The nested Indian buffet process for flexible topic modeling

Jen-Tzung Chien*, Ying Lan Chang

*Corresponding author for this work

Research output: Contribution to journalConference article

1 Scopus citations

Abstract

This paper presents a flexible topic model based on the nested Indian buffet process (nIBP). The flexibility is achieved by relaxing three constraints: (1) number of topics is fixed, (2) topics are independent, and (3) topic hierarchy for a document is limited by a single tree path. Bayesian nonparametric learning is conducted to build a tree model where the number of topics and the topic hierarchies are automatically learnt from the given data. In particular, we propose the nIBP to construct the topic mixture model for representation of heterogeneous documents where the mixture components are flexibly selected from tree nodes or dishes that a document or customer chooses in Indian buffet process. The selection is performed in a nested and hierarchical manner. The experiments on document representation show the benefits of using the proposed nIBP.

Original languageEnglish
Pages (from-to)1434-1437
Number of pages4
JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
StatePublished - 1 Jan 2014
Event15th Annual Conference of the International Speech Communication Association: Celebrating the Diversity of Spoken Languages, INTERSPEECH 2014 - Singapore, Singapore
Duration: 14 Sep 201418 Sep 2014

Keywords

  • Bayesian learning
  • Indian buffet process
  • Structural learning
  • Topic model

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