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        <title>LAST at SemEval-2021 Task 1: Improving Multi-Word Complexity Prediction Using Bigram Association Measures</title>
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        <namePart type="given">Yves</namePart>
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            <namePart type="given">Nathan</namePart>
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    <abstract>This paper describes the system developed by the Laboratoire d’analyse statistique des textes (LAST) for the Lexical Complexity Prediction shared task at SemEval-2021. The proposed system is made up of a LightGBM model fed with features obtained from many word frequency lists, published lexical norms and psychometric data. For tackling the specificity of the multi-word task, it uses bigram association measures. Despite that the only contextual feature used was sentence length, the system achieved an honorable performance in the multi-word task, but poorer in the single word task. The bigram association measures were found useful, but to a limited extent.</abstract>
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%0 Conference Proceedings
%T LAST at SemEval-2021 Task 1: Improving Multi-Word Complexity Prediction Using Bigram Association Measures
%A Bestgen, Yves
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y Schluter, Natalie
%Y Emerson, Guy
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%S Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F bestgen-2021-last-semeval
%X This paper describes the system developed by the Laboratoire d’analyse statistique des textes (LAST) for the Lexical Complexity Prediction shared task at SemEval-2021. The proposed system is made up of a LightGBM model fed with features obtained from many word frequency lists, published lexical norms and psychometric data. For tackling the specificity of the multi-word task, it uses bigram association measures. Despite that the only contextual feature used was sentence length, the system achieved an honorable performance in the multi-word task, but poorer in the single word task. The bigram association measures were found useful, but to a limited extent.
%R 10.18653/v1/2021.semeval-1.71
%U https://aclanthology.org/2021.semeval-1.71
%U https://doi.org/10.18653/v1/2021.semeval-1.71
%P 571-577

Markdown (Informal)

[LAST at SemEval-2021 Task 1: Improving Multi-Word Complexity Prediction Using Bigram Association Measures](https://aclanthology.org/2021.semeval-1.71) (Bestgen, SemEval 2021)

• LAST at SemEval-2021 Task 1: Improving Multi-Word Complexity Prediction Using Bigram Association Measures (Bestgen, SemEval 2021)

ACL

• Yves Bestgen. 2021. LAST at SemEval-2021 Task 1: Improving Multi-Word Complexity Prediction Using Bigram Association Measures. In Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), pages 571–577, Online. Association for Computational Linguistics.