Constraint-Based Open-Domain Question Answering Using Knowledge Graph Search

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9924)

Abstract

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.

Keywords

Question answering Constrained conditional models Knowledge graph Vector representation 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Faculty of Mathematics and Physics, Institute of Formal and Applied LinguisticsCharles University in PraguePraha 1Czech Republic

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