Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving Data Driven Wordclass Tagging by System Combination   In this paper we examine how the differences in modelling between different
data driven systems performing the same NLP task can be exploited to yield a
higher accuracy than the best individual system. We do this by means of an
experiment involving the task of morpho-syntactic wordclass tagging. Four
well-known tagger generators (Hidden Markov Model, Memory-Based, Transformation
Rules and Maximum Entropy) are trained on the same corpus data. After
comparison, their outputs are combined using several voting strategies and
second stage classifiers. All combination taggers outperform their best
component, with the best combination showing a 19.1% lower error rate than the
best individual tagger.

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The categories of this paper are: atom-ph
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The categories of this paper are: eess
5
The categories of this paper are: gr-qc
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The categories of this paper are: chem-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: stat
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The categories of this paper are: nlin
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The categories of this paper are: nucl-th
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The categories of this paper are: supr-con
4
The categories of this paper are: cond-mat
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The categories of this paper are: dg-ga
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The categories of this paper are: funct-an
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The categories of this paper are: hep-lat
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The categories of this paper are: econ
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The categories of this paper are: ao-sci
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The categories of this paper are: astro-ph