Using Multi-Label Classification for Improved Question Answering
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Using Multi-Label Classification for Improved Question Answering. / Usbeck, Ricardo; Hoffmann, Michael; Röder, Michael et al.
Conference XXX. 2017.Research output: Contributions to collected editions/works › Article in conference proceedings › Research
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TY - CHAP
T1 - Using Multi-Label Classification for Improved Question Answering
AU - Usbeck, Ricardo
AU - Hoffmann, Michael
AU - Röder, Michael
AU - Lehmann, Jens
AU - Ngomo, Axel-Cyrille Ngonga
N1 - 15 pages, 4 Tables, 3 Figues
PY - 2017/10/24
Y1 - 2017/10/24
N2 - 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.
AB - 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.
KW - cs.IR
KW - cs.CL
KW - Informatics
U2 - 10.48550/arXiv.1710.08634
DO - 10.48550/arXiv.1710.08634
M3 - Article in conference proceedings
BT - Conference XXX
ER -