# Classifying Documents within Multiple Hierarchical Datasets using Multi-Task Learning

## TL;DR

This paper presents a multi-task learning approach for classifying documents within hierarchical datasets like DMOZ and Wikipedia, demonstrating improved accuracy especially with limited training data.

## Contribution

It introduces a novel MTL method that leverages relationships across hierarchical datasets for document classification, outperforming traditional methods.

## Key findings

- MTL outperforms STL with fewer training examples
- The proposed approach improves classification accuracy
- Transfer learning shows comparable benefits

## Abstract

Multi-task learning (MTL) is a supervised learning paradigm in which the prediction models for several related tasks are learned jointly to achieve better generalization performance. When there are only a few training examples per task, MTL considerably outperforms the traditional Single task learning (STL) in terms of prediction accuracy. In this work we develop an MTL based approach for classifying documents that are archived within dual concept hierarchies, namely, DMOZ and Wikipedia. We solve the multi-class classification problem by defining one-versus-rest binary classification tasks for each of the different classes across the two hierarchical datasets. Instead of learning a linear discriminant for each of the different tasks independently, we use a MTL approach with relationships between the different tasks across the datasets established using the non-parametric, lazy, nearest neighbor approach. We also develop and evaluate a transfer learning (TL) approach and compare the MTL (and TL) methods against the standard single task learning and semi-supervised learning approaches. Our empirical results demonstrate the strength of our developed methods that show an improvement especially when there are fewer number of training examples per classification task.