Details

Autor(en) / Beteiligte

• Chen, Peixian
• Zhang, Nevin L.
• Liu, Tengfei
• Poon, Leonard K.M.
• Chen, Zhourong
• Khawar, Farhan

Titel

Latent tree models for hierarchical topic detection

Ist Teil von

• Artificial intelligence, 2017-09, Vol.250, p.105-124

Ort / Verlag

Amsterdam: Elsevier B.V

Erscheinungsjahr

2017

Link zum Volltext

Quelle

Electronic Journals Library

Beschreibungen/Notizen

• We present a novel method for hierarchical topic detection where topics are obtained by clustering documents in multiple ways. Specifically, we model document collections using a class of graphical models called hierarchical latent tree models (HLTMs). The variables at the bottom level of an HLTM are observed binary variables that represent the presence/absence of words in a document. The variables at other levels are binary latent variables that represent word co-occurrence patterns or co-occurrences of such patterns. Each latent variable gives a soft partition of the documents, and document clusters in the partitions are interpreted as topics. Latent variables at high levels of the hierarchy capture long-range word co-occurrence patterns and hence give thematically more general topics, while those at low levels of the hierarchy capture short-range word co-occurrence patterns and give thematically more specific topics. In comparison with LDA-based methods, a key advantage of the new method is that it represents co-occurrence patterns explicitly using model structures. Extensive empirical results show that the new method significantly outperforms the LDA-based methods in term of model quality and meaningfulness of topics and topic hierarchies.