Abstract
We introduce a highly scalable approach for open-domain question answering with no dependence on any logical form to surface form map** data set or any linguistic analytic tool such as POS tagger or named entity recognizer. We define our approach under the Constrained Conditional Models framework which lets us scale to a full knowledge graph with no limitation on the size. On a standard benchmark, we obtained competitive results to state-of-the-art in open-domain question answering task.
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Notes
- 1.
Logical symbols are given in an abbreviated form to save space. For instance, the full form of the second type above is http://rdf.freebase.com/book/book_subject.
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Acknowledgments
This research was partially funded by the Ministry of Education, Youth and Sports of the Czech Republic under the grant agreement LK11221, core research funding, SVV project number 260 333 and GAUK 207-10/250098 of Charles University in Prague. This work has been using language resources distributed by the LINDAT/CLARIN project of the Ministry of Education, and Sports of the Czech Republic (project LM2010013). The authors gratefully appreciate Ondřej Dušek for his helpful comments on the final draft.
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Aghaebrahimian, A., Jurčíček, F. (2016). Constraint-Based Open-Domain Question Answering Using Knowledge Graph Search. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds) Text, Speech, and Dialogue. TSD 2016. Lecture Notes in Computer Science(), vol 9924. Springer, Cham. https://doi.org/10.1007/978-3-319-45510-5_4
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