Papers with clustering approach
Disentangling Aspect and Stance via a Siamese Autoencoder for Aspect Clustering of Vaccination Opinions (2023.findings-acl)
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| Challenge: | Existing approaches to mining public opinions about vaccines from social media make direct usage of supervision information constraining the models to predefined aspect classes while still not distinguishing those aspects from users’ stances. |
| Approach: | They propose a model for vaccination opinion mining from social media that disentangles users’ stances from opinions via a disentangling attention mechanism and a Swapping-Autoencoder. |
| Outcome: | The proposed model outperforms existing methods on aspect-based opinion mining. |
Proposition-Level Clustering for Multi-Document Summarization (2022.naacl-main)
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Ori Ernst, Avi Caciularu, Ori Shapira, Ramakanth Pasunuru, Mohit Bansal, Jacob Goldberger, Ido Dagan
| Challenge: | Existing methods focused on clustering sentences to indicate information saliency and avoid redundancy. |
| Approach: | They propose to group together sub-sentential propositions to generate a representative sentence for each cluster via text fusion. |
| Outcome: | The proposed method improves over the previous state-of-the-art method in the DUC 2004 and TAC 2011 datasets, both in automatic ROUGE scores and human preference. |
Multi-word Measures: Modeling Semantic Change in Compound Nouns (2025.findings-acl)
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| Challenge: | Compound words provide a multifaceted challenge for diachronic models of semantic change . novel sense-targeting approach targets both noun compounds and their constituent parts . |
| Approach: | They propose a dataset of relatedness judgements of noun compounds in English and german . they use contrasting vector representations to evaluate their ability to cluster example sentence pairs . |
| Outcome: | The proposed approach captures diachronic meaning changes for multi-word expressions without condensing individual senses into an aggregate value. |
Beyond prompting: Making Pre-trained Language Models Better Zero-shot Learners by Clustering Representations (2022.emnlp-main)
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| Challenge: | Existing methods for zero-shot text classification involve heavy human engineering or complicated self-training pipelines. |
| Approach: | They propose to fit unlabeled text with a Bayesian Gaussian Mixture Model and use class names to cluster them. |
| Outcome: | The proposed approach outperforms prompt-based methods on topic and sentiment datasets and outperformed previous studies significantly on unbalanced datasets. |