Papers with anomaly detection algorithms
Semantic Novelty Detection in Natural Language Descriptions (2021.emnlp-main)
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Nianzu Ma, Alexander Politowicz, Sahisnu Mazumder, Jiahua Chen, Bing Liu, Eric Robertson, Scott Grigsby
| Challenge: | Existing novelty detection algorithms are coarse-grained, working at the document or topic level. |
| Approach: | They propose to use a fine-grained semantic novelty detection problem to solve a novel novel scene problem. |
| Outcome: | The proposed model outperforms baseline models on the proposed task by large margins. |
Enhancing Two Steps Textual Anomaly Detection through Anisotropy Mitigation (2026.acl-long)
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| Challenge: | Recent approaches to anomaly detection focus on embeddings from pre-trained models . however, the geometric properties of pre-training embedders can hinder detection algorithms . |
| Approach: | They propose to apply anomaly detection algorithms to embeddings from pre-trained models to improve accuracy. |
| Outcome: | The proposed approach improves similarity-trained models by adapting embeddings to assumptions made by classical detection algorithms. |