Applying hlda to Practical Topic Modeling
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1 Joseph Heng CIST Lab of BUPT March 17, 2013
2 Outline 1 HLDA Discussion 2 the nested CRP GEM Distribution Dirichlet Distribution Posterior Inference
3 Outline 1 HLDA Discussion 2 the nested CRP GEM Distribution Dirichlet Distribution Posterior Inference 3 Manual Modeling Procedure Empirical Results
4 Outline 1 HLDA Discussion 2 the nested CRP GEM Distribution Dirichlet Distribution Posterior Inference 3 Manual Modeling Procedure Empirical Results 4
5 Outline 1 HLDA Discussion 2 the nested CRP GEM Distribution Dirichlet Distribution Posterior Inference 3 Manual Modeling Procedure Empirical Results 4
6 Background HLDA Discussion The Goal in Our Paper. HLDA has been proved to be a powerful tool. One of the bottlenecks which prevent its large-scale application is that we cannot find a quick and effective approach to modeling new data properly. There exist lots of factors, eg. hyper-parameter settings, uncertainty of random algorithms and features of different corpus. Probabilistic Topic Models Topic models are algorithms for discovering the main themes that pervade a large and otherwise unstructured collection of documents. Topic models can organize the collection according to the discovered themes. LDA-related LDA and other topic models are part of the larger field of probabilistic modeling. In generative probabilistic modeling, we treat our data as arising from a generative process that includes hidden variables.
7 Merits of HLDA HLDA Discussion 1 Generative process for documents [2] 2 Posterior approximate inference with Gibbs sampling [1] p(z d,n z (d,n), c, w, π, η) p(z d,n z d, n, m, π)p(z d,n z, c, w (d,n), η) (1) p(c d w, c d, z, η, γ) p(c d c d, γ)p(w d c, w d, z, η) (2)
8 Merits of HLDA HLDA Discussion 1 Generative process for documents [2] 2 Posterior approximate inference with Gibbs sampling [1] p(z d,n z (d,n), c, w, π, η) p(z d,n z d, n, m, π)p(z d,n z, c, w (d,n), η) (1) p(c d w, c d, z, η, γ) p(c d c d, γ)p(w d c, w d, z, η) (2) 3 Assessing convergence and approximating the mode. L (t) = log p(c (t) 1:D, z(t) 1:D, z 1:D η, γ, m, π) (3)
9 Merits of HLDA HLDA Discussion 1 Generative process for documents [2] 2 Posterior approximate inference with Gibbs sampling [1] p(z d,n z (d,n), c, w, π, η) p(z d,n z d, n, m, π)p(z d,n z, c, w (d,n), η) (1) p(c d w, c d, z, η, γ) p(c d c d, γ)p(w d c, w d, z, η) (2) 3 Assessing convergence and approximating the mode. L (t) = log p(c (t) 1:D, z(t) 1:D, z 1:D η, γ, m, π) (3)
10 Practical Difficulty HLDA Discussion What s practical problem [3] when using hdla to topic modeling? Why unified framework?
11 Practical Difficulty HLDA Discussion What s practical problem [3] when using hdla to topic modeling? Why unified framework?
12 Practical Difficulty HLDA Discussion What s practical problem [3] when using hdla to topic modeling? Why unified framework? Figure : unified analysis framework with two clues
13 Generative Process the nested CRP GEM Distribution Dirichlet Distribution Posterior Inference Document are assumed to be drawn from the following process. For each node k T in the infinite tree, draw a topic β k Dirichlet(γ). For each document, d 1,2,...,D Draw C d ncrp(γ) to choose the path Draw a distribution over levels in the tree, θ d m, π GEM(m, π). For each word, Choose level Z d,n θ Mult(θ d ). Choose word W d,n Z d,n, C d, β Mult(β cd, [Z d,n ).
14 ncrp the nested CRP GEM Distribution Dirichlet Distribution Posterior Inference to CRP algorithm γ parameters Experiments with γ Comparision
15 GEM the nested CRP GEM Distribution Dirichlet Distribution Posterior Inference The Different View of DP Parameters m and π Experiments with m and π
16 Dirichlet Process the nested CRP GEM Distribution Dirichlet Distribution Posterior Inference 1 Experiment with parameter 2 Relationship with Three above.
17 Iterator Convergency the nested CRP GEM Distribution Dirichlet Distribution Posterior Inference Monte Carlo Markov Chain (Collapsed) Gibbs Sampling Algorithm
18 Manual Modeling Procedure Empirical Results Tree Depth
19 Manual Modeling Procedure Empirical Results Tree Depth theme\depth XTUS IRSA LTFC EUDC GBAB
20 Manual Modeling Procedure Empirical Results Tree Depth Sampling or not
21 Manual Modeling Procedure Empirical Results Tree Depth Sampling or not
22 Manual Modeling Procedure Empirical Results Tree Depth Sampling or not Corpus features
23 Manual Modeling Procedure Empirical Results Tree Depth Sampling or not Corpus features
24 Manual Modeling Procedure Manual Modeling Procedure Empirical Results Generate hlda input and extract features from corpus. Approximate depth of tree. Topic parameter for each level. ncrp parameter for non-leaf levels. m, π parameter for words allocations. Sampling for hyper-parameters or not.
25 Empirical Results Manual Modeling Procedure Empirical Results Experiments have been conducted with the guide of modeling procedure above with only three modifications to the settings. theme level#1 level#2 hlda#1 hlda#2 score XTUS EUDC IRSA HCDH CQWF SBAG GBAB LTFC MACO NOHN
26 Asli C and Dilek H. A hybrid hierarchical model for multi-document summarization. ACL 10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages , Blei David, Andrew Ng, and Jordan. Latent dirichlet allocation. Journal of Machine Learning Research, Paisley J, Wang C, Blei D M, and Jordan. Nested hierarchical dirichlet proceses. arxiv preprint arxiv: , 2012.
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