تصف هذه الورقة أنظمة المقدمة إلى المهمة SE-MEVAL 2021 1: تنبؤ التعقيد المعجمي (LCP).نقارن نماذج الانحدار الخطية وغير الخطية المدربة للعمل في كلا المسارين للمهمة.نظرا لأن كلا النظامين قادرين على التعميم بشكل أفضل عند توفير معلومات حول تعقيدات كلمة واحدة ويعتبر التعبير المتعدد الكلمة (MWE) في وقت واحد.أثبت هذا النهج أنه الأكثر فائدة لأهداف التعبير المتعددة الكلمة.نوضح أيضا أن بعض الميزات المصنوعة يدويا تختلف في أهميتها للأنواع المستهدفة.
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.
References used
https://aclanthology.org/
Evaluating the complexity of a target word in a sentential context is the aim of the Lexical Complexity Prediction task at SemEval-2021. This paper presents the system created to assess single words lexical complexity, combining linguistic and psycho
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
This paper presents the results and main findings of SemEval-2021 Task 1 - Lexical Complexity Prediction. We provided participants with an augmented version of the CompLex Corpus (Shardlow et al. 2020). CompLex is an English multi-domain corpus in wh
In this contribution, we describe the system presented by the PolyU CBS-Comp Team at the Task 1 of SemEval 2021, where the goal was the estimation of the complexity of words in a given sentence context. Our top system, based on a combination of lexic
This paper describes a system submitted by team BigGreen to LCP 2021 for predicting the lexical complexity of English words in a given context. We assemble a feature engineering-based model with a deep neural network model founded on BERT. While BERT