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dc.contributor.authorQuoc, Viet Hung Nguyen
dc.contributor.authorChi, Thang Duong
dc.contributor.authorThanh, Tam Nguyen
dc.contributor.authorWeidlich, Matthias
dc.contributor.authorAberer, Karl
dc.contributor.authorYin, Hongzhi
dc.contributor.authorZhou, Xiaofang
dc.date.accessioned2018-06-25T12:30:35Z
dc.date.available2018-06-25T12:30:35Z
dc.date.issued2017
dc.identifier.issn1066-8888
dc.identifier.doi10.1007/s00778-017-0462-9
dc.identifier.urihttp://hdl.handle.net/10072/349518
dc.description.abstractThe amount of controversial issues being discussed on the Web has been growing dramatically. In articles, blogs, and wikis, people express their points of view in the form of arguments, i.e., claims that are supported by evidence. Discovery of arguments has a large potential for informing decision-making. However, argument discovery is hindered by the sheer amount of available Web data and its unstructured, free-text representation. The former calls for automatic text-mining approaches, whereas the latter implies a need for manual processing to extract the structure of arguments. In this paper, we propose a crowdsourcing-based approach to build a corpus of arguments, an argumentation base, thereby mediating the trade-off of automatic text-mining and manual processing in argument discovery. We develop an end-to-end process that minimizes the crowd cost while maximizing the quality of crowd answers by: (1) ranking argumentative texts, (2) pro-actively eliciting user input to extract arguments from these texts, and (3) aggregating heterogeneous crowd answers. Our experiments with real-world datasets highlight that our method discovers virtually all arguments in documents when processing only 25% of the text with more than 80% precision, using only 50% of the budget consumed by a baseline algorithm.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherSpringer Link
dc.relation.ispartofpagefrom511
dc.relation.ispartofpageto535
dc.relation.ispartofissue4
dc.relation.ispartofjournalVLDB Journal
dc.relation.ispartofvolume26
dc.subject.fieldofresearchData management and data science
dc.subject.fieldofresearchDistributed computing and systems software
dc.subject.fieldofresearchInformation systems
dc.subject.fieldofresearchDatabase systems
dc.subject.fieldofresearchcode4605
dc.subject.fieldofresearchcode4606
dc.subject.fieldofresearchcode4609
dc.subject.fieldofresearchcode460505
dc.titleArgument discovery via crowdsourcing
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.hasfulltextNo Full Text
gro.griffith.authorNguyen, Henry
gro.griffith.authorNguyen, Thanh Tam


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