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        <title>POS Tagging for German: how important is the Right Context?</title>
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    <name type="personal">
        <namePart type="given">Steliana</namePart>
        <namePart type="family">Ivanova</namePart>
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    <name type="personal">
        <namePart type="given">Sandra</namePart>
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            <title>Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC’08)</title>
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        <name type="personal">
            <namePart type="given">Nicoletta</namePart>
            <namePart type="family">Calzolari</namePart>
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        <name type="personal">
            <namePart type="given">Khalid</namePart>
            <namePart type="family">Choukri</namePart>
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        <name type="personal">
            <namePart type="given">Bente</namePart>
            <namePart type="family">Maegaard</namePart>
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            <namePart type="given">Joseph</namePart>
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        <name type="personal">
            <namePart type="given">Jan</namePart>
            <namePart type="family">Odijk</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Stelios</namePart>
            <namePart type="family">Piperidis</namePart>
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        <name type="personal">
            <namePart type="given">Daniel</namePart>
            <namePart type="family">Tapias</namePart>
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            <publisher>European Language Resources Association (ELRA)</publisher>
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    <abstract>Part-of-Speech tagging is generally performed by Markov models, based on bigram or trigram models. While Markov models have a strong concentration on the left context of a word, many languages require the inclusion of right context for correct disambiguation. We show for German that the best results are reached by a combination of left and right context. If only left context is available, then changing the direction of analysis and going from right to left improves the results. In a version of MBT with default parameter settings, the inclusion of the right context improved POS tagging accuracy from 94.00% to 96.08%, thus corroborating our hypothesis. The version with optimized parameters reaches 96.73%.</abstract>
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%0 Conference Proceedings
%T POS Tagging for German: how important is the Right Context?
%A Ivanova, Steliana
%A Kuebler, Sandra
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Odijk, Jan
%Y Piperidis, Stelios
%Y Tapias, Daniel
%S Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC’08)
%D 2008
%8 May
%I European Language Resources Association (ELRA)
%C Marrakech, Morocco
%F ivanova-kuebler-2008-pos
%X Part-of-Speech tagging is generally performed by Markov models, based on bigram or trigram models. While Markov models have a strong concentration on the left context of a word, many languages require the inclusion of right context for correct disambiguation. We show for German that the best results are reached by a combination of left and right context. If only left context is available, then changing the direction of analysis and going from right to left improves the results. In a version of MBT with default parameter settings, the inclusion of the right context improved POS tagging accuracy from 94.00% to 96.08%, thus corroborating our hypothesis. The version with optimized parameters reaches 96.73%.
%U http://www.lrec-conf.org/proceedings/lrec2008/pdf/253_paper.pdf

Markdown (Informal)

[POS Tagging for German: how important is the Right Context?](http://www.lrec-conf.org/proceedings/lrec2008/pdf/253_paper.pdf) (Ivanova & Kuebler, LREC 2008)

• POS Tagging for German: how important is the Right Context? (Ivanova & Kuebler, LREC 2008)

ACL

• Steliana Ivanova and Sandra Kuebler. 2008. POS Tagging for German: how important is the Right Context?. In Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08), Marrakech, Morocco. European Language Resources Association (ELRA).