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        <title>Manchester Metropolitan at SemEval-2021 Task 1: Convolutional Networks for Complex Word Identification</title>
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    <name type="personal">
        <namePart type="given">Robert</namePart>
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            <namePart type="given">Alexis</namePart>
            <namePart type="family">Palmer</namePart>
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    <abstract>We present two convolutional neural networks for predicting the complexity of words and phrases in context on a continuous scale. Both models utilize word and character embeddings alongside lexical features as inputs. Our system displays reasonable results with a Pearson correlation of 0.7754 on the task as a whole. We highlight the limitations of this method in properly assessing the context of the target text, and explore the effectiveness of both systems across a range of genres. Both models were submitted as part of LCP 2021, which focuses on the identification of complex words and phrases as a context dependent, regression based task.</abstract>
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%0 Conference Proceedings
%T Manchester Metropolitan at SemEval-2021 Task 1: Convolutional Networks for Complex Word Identification
%A Flynn, Robert
%A Shardlow, Matthew
%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 flynn-shardlow-2021-manchester
%X We present two convolutional neural networks for predicting the complexity of words and phrases in context on a continuous scale. Both models utilize word and character embeddings alongside lexical features as inputs. Our system displays reasonable results with a Pearson correlation of 0.7754 on the task as a whole. We highlight the limitations of this method in properly assessing the context of the target text, and explore the effectiveness of both systems across a range of genres. Both models were submitted as part of LCP 2021, which focuses on the identification of complex words and phrases as a context dependent, regression based task.
%R 10.18653/v1/2021.semeval-1.76
%U https://aclanthology.org/2021.semeval-1.76
%U https://doi.org/10.18653/v1/2021.semeval-1.76
%P 603-608

Markdown (Informal)

[Manchester Metropolitan at SemEval-2021 Task 1: Convolutional Networks for Complex Word Identification](https://aclanthology.org/2021.semeval-1.76) (Flynn & Shardlow, SemEval 2021)

• Manchester Metropolitan at SemEval-2021 Task 1: Convolutional Networks for Complex Word Identification (Flynn & Shardlow, SemEval 2021)

ACL

• Robert Flynn and Matthew Shardlow. 2021. Manchester Metropolitan at SemEval-2021 Task 1: Convolutional Networks for Complex Word Identification. In Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), pages 603–608, Online. Association for Computational Linguistics.