<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="ballesteros-nivre-2012-maltoptimizer-system">
    <titleInfo>
        <title>MaltOptimizer: A System for MaltParser Optimization</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Miguel</namePart>
        <namePart type="family">Ballesteros</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Joakim</namePart>
        <namePart type="family">Nivre</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2012-may</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC’12)</title>
        </titleInfo>
        <originInfo>
            <publisher>European Language Resources Association (ELRA)</publisher>
            <place>
                <placeTerm type="text">Istanbul, Turkey</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Freely available statistical parsers often require careful optimization to produce state-of-the-art results, which can be a non-trivial task especially for application developers who are not interested in parsing research for its own sake. We present MaltOptimizer, a freely available tool developed to facilitate parser optimization using the open-source system MaltParser, a data-driven parser-generator that can be used to train dependency parsers given treebank data. MaltParser offers a wide range of parameters for optimization, including nine different parsing algorithms, two different machine learning libraries (each with a number of different learners), and an expressive specification language that can be used to define arbitrarily rich feature models. MaltOptimizer is an interactive system that first performs an analysis of the training set in order to select a suitable starting point for optimization and then guides the user through the optimization of parsing algorithm, feature model, and learning algorithm. Empirical evaluation on data from the CoNLL 2006 and 2007 shared tasks on dependency parsing shows that MaltOptimizer consistently improves over the baseline of default settings and sometimes even surpasses the result of manual optimization.</abstract>
    <identifier type="citekey">ballesteros-nivre-2012-maltoptimizer-system</identifier>
    <location>
        <url>http://www.lrec-conf.org/proceedings/lrec2012/pdf/715_Paper.pdf</url>
    </location>
    <part>
        <date>2012-may</date>
        <extent unit="page">
            <start>2757</start>
            <end>2763</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T MaltOptimizer: A System for MaltParser Optimization
%A Ballesteros, Miguel
%A Nivre, Joakim
%S Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC’12)
%D 2012
%8 may
%I European Language Resources Association (ELRA)
%C Istanbul, Turkey
%F ballesteros-nivre-2012-maltoptimizer-system
%X Freely available statistical parsers often require careful optimization to produce state-of-the-art results, which can be a non-trivial task especially for application developers who are not interested in parsing research for its own sake. We present MaltOptimizer, a freely available tool developed to facilitate parser optimization using the open-source system MaltParser, a data-driven parser-generator that can be used to train dependency parsers given treebank data. MaltParser offers a wide range of parameters for optimization, including nine different parsing algorithms, two different machine learning libraries (each with a number of different learners), and an expressive specification language that can be used to define arbitrarily rich feature models. MaltOptimizer is an interactive system that first performs an analysis of the training set in order to select a suitable starting point for optimization and then guides the user through the optimization of parsing algorithm, feature model, and learning algorithm. Empirical evaluation on data from the CoNLL 2006 and 2007 shared tasks on dependency parsing shows that MaltOptimizer consistently improves over the baseline of default settings and sometimes even surpasses the result of manual optimization.
%U http://www.lrec-conf.org/proceedings/lrec2012/pdf/715_Paper.pdf
%P 2757-2763

Markdown (Informal)

[MaltOptimizer: A System for MaltParser Optimization](http://www.lrec-conf.org/proceedings/lrec2012/pdf/715_Paper.pdf) (Ballesteros & Nivre, LREC 2012)

• MaltOptimizer: A System for MaltParser Optimization (Ballesteros & Nivre, LREC 2012)

ACL

• Miguel Ballesteros and Joakim Nivre. 2012. MaltOptimizer: A System for MaltParser Optimization. In Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12), pages 2757–2763, Istanbul, Turkey. European Language Resources Association (ELRA).