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Register a new userSoochowDS at ROCLING-2021 Shared Task: Text Sentiment Analysis Using BERT and LSTM
Soochowds في المهمة المشتركة Rocling-2021: تحليل معنويات النص باستخدام Bert و LSTM
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في هذه المهمة المشتركة، تقترح هذه الورقة طريقة للجمع بين نموذج ناقلات Word القائم على BERT ومقدمة تنبؤ LSTM للتنبؤ بقيم التكافؤ والإثارة في النص.من بينها، ناقل الكلمات المستند إلى بيرت هو 768 ثيم، ويتم تغذية كل ناقلات كلمة في الجملة بالتتابع لطراز LSTM للتنبؤ.تظهر النتائج التجريبية أن أداء طريقة لدينا المقترحة أفضل من نتائج نموذج الانحدار لاسو.
In this shared task, this paper proposes a method to combine the BERT-based word vector model and the LSTM prediction model to predict the Valence and Arousal values in the text. Among them, the BERT-based word vector is 768-dimensional, and each word vector in the sentence is sequentially fed to the LSTM model for prediction. The experimental results show that the performance of our proposed method is better than the results of the Lasso Regression model.
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This paper present a description for the ROCLING 2021 shared task in dimensional sentiment analysis for educational texts. We submitted two runs in the final test. Both runs use the standard regression model. The Run1 uses Chinese version of BERT as
the base, and in Run2 we use the early version of MacBERT that Chinese version of RoBERTa-like BERT model, RoBERTa-wwm-ext. Using powerful pre-training model of BERT for text embedding to help train the model.
This paper presents the ROCLING 2021 shared task on dimensional sentiment analysis for educational texts which seeks to identify a real-value sentiment score of self-evaluation comments written by Chinese students in the both valence and arousal dime
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