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Latent Semantic Variable Models
Published on Feb 25, 200729741 Views
In the context of information retrieval and natural language processing, latent variable models are quite useful in modeling and discovering hidden structure that often leads to "semantic" data repres
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Chapter list
Latent Semantic Variable Models 00:01
Introduction Information Retrieval & Latent Semantic Indexing Probabilistic Latent Semantic Indexing Semantic Features for Text Categorizisation Probabilistic HITS Collaborative Filtering Concl00:43
Latent Structure01:19
Matrix Decomposition02:23
Introduction Information Retrieval & Latent Semantic Indexing Probabilistic Latent Semantic Indexing Semantic Features for Text Categorizisation Probabilistic HITS Collaborative Filtering Concl03:13
Searching & Finding03:23
Ad Hoc Retrieval04:14
Document-Term Matrix04:55
A 100 Millionths of a Typical Document-Term Matrix06:47
Robust Information Retrieval — Beyond Keyword-based Search07:45
Challenges09:48
Latent Semantic Analysis10:41
Singular Value Decomposition12:18
Low-rank Approximation13:26
LSA Decomposition14:35
Latent Semantic Analysis15:21
Introduction Information Retrieval & Latent Semantic Indexing Probabilistic Latent Semantic Indexing Semantic Features for Text Categorizisation Probabilistic HITS Collaborative Filtering Concl16:31
Search as Statistical Inference16:39
Language Model Paradigm in IR19:00
Language Model Paradigm20:10
Language Model Paradigm20:46
Naive Approach21:13
Estimation Problem21:51
Probabilistic Latent Semantic Analysis22:38
pLSA – Latent Variable Model24:13
pLSA: Matrix Decomposition25:25
pLSA: Graphical Model27:19
pLSA via Likelihood Maximization28:37
Expectation Maximization Algorithm29:26
EM Algorithm: Derivation31:22
Tempered EM Algorithm31:41
Example (1)35:05
Example (2)37:32
Experimental Evaluation38:13
Live Implementation39:41
Latent Dirichlet Allocation43:08
Introduction Information Retrieval & Latent Semantic Indexing Probabilistic Latent Semantic Indexing Semantic Features for Text Categorizisation Probabilistic HITS Collaborative Filtering Concl46:17
Concept-based Text Categorization46:32
Terms & Concepts as Features47:32
Improvements on Reuters-2157848:36
Improvements on OHSUMED8749:32
Literature & Related Work50:59
Introduction Information Retrieval & Latent Semantic Indexing Probabilistic Latent Semantic Indexing Semantic Features for Text Categorizisation Probabilistic HITS Collaborative Filtering Concl52:00
Probabilistic HITS52:01
Finding Latent Web Communities53:40
Decomposing the Web Graph54:06
Linking Hyperlinks and Content55:01
Example: Ulysses56:06
Literature & Related Work57:38
Introduction Information Retrieval & Latent Semantic Indexing Probabilistic Latent Semantic Indexing Semantic Features for Text Categorizisation Probabilistic HITS Collaborative Filtering Concl57:40
Predictions & Recommendations57:51