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        <title>Fast and Accurate Decision Trees for Natural Language Processing Tasks</title>
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        <namePart type="given">Tiberiu</namePart>
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            <title>Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017</title>
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            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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        <name type="personal">
            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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    <abstract>Decision trees have been previously employed in many machine-learning tasks such as part-of-speech tagging, lemmatization, morphological-attribute resolution, letter-to-sound conversion and statistical-parametric speech synthesis. In this paper we introduce an optimized tree-computation algorithm, which is based on the original ID3 algorithm. We also introduce a tree-pruning method that uses a development set to delete nodes from over-fitted models. The later mentioned algorithm also uses a results caching method for speed-up. Our algorithm is almost 200 times faster than a naive implementation and yields accurate results on our test datasets.</abstract>
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    <identifier type="doi">10.26615/978-954-452-049-6_016</identifier>
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%0 Conference Proceedings
%T Fast and Accurate Decision Trees for Natural Language Processing Tasks
%A Boros, Tiberiu
%A Dumitrescu, Stefan Daniel
%A Pipa, Sonia
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017
%D 2017
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F boros-etal-2017-fast
%X Decision trees have been previously employed in many machine-learning tasks such as part-of-speech tagging, lemmatization, morphological-attribute resolution, letter-to-sound conversion and statistical-parametric speech synthesis. In this paper we introduce an optimized tree-computation algorithm, which is based on the original ID3 algorithm. We also introduce a tree-pruning method that uses a development set to delete nodes from over-fitted models. The later mentioned algorithm also uses a results caching method for speed-up. Our algorithm is almost 200 times faster than a naive implementation and yields accurate results on our test datasets.
%R 10.26615/978-954-452-049-6_016
%U https://doi.org/10.26615/978-954-452-049-6_016
%P 103-110

Markdown (Informal)

[Fast and Accurate Decision Trees for Natural Language Processing Tasks](https://doi.org/10.26615/978-954-452-049-6_016) (Boros et al., RANLP 2017)

• Fast and Accurate Decision Trees for Natural Language Processing Tasks (Boros et al., RANLP 2017)

ACL

• Tiberiu Boros, Stefan Daniel Dumitrescu, and Sonia Pipa. 2017. Fast and Accurate Decision Trees for Natural Language Processing Tasks. In Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, pages 103–110, Varna, Bulgaria. INCOMA Ltd..