Query-based topic detection using concepts and named entities

Gialampoukidis, Ilias; Liparas, Dimitris; Vrochidis, Stefanos; Kompatsiaris, Ioannis

Query-based topic detection using concepts and named entities

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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.

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.

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.