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Knowledge Discovery of Multiple-topic Document using Parametric Mixture Model with Dirichlet Prior
Published on Sep 14, 20073649 Views
Documents, such as those seen onWikipedia and Folksonomy, have tended to be assigned with multiple topics as a meta-data. Therefore, it is more and more important to analyze a relationship between a d
Chapter list
Knowledge Discovery of Multiple-topic Document using Parametric Mixture Model with Dirichlet Prior 00:00
Contents 100:18
What kind of relationship between a document and topics exists?00:26
Contents 201:38
Probabilistic Generative Model01:42
PMM-part0103:14
PMM-part0204:14
Contents 304:45
Dirichlet distribution on mixture ratio (π1,π2,π3) 04:48
Estimate of Mixture Ratio06:09
Graphical Model09:03
Contents 409:42
Multiple-topic classification09:45
Evaluation by F-measure10:11
F-measure:PDMM vs PMM 1/211:39
Precision:PDMM vs PMM12:35
Recall:PDMM vs PMM12:47
F-measure:PDMM vs PMM 2/212:54
Contents 513:48
Word Ranking13:54
[Female(0.499)], [Male(0.460)] [Biological Markers(0.041)] 14:57
[Rats(0.411)], [Child(0.352)] [Incidence(0.237)]16:08
[Female(0.442)], [Animals(0.437)] [Pregnancy(0.066)],[Glucose(0.055)]16:23
[Pregnancy(0.502)],[Glucose(0.498)]17:11
Summary17:59
[Thank] [you] [for] [listening] [!]18:27