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        <title>Semantic Pleonasm Detection</title>
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    <name type="personal">
        <namePart type="given">Omid</namePart>
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        <namePart type="given">Andrew</namePart>
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        <namePart type="family">Lucas</namePart>
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    <name type="personal">
        <namePart type="given">Rebecca</namePart>
        <namePart type="family">Hwa</namePart>
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            <title>Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)</title>
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            <namePart type="given">Marilyn</namePart>
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            <namePart type="given">Heng</namePart>
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        <name type="personal">
            <namePart type="given">Amanda</namePart>
            <namePart type="family">Stent</namePart>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>Pleonasms are words that are redundant. To aid the development of systems that detect pleonasms in text, we introduce an annotated corpus of semantic pleonasms. We validate the integrity of the corpus with interannotator agreement analyses. We also compare it against alternative resources in terms of their effects on several automatic redundancy detection methods.</abstract>
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    <identifier type="doi">10.18653/v1/N18-2036</identifier>
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%0 Conference Proceedings
%T Semantic Pleonasm Detection
%A Kashefi, Omid
%A Lucas, Andrew T.
%A Hwa, Rebecca
%Y Walker, Marilyn
%Y Ji, Heng
%Y Stent, Amanda
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F kashefi-etal-2018-semantic
%X Pleonasms are words that are redundant. To aid the development of systems that detect pleonasms in text, we introduce an annotated corpus of semantic pleonasms. We validate the integrity of the corpus with interannotator agreement analyses. We also compare it against alternative resources in terms of their effects on several automatic redundancy detection methods.
%R 10.18653/v1/N18-2036
%U https://aclanthology.org/N18-2036
%U https://doi.org/10.18653/v1/N18-2036
%P 225-230

Markdown (Informal)

[Semantic Pleonasm Detection](https://aclanthology.org/N18-2036) (Kashefi et al., NAACL 2018)

• Semantic Pleonasm Detection (Kashefi et al., NAACL 2018)

ACL

• Omid Kashefi, Andrew T. Lucas, and Rebecca Hwa. 2018. Semantic Pleonasm Detection. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 225–230, New Orleans, Louisiana. Association for Computational Linguistics.