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 |