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Complex words identification using word-level features for SemEval-2020 Task 1

التعرف على الكلمات المعقدة باستخدام ميزات مستوى Word-Level لمهمة Semeval-2020 1

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 Publication date 2021
and research's language is English
 Created by Shamra Editor




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This article describes a system to predict the complexity of words for the Lexical Complexity Prediction (LCP) shared task hosted at SemEval 2021 (Task 1) with a new annotated English dataset with a Likert scale. Located in the Lexical Semantics track, the task consisted of predicting the complexity value of the words in context. A machine learning approach was carried out based on the frequency of the words and several characteristics added at word level. Over these features, a supervised random forest regression algorithm was trained. Several runs were performed with different values to observe the performance of the algorithm. For the evaluation, our best results reported a M.A.E score of 0.07347, M.S.E. of 0.00938, and R.M.S.E. of 0.096871. Our experiments showed that, with a greater number of characteristics, the precision of the classification increases.



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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 r esults 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.
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