Author Tree-Structured Hierarchical Dirichlet Process

Md Hijbul Alam*, Jaakko Peltonen, Jyrki Nummenmaa, Kalervo Järvelin

*Tämän työn vastaava kirjoittaja

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference contributionScientificvertaisarvioitu

1 Sitaatiot (Scopus)

Abstrakti

Three key aspects of online discussion venues are the multitude of participants, the underlying trends of content, and the structure of the venue. However, most models are unable to take into account all three of these. In hierarchically organized message forums, authors may participate differently at multiple levels of sections, with different interests and contributions across the hierarchy. Well-designed probabilistic models of online discussion are applicable to many tasks such as prediction of future content or authorship attribution. However, traditional models such as Hierarchical Dirichlet Processes (HDPs) do not fully take into account authors, and are further unable to fully take into account deep hierarchical venues where documents can arise at all tree nodes. We introduce the Author Tree-structured Hierarchical Dirichlet Process (ATHDP), allowing Dirichlet process based topic modeling of both text content and authors over a given tree structure of arbitrary size and height. Experiments on six hierarchical discussion data sets demonstrate better performance of ATHDP compared to traditional HDP based alternatives in terms of perplexity and authorship attribution accuracy.

AlkuperäiskieliEnglanti
OtsikkoDiscovery Science - 21st International Conference, DS 2018, Proceedings
ToimittajatMichelangelo Ceci, Larisa Soldatova, Joaquin Vanschoren, George Papadopoulos
Sivut311-327
Sivumäärä17
DOI - pysyväislinkit
TilaJulkaistu - 1 tammik. 2018
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaInternational Conference on Discovery Science - Limassol, Kypros
Kesto: 29 lokak. 201831 lokak. 2018
Konferenssinumero: 21

Julkaisusarja

NimiLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Vuosikerta11198 LNAI
ISSN (painettu)0302-9743
ISSN (elektroninen)1611-3349

Conference

ConferenceInternational Conference on Discovery Science
LyhennettäDS
Maa/AlueKypros
KaupunkiLimassol
Ajanjakso29/10/201831/10/2018

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