Please use this identifier to cite or link to this item: https://oar.tib.eu/jspui/handle/123456789/5077
Full metadata record
DC FieldValueLanguage
dc.rights.licenseCC BY 4.0 Unportedger
dc.contributor.authorSingh, K.-
dc.contributor.authorRadhakrishna, A.S.-
dc.contributor.authorBoth, A.-
dc.contributor.authorShekarpour, S.-
dc.contributor.authorLytra, I.-
dc.contributor.authorUsbeck, R.-
dc.contributor.authorVyas, A.-
dc.contributor.authorKhikmatullaev, A.-
dc.contributor.authorPunjani, D.-
dc.contributor.authorLange, C.-
dc.contributor.authorVidal, M.E.-
dc.contributor.authorLehmann, J.-
dc.contributor.authorAuer, S.-
dc.date.accessioned2020-07-21T09:12:28Z-
dc.date.available2020-07-21T09:12:28Z-
dc.date.issued2018-
dc.identifier.urihttp://dx.doi.org/10.34657/3706-
dc.identifier.urihttps://oar.tib.eu/jspui/handle/123456789/5077
dc.description.abstractModern question answering (QA) systems need to flexibly integrate a number of components specialised to fulfil specific tasks in a QA pipeline. Key QA tasks include Named Entity Recognition and Disambiguation, Relation Extraction, and Query Building. Since a number of different software components exist that implement different strategies for each of these tasks, it is a major challenge to select and combine the most suitable components into a QA system, given the characteristics of a question. We study this optimisation problem and train classifiers, which take features of a question as input and have the goal of optimising the selection of QA components based on those features. We then devise a greedy algorithm to identify the pipelines that include the suitable components and can effectively answer the given question. We implement this model within Frankenstein, a QA framework able to select QA components and compose QA pipelines. We evaluate the effectiveness of the pipelines generated by Frankenstein using the QALD and LC-QuAD benchmarks. These results not only suggest that Frankenstein precisely solves the QA optimisation problem but also enables the automatic composition of optimised QA pipelines, which outperform the static Baseline QA pipeline. Thanks to this flexible and fully automated pipeline generation process, new QA components can be easily included in Frankenstein, thus improving the performance of the generated pipelines.eng
dc.language.isoeng-
dc.publisherNew York City : Association for Computing Machinery-
dc.relation.ispartofseriesThe Web Conference 2018 - Proceedings of the World Wide Web Conference, WWW 2018-
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/ger
dc.subjectQA frameworkeng
dc.subjectQuestion answeringeng
dc.subjectSemantic searcheng
dc.subjectSemantic webeng
dc.subjectSoftware reusabilityeng
dc.subjectNatural language processing systemseng
dc.subjectOptimizationeng
dc.subjectWorld Wide Webeng
dc.subjectAutomatic compositioneng
dc.subjectGeneration processeng
dc.subjectNamed entity recognitioneng
dc.subjectNumber of componentseng
dc.subjectOptimisation problemseng
dc.subjectQuestion answering systemseng
dc.subjectRelation extractioneng
dc.subjectSoftware componenteng
dc.subjectPipelineseng
dc.subject.ddc004-
dc.titleWhy reinvent the wheel: Let's build question answering systems togethereng
dc.typeconferenceObject-
dc.typearticle-
dc.typeText-
dc.description.versionpublishedVersioneng
local.accessRightsopenAccess-
wgl.contributorTIBger
wgl.subjectInformatikger
wgl.typeKonferenzbeitragger
dc.bibliographicCitation.firstPage1247-
dc.bibliographicCitation.lastPage1256-
dc.relation.doihttps://doi.org/10.1145/3178876.3186023-
local.identifier.doihttp://dx.doi.org/10.34657/3706-
Appears in Collections:Informationswissenschaften

Files in This Item:
File SizeFormat 
Singh et al 2018, Why Reinvent the Wheel.pdf1,42 MBAdobe PDFView/Open


This item is licensed under a Creative Commons License Creative Commons