# Domain-Guided Task Decomposition with Self-Training for Detecting Personal Events in Social Media

arXiv: https://arxiv.org/abs/2004.10201 · HTML: https://mlsanity.com/paper/2004.10201/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Apr 2020.

## Built on by (most cited first, 5 of 5)

- [Semi-Supervised Text Classification via Self-Pretraining](https://mlsanity.com/paper/2109.15300/) (2021)
- [Multi-View Active Learning for Short Text Classification in User-Generated Data](https://mlsanity.com/paper/2112.02611/) (2021)
- [Multiple-Source Domain Adaptation via Coordinated Domain Encoders and Paired Classifiers](https://mlsanity.com/paper/2201.11870/) (2022)
- [View Distillation with Unlabeled Data for Extracting Adverse Drug Effects from User-Generated Data](https://mlsanity.com/paper/2105.11354/) (2021)
- [Neural Networks Against (and For) Self-Training: Classification with Small Labeled and Large Unlabeled Sets](https://mlsanity.com/paper/2401.00575/) (2023)

## Abstract (arXiv)

Mining social media content for tasks such as detecting personal experiences or events, suffer from lexical sparsity, insufficient training data, and inventive lexicons. To reduce the burden of creating extensive labeled data and improve classification performance, we propose to perform these tasks in two steps: 1. Decomposing the task into domain-specific sub-tasks by identifying key concepts, thus utilizing human domain understanding; and 2. Combining the results of learners for each key concept using co-training to reduce the requirements for labeled training data. We empirically show the effectiveness and generality of our approach, Co-Decomp, using three representative social media mining tasks, namely Personal Health Mention detection, Crisis Report detection, and Adverse Drug Reaction monitoring. The experiments show that our model is able to outperform the state-of-the-art text classification models--including those using the recently introduced BERT model--when small amounts of training data are available.