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Katildakat في مهمة Semeval-2021 1: التعقيد المعجمي التنبؤ بكلمات واحدة وتعبيرات متعددة الكلمات باللغة الإنجليزية
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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.
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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
linguistic variables in a set of experiments involving random forest and XGboost regressors. Beyond encoding out-of-context information about the lemma, we implemented features based on pre-trained language models to model the target word's in-context complexity.
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.
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
ich words and multi-word expressions (MWEs) were annotated with respect to their complexity using a five point Likert scale. SemEval-2021 Task 1 featured two Sub-tasks: Sub-task 1 focused on single words and Sub-task 2 focused on MWEs. The competition attracted 198 teams in total, of which 54 teams submitted official runs on the test data to Sub-task 1 and 37 to Sub-task 2.
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
al, syntactic, word embeddings and Transformers-derived features and on a Gradient Boosting Regressor, achieves a top correlation score of 0.754 on the subtask 1 for single words and 0.659 on the subtask 2 for multiword expressions.
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
itself performs competitively, our feature engineering-based model helps in extreme cases, eg. separating instances of easy and neutral difficulty. Our handcrafted features comprise a breadth of lexical, semantic, syntactic, and novel phonological measures. Visualizations of BERT attention maps offer insight into potential features that Transformers models may learn when fine-tuned for lexical complexity prediction. Our ensembled predictions score reasonably well for the single word subtask, and we demonstrate how they can be harnessed to perform well on the multi word expression subtask too.
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