Title :
Research on Chinese multi-document hierarchical topic modeling automatic evaluation methods
Author :
Yu Liu ; Lei Li ; Shuhong Wan ; Zhiqiao Gao
Author_Institution :
Center for Intell. Sci. & Technol., Beijing Univ. of Posts & Telecommun., Beijing, China
Abstract :
Hierarchical Latent Dirichlet Allocation (hLDA) has achieved good results in the supervised and unsupervised multi-document hierarchical topic modeling. However, the result is diversified. The results maintain randomness even with the same parameters. Thus, this paper proposed automatic evaluation methods for unsupervised multi-document hLDA modeling results over previous studies. This paper used 10 topics of corpus of ACL2013 multilingual multi-document summarization and found 90 topics of news as experimental corpus, then compared the different modeling results. The results showed that automatic evaluation method can provide a good reference for the optimization of the modeling results.
Keywords :
document handling; natural language processing; optimisation; unsupervised learning; Chinese multidocument hierarchical topic modeling automatic evaluation methods; automatic evaluation method; hierarchical latent Dirichlet allocation; optimization; supervised multidocument hierarchical topic modeling; unsupervised multidocument hLDA modeling; Clustering methods; Data models; Frequency estimation; Indexes; Manuals; Resource management; Semantics; Automatic Evaluation Methods; Hierarchical LDA; Hierarchical Topic Modeling;
Conference_Titel :
Cloud Computing and Intelligence Systems (CCIS), 2014 IEEE 3rd International Conference on
Print_ISBN :
978-1-4799-4720-1
DOI :
10.1109/CCIS.2014.7175776
