PWA-PEM for Latent Tree Model and Hierarchical Topic Detection - Intelligent Information Processing IX Access content directly
Conference Papers Year : 2018

PWA-PEM for Latent Tree Model and Hierarchical Topic Detection

Abstract

Hierarchical Latent Tree Analysis (HLTA) is a new method of topic detection. However, HLTA data input uses TF-IDF selection term, and relies on EM algorithm for parameter estimation. To solve this problem, a method of accelerating part of speech weight (PWA-PEM-HLTA) is proposed based on Progressive EM-HLTA (PEM-HLTA). Experimental results show that this method improves the execution efficiency of PEM-HLTA, averaging 4.9 times speed, and improves the speed of 6 times in the best case.
Fichier principal
Vignette du fichier
473854_1_En_19_Chapter.pdf (446.14 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-02197798 , version 1 (30-07-2019)

Licence

Attribution

Identifiers

Cite

Zhuchen Liu, Hao Chen, Jie Li, Yanhua Yu. PWA-PEM for Latent Tree Model and Hierarchical Topic Detection. 10th International Conference on Intelligent Information Processing (IIP), Oct 2018, Nanning, China. pp.183-191, ⟨10.1007/978-3-030-00828-4_19⟩. ⟨hal-02197798⟩
35 View
58 Download

Altmetric

Share

Gmail Facebook X LinkedIn More