Papers with Multi-label document classification
SEP-MLDC: A Simple and Effective Paradigm for Multi-Label Document Classification (2025.findings-naacl)
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| Challenge: | Existing methods focus on optimizing document features, overlooking the potential of high-quality label features to enhance classification performance. |
| Approach: | They propose a multi-label document classification paradigm that utilizes large language models to expand the label content and generate pseudo-samples for the tail categories. |
| Outcome: | The proposed method significantly outperforms state-of-the-art models. |
Beyond Text: Incorporating Metadata and Label Structure for Multi-Label Document Classification using Heterogeneous Graphs (2021.emnlp-main)
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| Challenge: | Existing methods for multi-label document classification ignore the heterogeneous graphical structures of metadata and labels. |
| Approach: | They propose a neural network based approach to multi-label document classification that uses two heterogeneous graphs to model metadata and labels. |
| Outcome: | The proposed approach outperforms state-of-the-art models on two benchmark datasets. |
Effective Convolutional Attention Network for Multi-label Clinical Document Classification (2021.emnlp-main)
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| Challenge: | a large number of medical encounters need to be coded everyday due to long document sets and large label set. |
| Approach: | They propose a convolutional attention network for multi-label document classification problem . they use convolution-based encoders and convolution networks to aggregate information across documents . |
| Outcome: | The proposed model outperforms prior best model and multilingual Transformer model on a widely used dataset in the medical domain. |