Papers with semi-supervised manner
A Dual-View Approach to Classifying Radiology Reports by Co-Training (2024.lrec-main)
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| Challenge: | Using the structure of a radiology report, we propose a co-training approach to train two machine learning models using the dual views of MRI and CT data. |
| Approach: | They propose a co-training approach where two machine learning models are built upon the Findings and Impression sections and use each other's information to boost performance with massive unlabeled data in a semi-supervised manner. |
| Outcome: | The proposed model outperforms supervised and semi-supervised methods in a public health surveillance study and outperformed existing methods. |
Learning to Robustly Aggregate Labeling Functions for Semi-supervised Data Programming (2022.findings-acl)
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Ayush Maheshwari, Krishnateja Killamsetty, Ganesh Ramakrishnan, Rishabh Iyer, Marina Danilevsky, Lucian Popa
| Challenge: | supervised machine learning requires large amounts of labeled data to train models. |
| Approach: | They propose a framework to generate human-interpretable labeling functions . they propose to learn a model on the same labeled dataset and unlabeled data . |
| Outcome: | The proposed framework outperforms prior approaches on several text classification datasets. |
A Pseudo Label based Dataless Naive Bayes Algorithm for Text Classification with Seed Words (C18-1)
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| Challenge: | Existing supervised text classifications require a large number of manually labeled documents. |
| Approach: | They develop a pseudo-label based dataless Naive Bayes classifier with seed words . they initialize pseudo-labels for each document using seed word occurrences . |
| Outcome: | The proposed classifier outperforms traditional supervised text classification algorithms with seed words on an imbalanced dataset. |
Summarize-then-Answer: Generating Concise Explanations for Multi-hop Reading Comprehension (2021.emnlp-main)
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| Challenge: | Recent approaches to multi-hop Reading Comprehension (RC) have greatly improved its explainability, models ability to explain their own answers. |
| Approach: | They propose to generate a question-focused abstractive summary of input paragraphs and feed it to an RC system. |
| Outcome: | The proposed explanation generates more compact explanations than an extractive explainer with limited supervision while maintaining sufficiency. |
Large Language and Protein Assistant for Protein-Protein Interactions Prediction (2025.acl-long)
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Peng Zhou, Pengsen Ma, Jianmin Wang, Xibao Cai, Haitao Huang, Wei Liu, Longyue Wang, Lai Hou Tim, Xiangxiang Zeng
| Challenge: | Existing methods for predicting protein-protein interactions oversimplify the problem of PPI prediction in a semi-supervised manner. |
| Approach: | They propose a multimodal large language model that integrates proteins and PPI networks. |
| Outcome: | Experiments show that LLaPA can predict protein-protein interactions (mPPI) types and affinities based on sequence data. |
Instances and Labels: Hierarchy-aware Joint Supervised Contrastive Learning for Hierarchical Multi-Label Text Classification (2023.findings-emnlp)
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| Challenge: | Existing approaches to hierarchical multi-label text classification (HMTC) ignore the correlation between similar samples and introduce noise . |
| Approach: | They propose a semi-supervised method that uses a label hierarchy to bring text and label embeddings closer to each other by supervised contrastive learning. |
| Outcome: | The proposed method bridges the gap between supervised contrastive learning and HMTC by bringing text and label embeddings closer. |