Papers with Multi-label text classification
Text2Topic: Multi-Label Text Classification System for Efficient Topic Detection in User Generated Content with Zero-Shot Capabilities (2023.emnlp-industry)
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Fengjun Wang, Moran Beladev, Ofri Kleinfeld, Elina Frayerman, Tal Shachar, Eran Fainman, Karen Lastmann Assaraf, Sarai Mizrachi, Benjamin Wang
| Challenge: | In the digital age, large-scale online travel platforms face the challenge of extracting valuable insights from massive volumes of textual data. |
| Approach: | They propose a model that uses a bi-encoder transformer architecture to extract structured information from textual data. |
| Outcome: | The proposed model outperforms state-of-the-art models with 92.9% micro mAP and 75.8% macro mA score compared to baseline models . the proposed model can be used to find hotel facilities and hotel rooms based on positive reviews . |
Label-Specific Document Representation for Multi-Label Text Classification (D19-1)
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| Challenge: | Existing methods to classify documents using labels only assign one label to document . multi-label text classification is a challenging task because of the huge amount of documents, words and labels. |
| Approach: | They propose a Label-Specific Attention Network (LSAN) to learn a label-specific document representation. |
| Outcome: | The proposed model outperforms state-of-the-art methods on four datasets . it can predict low-frequency labels, and it can be used in sentimental analysis . |
Label-Specific Dual Graph Neural Network for Multi-Label Text Classification (2021.acl-long)
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| Challenge: | Existing studies for multi-label text classification do not explore label-specific semantic components from documents. |
| Approach: | They propose a label-specific dual graph neural network that incorporates category information to learn label-related components from documents. |
| Outcome: | The proposed model outperforms state-of-the-art models on three benchmark datasets and achieves better performance with respect to tail labels. |
From Text Segmentation to Enhanced Representation Learning: A Novel Approach to Multi-Label Classification for Long Texts (2024.findings-emnlp)
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| Challenge: | Existing models rely on pre-trained language models, which have a maximum input sequence length of 512 tokens, and therefore have 'input length limitation'. |
| Approach: | They propose a text segmentation algorithm which guarantees to produce the optimal segmentation to address the issue of input length limitation caused by PLMs. |
| Outcome: | The proposed method improves both text and label representations on MLTC datasets, unraveling the intricate correlations between texts and labels. |
Towards Better Representations for Multi-Label Text Classification with Multi-granularity Information (2023.findings-emnlp)
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| Challenge: | Existing studies have shown that pre-trained language models generate word frequency-oriented text representations, causing texts with different labels to be closely distributed in a narrow region, which is difficult to classify. |
| Approach: | They propose a framework to refine the text representation for multi-label text classification using contrastive learning and multi-task learning modules. |
| Outcome: | The proposed framework improves the quality of the representations and yields stable and competitive improvements. |
Balancing Methods for Multi-label Text Classification with Long-Tailed Class Distribution (2021.emnlp-main)
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| Challenge: | Multi-label text classification is a challenging task because it requires capturing label dependencies. |
| Approach: | They propose to use distribution-balanced loss functions to solve label dependency problems in multi-label text classification by capturing label dependencies from a fixed-set of labels. |
| Outcome: | The proposed loss function addresses both the class imbalance and label linkage problems and outperforms other loss functions. |
DKEC: Domain Knowledge Enhanced Multi-Label Classification for Diagnosis Prediction (2024.emnlp-main)
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| Challenge: | Prior work focused on hierarchical label structures but neglected to incorporate external knowledge from medical guidelines. |
| Approach: | They propose to incorporate external knowledge from medical guidelines into domain knowledge enhanced classification for diagnosis prediction. |
| Outcome: | The proposed system outperforms state-of-the-art label-wise attention networks and transformer models on a real-world emergency medical services dataset and a public electronic health record dataset. |
MADE: A Living Benchmark for Multi-Label Text Classification with Uncertainty Quantification of Medical Device Adverse Events (2026.acl-long)
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| Challenge: | Existing MLTC benchmarks are saturated and may be affected by training data contamination. |
| Approach: | They propose a machine learning benchmark based on medical device adverse event reports . they establish baselines across 20 encoder- and decoder-only models . |
| Outcome: | The proposed benchmarks show that small fine-tuned models achieve the strongest head-to-tail accuracy while maintaining competitive UQ. |