Títol:
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Query-based topic detection using concepts and named entities
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Autor/a:
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Gialampoukidis, Ilias; Liparas, Dimitris; Vrochidis, Stefanos; Kompatsiaris, Ioannis
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Abstract:
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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. |
Abstract:
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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. |
Abstract:
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This work was supported by the projects MULTISENSOR (FP7-610411) and KRISTINA (H2020-645012), funded by the European Commission. |
Drets:
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© The authors. Atribución-NoComercial-SinDerivadas 3.0 España (CC BY-NC-ND 3.0 ES)
https://creativecommons.org/licenses/by-nc-nd/3.0/es/deed.es
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Tipus de document:
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Objecte de conferència Article - Versió publicada |
Publicat per:
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CEUR Workshop Proceedings
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