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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 – 2 of 2 1 Open Aspect Target Sentiment Classification with Natural Language Prompts ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Birle, Ian; Chang, Haw-Shiuan; Hough, Alfred; Pinette, Brian; Seoh, Ronald; Tak, Mrinal. - : Underline Science Inc., 2021 Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.509/ Abstract: For many business applications, we often seek to analyze sentiments associated with any arbitrary aspects of commercial products, despite having a very limited amount of labels or even without any labels at all. However, existing aspect target sentiment classification (ATSC) models are not trainable if annotated datasets are not available. Even with labeled data, they fall short of reaching satisfactory performance. To address this, we propose simple approaches that better solve ATSC with natural language prompts, enabling the task under zero-shot cases and enhancing supervised settings, especially for few-shot cases. Under the few-shot setting for SemEval 2014 Task 4 laptop domain, our method of reformulating ATSC as an NLI task outperforms supervised SOTA approaches by up to 24.13 accuracy points and 33.14 macro F1 points. Moreover, we demonstrate that our prompts could handle implicitly stated aspects as well: our models reach ... Keyword: Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Inference; Natural Language Processing; Sentiment Analysis URL: https://dx.doi.org/10.48448/xy0p-qf26 https://underline.io/lecture/37815-open-aspect-target-sentiment-classification-with-natural-language-prompts BASE Hide details 2 The materials science procedural text corpus: Annotating materials synthesis procedures with shallow semantic structures Mysore, Sheshera; Jensen, Zach; Kim, Edward... In: arXiv (2020) BASE Show details

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