Incorporating semantic similarity metrics into hierarchical dataset selection can enhance the contextual relevance and quality of selected data subsets.

Motivation

While hierarchical structures provide a powerful framework for dataset selection, they may not fully capture the semantic relationships between data points, potentially leading to less contextually relevant selections. By integrating semantic similarity, the approach could improve the contextual and thematic coherence of the selected datasets.

Proposed Method

Develop an enhanced version of the hierarchical dataset selection algorithm that incorporates semantic similarity metrics, such as word embeddings or ontology-based methods. Conduct experiments comparing the performance of this new method against the original by evaluating the accuracy and relevance of model predictions trained on these datasets across multiple domains, such as natural language processing and image classification.

Expected Contribution

This research would demonstrate how semantic information can be leveraged to improve dataset selection processes, potentially leading to more effective machine learning models.

Required Resources

Access to semantic analysis tools, multiple large-scale datasets from different domains, and moderate compute resources for model training and evaluation.

Source Paper

Hierarchical Dataset Selection for High-Quality Data Sharing

View Paper Details →