Using Multi-Label Classification for Improved Question Answering

2017-10-24 · Ricardo Usbeck, Michael Hoffmann, Michael Röder, Jens Lehmann, Axel-Cyrille Ngonga Ngomo

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.

📄 PDF Abstract BibTeX arXiv:1710.08634

Code (1)

dice-group/NLIWOD/tree/master/qa.ml 공식 구현

Tasks

Classification General Classification Multi-Label Classification MUlTI-LABEL-ClASSIFICATION Question Answering

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