We introduce a highly scalable approach for open-domain question answering with no dependence on any logical form to surface form mapping 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.
CITATION STYLE
Aghaebrahimian, A., & Jurčíček, F. (2016). Constraint-based open-domain question answering using knowledge graph search. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9924 LNCS, pp. 28–36). Springer Verlag. https://doi.org/10.1007/978-3-319-45510-5_4
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