y Tapias, Daniel
%S Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)
%D 2006
%8 May
%I European Language Resources Association (ELRA)
%C Genoa, Italy
%F michailidis-etal-2006-greek
%X We describe our work on Greek Named Entity Recognition using comparatively three different machine learning techniques: (i) Support Vector Machines (SVM), (ii) Maximum Entropy and (iii) Onetime, a shortcut method based on previous work of one of the authors. The majority of our system’s features use linguistic knowledge provided by: morphology, punctuation, position of the lexical units within a sentence and within a text, electronic dictionaries, and the outputs of external tools (a tokenizer, a sentence splitter, and a Hellenic version of Brill’s Part of Speech Tagger). After testing we observed that the application of a few simple Post Testing Classification Correction (PTCC) rules created after the observation of output errors, improved the results of the SVM and the Maximum Entropy systems output. We achieved very good results with the three methods. Our best configurations (Support Vector Machines with a second degree polynomial kernel and Maximum Entropy) achieved both after the application of PTCC rules an overall F-measure of 91.06.
%U http://www.lrec-conf.org/proceedings/lrec2006/pdf/557_pdf.pdf

Markdown (Informal)

[Greek Named Entity Recognition using Support Vector Machines, Maximum Entropy and Onetime](http://www.lrec-conf.org/proceedings/lrec2006/pdf/557_pdf.pdf) (Michailidis et al., LREC 2006)

• Greek Named Entity Recognition using Support Vector Machines, Maximum Entropy and Onetime (Michailidis et al., LREC 2006)

ACL

• Ionas Michailidis, Konstantinos Diamantaras, Spiros Vasileiadis, and Yannick Frère. 2006. Greek Named Entity Recognition using Support Vector Machines, Maximum Entropy and Onetime. In Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06), Genoa, Italy. European Language Resources Association (ELRA).