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Register a new userTUDA-CCL at SemEval-2021 Task 1: Using Gradient-boosted Regression Tree Ensembles Trained on a Heterogeneous Feature Set for Predicting Lexical Complexity
Tuda-CCL في مهمة Semeval-2021: استخدام مجموعات شجرة الانحدار المزدوجة للتدرج المدربين على ميزة غير متجانسة مجموعة للتنبؤ بالتعقيد المعجمي
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في هذه الورقة، نقدم أنظمتنا المقدمة إلى مهمة Semeval-2021 1 بشأن تنبؤ التعقيد المعجمي. وكان الهدف من هذه المهمة المشتركة هو إنشاء أنظمة قادرة على التنبؤ بالتعقيد المعجمي لرموز الرموز Word وبرامج Bigram متعددة الكلمات داخل سياق جملة معطى، أالقيمة المستمرة تشير إلى الصعوبة في فهم الكلام المعني.تعتمد نهجنا على مجموعة من مجموعات الانحدار المتدرج المجهزة باستخدام ميزة غير متجانسة تم تعيينها بين الميزات اللغوية، ومظلات الكلمة الثابتة والسياقية، وتعاونية نفسية المعيار، وتردد Wordnet، و Word- وشخصية Bigram وإدراجه وإدراجه في نصوص الكلمات لإنشاء نموذج قادر على تعيين كلمة قادرة على تعيين كلمة قادرة على تعيين كلمة واحدةأو تعبير متعدد الكلمات تعبير تعقيد تعتمد على السياق.يمكننا أن نظهر أنه يمكن أن تساعد تضمين السلسلة السياقية بشكل خاص في التنبؤ بالتعقيد المعجمي.
In this paper, we present our systems submitted to SemEval-2021 Task 1 on lexical complexity prediction.The aim of this shared task was to create systems able to predict the lexical complexity of word tokens and bigram multiword expressions within a given sentence context, a continuous value indicating the difficulty in understanding a respective utterance. Our approach relies on gradient boosted regression tree ensembles fitted using a heterogeneous feature set combining linguistic features, static and contextualized word embeddings, psycholinguistic norm lexica, WordNet, word- and character bigram frequencies and inclusion in wordlists to create a model able to assign a word or multiword expression a context-dependent complexity score. We can show that especially contextualised string embeddings can help with predicting lexical complexity.
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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.
We present our approach to predicting lexical complexity of words in specific contexts, as entered LCP Shared Task 1 at SemEval 2021. The approach consists of separating sentences into smaller chunks, embedding them with Sent2Vec, and reducing the em
beddings into a simpler vector used as input to a neural network, the latter for predicting the complexity of words and expressions. Results show that the pre-trained sentence embeddings are not able to capture lexical complexity from the language when applied in cross-domain applications.
In this paper, we present three supervised systems for English lexical complexity prediction of single and multiword expressions for SemEval-2021 Task 1. We explore the use of statistical baseline features, masked language models, and character-level
encoders to predict the complexity of a target token in context. Our best system combines information from these three sources. The results indicate that information from masked language models and character-level encoders can be combined to improve lexical complexity prediction.
The present work aims at assigning a complexity score between 0 and 1 to a target word or phrase in a given sentence. For each Single Word Target, a Random Forest Regressor is trained on a feature set consisting of lexical, semantic, and syntactic in
formation about the target. For each Multiword Target, a set of individual word features is taken along with single word complexities in the feature space. The system yielded the Pearson correlation of 0.7402 and 0.8244 on the test set for the Single and Multiword Targets, respectively.
This paper describes our submission to the SemEval-2021 shared task on Lexical Complexity Prediction. We approached it as a regression problem and present an ensemble combining four systems, one feature-based and three neural with fine-tuning, freque
ncy pre-training and multi-task learning, achieving Pearson scores of 0.8264 and 0.7556 on the trial and test sets respectively (sub-task 1). We further present our analysis of the results and discuss our findings.
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