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Register a new userCLULEX at SemEval-2021 Task 1: A Simple System Goes a Long Way
CLLEX في مهمة Semeval-2021 1: نظام بسيط يقطع شوطا طويلا
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تقدم هذه الورقة النظام الذي قدمناه إلى المهمة المشتركة التي نقدمها لأول المهمة المشتركة (LCP) 2021. توفر المهمة المشتركة للمشاركين مع مجموعة بيانات جديدة باللغة الإنجليزية تتضمن سياق الكلمة المستهدفة.نحن نشارك في المهمة الفرعية للتنبؤ بكلمة واحدة والتركيز على هندسة الميزة.يتم تدريب أفضل نظامنا على الميزات اللغوية و Adgeddings Word (درجة بيرسون من 0.7942).ومع ذلك، نوضح أن مجموعة ميزة أبسط تحقق نتائج مماثلة وتقديم نموذج تدرب على 36 ميزات لغوية (درجة بيرسون من 0.7925).
This paper presents the system we submitted to the first Lexical Complexity Prediction (LCP) Shared Task 2021. The Shared Task provides participants with a new English dataset that includes context of the target word. We participate in the single-word complexity prediction sub-task and focus on feature engineering. Our best system is trained on linguistic features and word embeddings (Pearson's score of 0.7942). We demonstrate, however, that a simpler feature set achieves comparable results and submit a model trained on 36 linguistic features (Pearson's score of 0.7925).
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This paper describes our contribution to SemEval 2021 Task 1 (Shardlow et al., 2021): Lexical Complexity Prediction. In our approach, we leverage the ELECTRA model and attempt to mirror the data annotation scheme. Although the task is a regression ta
sk, we show that we can treat it as an aggregation of several classification and regression models. This somewhat counter-intuitive approach achieved an MAE score of 0.0654 for Sub-Task 1 and MAE of 0.0811 on Sub-Task 2. Additionally, we used the concept of weak supervision signals from Gloss-BERT in our work, and it significantly improved the MAE score in Sub-Task 1.
Lexical complexity plays an important role in reading comprehension. lexical complexity prediction (LCP) can not only be used as a part of Lexical Simplification systems, but also as a stand-alone application to help people better reading. This paper
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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.
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.
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