clustering search results using plsa
DESCRIPTION
Clustering Search Results Using PLSA. 洪春涛. Outlines. Motivation Introduction to document clustering and PLSA algorithm Working progress and testing results. Motivation. Current Internet search engines are giving us too much information - PowerPoint PPT PresentationTRANSCRIPT
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Clustering Search Results Using PLSA
洪春涛
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Outlines
• Motivation
• Introduction to document clustering and PLSA algorithm
• Working progress and testing results
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Motivation
• Current Internet search engines are giving us too much information
• Clustering the search results may help find the desired information quickly
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The writer Truman Capote
The film Truman Capote
A demo of the searching result from Google.
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Document clustering
• Put the ‘similar’ documents together
=> How do we define ‘similar’?
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Vector Space Model of documents
The Vector Space Model (VSM) sees a document as a vector of terms:
Doc1: I see a bright future.
Doc2: I see nothing.
I see a bright future nothing
doc1 1 1 1 1 1 0
doc2 1 1 0 0 0 1
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The distance between doc1 and doc2 is then defined as
1 2cos( 1, 2)
| 1| * | 2 |
doc docdoc doc
doc doc
Cosine as Distance Between Documents
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Problems with cosine similarity
• Synonymy: different words may have the same meaning– Car manufacturer=automobile maker
• Polysemy: a word may have several different meanings- ‘Truman Capote’ may mean the writer or the film => We need a model that reflects the ‘meaning’
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Probabilistic Latent Semantic Analysis
Graphical model of PLSA:
( , ) ( ) ( | )
( | ) ( | ) ( | )z Z
P d w P d P w d
P w d P w z P z d
D1
Z1
W1
D: document
Z: latent class
W: word
These can also be written as:
( , ) ( ) ( | ) ( | )z Z
P d w P z P w z P d z
D2
Z1
W1 W1
0.10.9
0.30.7
D2
0.8
0.2
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• Through Maximization Likelihood, one gets the estimated parameters:
P(d|z)This is what we want – a document-topic matrix
that reflects meanings of the documents.
P(w|z)
P(z)
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Our approach
1. Get the P(d|z) matrix by PLSA, and
2. Use k-means clustering algorithm on the matrix
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Problems with this approach
• PLSA takes too much time
solution: optimization & parallelization
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Algorithm Outline
Expectation Maximization(EM) Algorithm:
Tempered EM:
E-step:
M-step:
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Basic Data Structures
p_w_z_current, p_w_z_prev:dense double matrix W*Z
p_d_z_current, p_d_z_prev:dense double matrix D*Z
p_z_current, p_z_prev:double array Z
n_d_w:sparse integer matrix N
Lemur Implementation
• In-need calculation of p_z_d_w
• Computational complexity:O(W*D*Z2)
• For the new3 dataset containing 9558 documents, 83487 unique terms, it takes days to finish a TEM iteration
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Optimization of the Algorithm
• Reduce complexity– calculate p_z_d_w just once in an iteration– complexity reduced to O(N*Z)
• Reduce cache miss by reverting loopsfor(int d=1;d<numDocs;d++){
for(int w=0;w<numTermsInThisDoc;w++){
for(int z=0;z<numZ;z++){
….
}
}
}
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Parallelization: Access Pattern
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Data Race
solution: divide the co-occurrence table into blocks
Block Dispatching Algorithm
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Block Dividing Algorithm
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cranmed
Experiment Setup
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Speedup
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HPC134 Tulsa
Memory Bandwidth Usage
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Memory Related Pipeline Stalls
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Available Memory Bandwidth of the Two Machines
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END
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Backup slides
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Test Results
PLSA VSM
Tr23 0.4977 0.5273
K1b 0.8473 0.5724
sports 0.7575 0.5563
Table 1. F-score of PLSA and VSM
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sizeZ 10 20 50 100
Lemur 29 48 263 1015
Optimized 2 3.2 7 13
Table 2. Time used in one EM iteration (in second)
Uses the k1b dataset
(2340 docs, 21247 unique terms, 530374 terms)
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Thanks!