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Query-based topic detection using concepts and named entities

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dc.contributor.author Gialampoukidis, Ilias
dc.contributor.author Liparas, Dimitris
dc.contributor.author Vrochidis, Stefanos
dc.contributor.author Kompatsiaris, Ioannis
dc.date.accessioned 2017-06-16T18:16:11Z
dc.date.available 2017-06-16T18:16:11Z
dc.date.issued 2016
dc.identifier.citation Gialampoukidis I, Liparas D, Vrochidis S, Kompatsiaris I. Query-based topic detection using concepts and named entities. In: Vrochidis S, Melero M, Wanner, Grivolla J, Estève Y. MMDA 2016 Multimodal Media Data Analytics Proceedings of the 1st International Workshop on Multimodal Media Data Analytics co-located with the 22nd European Conference on Artificial Intelligence (ECAI 2016); 2016 30 August; The Hague, Netherlands. [place unknown]: CEUR Workshop Proceedings, 2016. p. 18-22.
dc.identifier.uri http://hdl.handle.net/10230/32308
dc.description Comunicació presentada a: 1st International Workshop on Multimodal Media Data Analytics, celebrat juntament amb 22nd European Conference on Artificial Intelligence (ECAI 2016), el 30 d'agost de 2016 a La Haia, Holanda.
dc.description.abstract In this paper, we present a framework for topic detection in news articles. The framework receives as input the results retrieved from a query-based search and clusters them by topic. To this end, the recently introduced “DBSCAN-Martingale” method for automatically estimating the number of topics and the well-established Latent Dirichlet Allocation topic modelling approach for the assignment of news articles into topics of interest, are utilized. Furthermore, the proposed query-based topic detection framework works on high-level textual features (such as concepts and named entities) that are extracted from news articles. Our topic detection approach is tackled as a text clustering task, without knowing the number of clusters and compares favorably to several text clustering approaches, in a public dataset of retrieved results, with respect to four representative queries.
dc.description.sponsorship This work was supported by the projects MULTISENSOR (FP7-610411) and KRISTINA (H2020-645012), funded by the European Commission.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher CEUR Workshop Proceedings
dc.relation.ispartof Vrochidis S, Melero M, Wanner, Grivolla J, Estève Y. MMDA 2016 Multimodal Media Data Analytics Proceedings of the 1st International Workshop on Multimodal Media Data Analytics co-located with the 22nd European Conference on Artificial Intelligence (ECAI 2016); 2016 30 August; The Hague, Netherlands. [place unknown]: CEUR Workshop Proceedings, 2016. p. 18-22.
dc.rights © The authors. Atribución-NoComercial-SinDerivadas 3.0 España (CC BY-NC-ND 3.0 ES)
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/3.0/es/deed.es
dc.title Query-based topic detection using concepts and named entities
dc.type info:eu-repo/semantics/conferenceObject
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/645012
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/610411
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion


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