# Using Multi-Label Classification for Improved Question Answering

Source: [Using Multi-Label Classification for Improved Question Answering](<https://www.sota2.com/research/paper/68fa63be1e209131cea0098a>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:49:10.785Z

Authors: Ricardo Usbeck, Michael Hoffmann, Michael R\\"oder, Jens Lehmann, Axel-Cyrille Ngonga Ngomo

Published: 2017-10-24T07:40:16.000Z

## Abstract

A plethora of diverse approaches for question answering over RDF data have been developed in recent years. While the accuracy of these systems has increased significantly over time, most systems still focus on particular types of questions or particular challenges in question answering. What is a curse for single systems is a blessing for the combination of these systems. We show in this paper how machine learning techniques can be applied to create a more accurate question answering metasystem by reusing existing systems. In particular, we develop a multi-label classification-based metasystem for question answering over 6 existing systems using an innovative set of 14 question features. The metasystem outperforms the best single system by 14% F-measure on the recent QALD-6 benchmark. Furthermore, we analyzed the influence and correlation of the underlying features on the metasystem quality.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1710.08634>)

[PDF](<https://arxiv.org/pdf/1710.08634.pdf>)