@inproceedings{zampieri-etal-2017-complex,
title = "Complex Word Identification: Challenges in Data Annotation and System Performance",
author = "Zampieri, Marcos and
Malmasi, Shervin and
Paetzold, Gustavo and
Specia, Lucia",
editor = "Tseng, Yuen-Hsien and
Chen, Hsin-Hsi and
Lee, Lung-Hao and
Yu, Liang-Chih",
booktitle = "Proceedings of the 4th Workshop on Natural Language Processing Techniques for Educational Applications ({NLPTEA} 2017)",
month = dec,
year = "2017",
address = "Taipei, Taiwan",
publisher = "Asian Federation of Natural Language Processing",
url = "https://aclanthology.org/W17-5910",
pages = "59--63",
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.",
}
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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)
ACL