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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 3 of 3 1 End-to-end style-conditioned poetry generation: What does it take to learn from examples alone? ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Eger, Steffen. - : Underline Science Inc., 2021 BASE Show details 2 Learning Implicit Sentiment in Aspect-based Sentiment Analysis with Supervised Contrastive Pre-Training ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Li, Zhengyan. - : Underline Science Inc., 2021 BASE Show details 3 Adverse Drug Reaction Classification of Tweets with Fusion of Text and Drug Representations ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Sakhovskiy, Andrey; Tutubalina, Elena. - : Underline Science Inc., 2021 Abstract: In this paper, we focus on the classification of tweets as sources of potential signals for adverse drug effects. Following the intuition that text and drug structure representations are complementary, we introduce a multimodal model with two components. These components are state-of-the-art BERT-based models for language understanding and molecular property prediction. Experiments were carried out on multilingual benchmarks of the Social Media Mining for Health Research and Applications (#SMM4H) initiative. Our models obtained state-of-the-art results of 0.61 F1-measure and 0.57 F1-measure on #SMM4H 2021 Shared Tasks 1a and 2 in English and Russian, respectively. On the classification of French tweets from SMM4H 2020 Task 1, our approach pushes the state of the art by an absolute gain of 8% F1. Our experiments show that the molecular information obtained from neural networks is more beneficial for ADE classification than traditional molecular descriptors. ... Keyword: Computational Linguistics; Language Models; Machine Learning; Machine Learning and Data Mining; Natural Language Processing; Neural Network; Sentiment Analysis URL: https://dx.doi.org/10.48448/vk6q-q904 https://underline.io/lecture/39695-adverse-drug-reaction-classification-of-tweets-with-fusion-of-text-and-drug-representations BASE Hide details

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