Papers with Support Vector Machines
Exploiting Careful Design of SVM Solution for Aspect-term Sentiment Analysis (2024.findings-emnlp)
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| Challenge: | Aspect-term sentiment analysis (ATSA) identifies fine-grained sentiments towards specific aspects of text. |
| Approach: | They propose a pipeline to predict fine-grained sentiments for specific aspects of text . it decomposes the learning problem into multiple view subproblems and dynamically selects and constructs features with reinforcement learning. |
| Outcome: | The proposed pipeline surpasses SVM-based methods in predictive accuracy while maintaining a faster inference speed and significantly reducing the number of model parameters. |
D-CALM: A Dynamic Clustering-based Active Learning Approach for Mitigating Bias (2023.findings-acl)
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| Challenge: | Infusing clustering with active learning with AL can overcome the bias issue of both AL and traditional annotation methods while exploiting AL’s annotation efficiency. |
| Approach: | They propose an algorithm that dynamically adjusts clustering and annotation efforts in response to an estimated classifier error-rate. |
| Outcome: | The proposed algorithm outperforms baseline AL approaches with pretrained transformers and traditional Support Vector Machines on eight datasets for emotion, hatespeech, dialog act, and book type detection tasks. |
Distributional Term Set Expansion (L18-1)
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| Challenge: | Iterative term set expansion methods for distributional semantic models are used to label terms belonging to a sought after term set. |
| Approach: | They compare iterative term set expansion methods for distributional semantic models to the Simple Margin method, an active learning approach to classification using Support Vector Machines. |
| Outcome: | The proposed methods outperform centrality and classification based methods for distributional semantic models over five different term sets. |
Crowdsourcing Regional Variation Data and Automatic Geolocalisation of Speakers of European French (L18-1)
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Jean-Philippe Goldman, Yves Scherrer, Julie Glikman, Mathieu Avanzi, Christophe Benzitoun, Philippe Boula de Mareüil
| Challenge: | a crowdsourcing platform is used to collect linguistic data and document language use, with a focus on regional variation in European French. |
| Approach: | They propose a crowdsourcing platform to collect linguistic data and document language use with a special focus on regional variation in European French. |
| Outcome: | The proposed platform collects linguistic data and documents language use with a special focus on regional variation in European French. |