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        <title>Complex Word Identification: Challenges in Data Annotation and System Performance</title>
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    <name type="personal">
        <namePart type="given">Lucia</namePart>
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        <name type="personal">
            <namePart type="given">Yuen-Hsien</namePart>
            <namePart type="family">Tseng</namePart>
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            <namePart type="given">Hsin-Hsi</namePart>
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            <namePart type="given">Lung-Hao</namePart>
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        <name type="personal">
            <namePart type="given">Liang-Chih</namePart>
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    <abstract>This paper revisits the problem of complex word identification (CWI) following up the SemEval CWI shared task. We use ensemble classifiers to investigate how well computational methods can discriminate between complex and non-complex words. Furthermore, we analyze the classification performance to understand what makes lexical complexity challenging. Our findings show that most systems performed poorly on the SemEval CWI dataset, and one of the reasons for that is the way in which human annotation was performed.</abstract>
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%0 Conference Proceedings
%T Complex Word Identification: Challenges in Data Annotation and System Performance
%A Zampieri, Marcos
%A Malmasi, Shervin
%A Paetzold, Gustavo
%A Specia, Lucia
%Y Tseng, Yuen-Hsien
%Y Chen, Hsin-Hsi
%Y Lee, Lung-Hao
%Y Yu, Liang-Chih
%S Proceedings of the 4th Workshop on Natural Language Processing Techniques for Educational Applications (NLPTEA 2017)
%D 2017
%8 December
%I Asian Federation of Natural Language Processing
%C Taipei, Taiwan
%F zampieri-etal-2017-complex
%X This paper revisits the problem of complex word identification (CWI) following up the SemEval CWI shared task. We use ensemble classifiers to investigate how well computational methods can discriminate between complex and non-complex words. Furthermore, we analyze the classification performance to understand what makes lexical complexity challenging. Our findings show that most systems performed poorly on the SemEval CWI dataset, and one of the reasons for that is the way in which human annotation was performed.
%U https://aclanthology.org/W17-5910
%P 59-63

Markdown (Informal)

[Complex Word Identification: Challenges in Data Annotation and System Performance](https://aclanthology.org/W17-5910) (Zampieri et al., NLP-TEA 2017)

• Complex Word Identification: Challenges in Data Annotation and System Performance (Zampieri et al., NLP-TEA 2017)

ACL

• Marcos Zampieri, Shervin Malmasi, Gustavo Paetzold, and Lucia Specia. 2017. Complex Word Identification: Challenges in Data Annotation and System Performance. In Proceedings of the 4th Workshop on Natural Language Processing Techniques for Educational Applications (NLPTEA 2017), pages 59–63, Taipei, Taiwan. Asian Federation of Natural Language Processing.