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Register a new userComplex 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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توضح هذه المقالة نظاما للتنبؤ بمهمة تقوية التعقيد المعجمية (LCP) التي تم استضافتها في Semeval 2021 (المهمة 1) مع مجموعة بيانات جديدة مشروحة مع مقياس Likert.يقع المهمة في مسار الدلالات المعجمية، وتألفت المهمة من التنبؤ بقيمة تعقيد الكلمات في السياق.تم تنفيذ نهج لتعلم الآلات بناء على تواتر الكلمات والعديد من الخصائص المضافة على مستوى Word.على هذه الميزات، تم تدريب خوارزمية الانحدار الغابات العشوائية الخاضعة للإشراف.تم إجراء عدة أشواط بقيم مختلفة لمراقبة أداء الخوارزمية.للتقييم، أبلغت أفضل النتائج الخاصة بنا عن درجة M.A.E 0.07347، M.S.E.من 0.00938، و R.M.S.E.من 0.096871.أظهرت تجاربنا أنه مع عدد أكبر من الخصائص، فإن دقة التصنيف تزداد.
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
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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.
This paper describes systems submitted to Se- mEval 2021 Task 1: Lexical Complexity Prediction (LCP). We compare a linear and a non-linear regression models trained to work for both tracks of the task. We show that both systems are able to generalize
better when supplied with information about complexities of single word and multi-word expression (MWE) targets simultaneously. This approach proved to be the most beneficial for multi-word expression targets. We also demonstrate that some hand-crafted features differ in their importance for the target types.
This paper revisits feature engineering approaches for predicting the complexity level of English words in a particular context using regression techniques. Our best submission to the Lexical Complexity Prediction (LCP) shared task was ranked 3rd out
of 48 systems for sub-task 1 and achieved Pearson correlation coefficients of 0.779 and 0.809 for single words and multi-word expressions respectively. The conclusion is that a combination of lexical, contextual and semantic features can still produce strong baselines when compared against human judgement.
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