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

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...

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Bibliographic Details
Published inText, Speech, and Dialogue pp. 28 - 36
Main Authors Aghaebrahimian, Ahmad, Jurčíček, Filip
Format Book Chapter
LanguageEnglish
Published Cham Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
Constrained conditional models
Knowledge graph
Question answering
Vector representation
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Summary: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.
ISBN:9783319455099
3319455095
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-319-45510-5_4