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BERT (Bidirectional Encoder Representations from Transformers) and ALBERT (A Lite BERT) are methods for pre-training language models which can later be fine-tuned for a variety of Natural Language Understanding tasks. These methods have been applied to a number of such tasks (mostly in English), achieving results that outperform the state-of-the-ar...
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... some examples expose the sentiment through emoticons. So, correctly interpreting these symbols Table 5, we did a 5-fold crossvalidation over all data. The fact that this corpus has several emoticons and out-of-vocabulary expressions makes it hard for the models that were There is a public ranking of this classification task on Kaggle 15 . ...Context 2
... some examples expose the sentiment through emoticons. So, correctly interpreting these symbols Table 5, we did a 5-fold crossvalidation over all data. The fact that this corpus has several emoticons and out-of-vocabulary expressions makes it hard for the models that were There is a public ranking of this classification task on Kaggle 15 . ...Similar publications
Aspect-based sentiment analysis (ABSA) includes two sub-tasks namely, aspect extraction and aspect-level sentiment classification. Most existing works address these sub-tasks independently by applying a supervised learning approach using labeled data. However, obtaining such labeled sentences is difficult and extremely expensive. Hence, it is impor...
Stimuli are easier to process when the preceding context (e.g., a sentence, in the case of a word) makes them predictable. However, it remains unclear whether context-based facilitation arises due to predictive preactivation of a limited set of relatively probable upcoming stimuli (with facilitation then linearly related to probability) or, instead...
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This paper presents our solution for SemEval2022 Task 10: Structured Sentiment Analysis. The solution consisted of two modules:
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