Transcript Author-Topic Models - Stanford University

Modeling Documents
Amruta Joshi
Department of Computer Science
Stanford University
6th June 2005
Research in Algorithms for the InterNet
1
Outline

Topic Models
 Topic
Extraction2
 Author Information
 Modeling Topics
 Modeling Authors
 Author Topic Model
 Inference

Integrating topics and syntax
 Probabilistic
Models
 Composite Model
 Inference
Amruta Joshi, Stanford Univ.
Research in Algorithms for the InterNet
2
Motivation
Identifying content of a document
 Identifying its latent structure


More specifically
 Given
a collection of documents we want to
create a model to collect information about
Authors
 Topics
 Syntactic constructs

Amruta Joshi, Stanford Univ.
Research in Algorithms for the InterNet
3
Topics & Authors

Why model topics?
 Observe
topic trends
 How documents relate to one-another
 Tagging abstracts

Why model authors’ interests?
 Identifying
what author writes about
 Identifying authors with similar interests
 Authorship attribution
 Creating reviewer lists
 Finding unusual work by an author
Amruta Joshi, Stanford Univ.
Research in Algorithms for the InterNet
4
Topic Extraction: Overview

Supervised Learning
Techniques
 Learn
from labeled document
collection
 But Unlabeled documents,
Rapidly changing fields (Yang
1998)
Amruta Joshi, Stanford Univ.
Research in Algorithms for the InterNet
rivers
In floods, the
banks of a
river overflow
5
Topic Extraction: Overview

Dimensionality Reduction
Represent documents in
Vector Space of terms
 Map to low-dimensionality

Non-linear dim. reduction
 WEBSOM (Lagus et. al. 1999)
 Linear Projection
 LSI (Berry, Dumais, O’Brien
1995)


Regions represent topics
Amruta Joshi, Stanford Univ.
Research in Algorithms for the InterNet
6
Topic Extraction: Overview

Cluster documents on semantic content
 Typically,

each cluster has just 1 topic
Aspect Model
 Topic
modeled as distribution over words
 Documents generated from multiple topics
Amruta Joshi, Stanford Univ.
Research in Algorithms for the InterNet
7
Author Information: Overview

Analyzing text using


Stylometry
 statistical analysis using
literary style, frequency of
word usage, etc
Semantics
 Content of document
Amruta Joshi, Stanford Univ.
Research in Algorithms for the InterNet
As doth the lion in
the Capitol, A man
no mightier than
thyself or me …
8
Author Information: Overview

Graph-based models
D1
D2
 Build
Interactive
ReferralWeb using citations

D3
D4
Kautz, Selman, Shah 1997
 Build
Co-Author Graphs
White & Smith
 Page-Rank for analysis

Amruta Joshi, Stanford Univ.
Research in Algorithms for the InterNet
9
The Big Idea

Topic Model


Author Model


Model topics as distribution over words
Model author as distribution over words
Author-Topic Model
Probabilistic Model for both
 Model topics as distribution over words
 Model authors as distribution over topics

Amruta Joshi, Stanford Univ.
Research in Algorithms for the InterNet
10
Bayesian Networks
Pneumonia
Tuberculosis
nodes = random variables
edges = direct probabilistic
influence
Lung Infiltrates
XRay
Sputum Smear
Topology captures independence:
XRay conditionally independent of Pneumonia given
Infiltrates
Slide Credit: Lisa Getoor, UMD College Park
Amruta Joshi, Stanford Univ.
Research in Algorithms for the InterNet
11
Bayesian Networks
Pneumonia
Tuberculosis
Lung Infiltrates
XRay
Sputum Smear
P T
P(I |P, T )
p
t
0.7
0.3
p
t
0.6
0.4
p
t
0.2
0.8
p
t
0.01 0.99
 Associated
with each node Xi there is a conditional
probability distribution P(Xi|Pai:) — distribution over
Xi for each assignment to parents
If variables are discrete, P is usually multinomial
 P can be linear Gaussian, mixture of Gaussians, …

Slide Credit: Lisa Getoor, UMD College Park
Amruta Joshi, Stanford Univ.
Research in Algorithms for the InterNet
12
BN Learning
P
I
Inducer
Data

T
X
S
BN models can be learned from empirical data
 parameter
estimation via numerical optimization
 structure learning via combinatorial search.
Slide Credit: Lisa Getoor, UMD College Park
Amruta Joshi, Stanford Univ.
Research in Algorithms for the InterNet
13
Generative Model
Probabilistic Generative Process
Mixture
components
Mixture
weights
Amruta Joshi, Stanford Univ.
Statistical Inference
Bayesian approach: use priors
Mixture weights
~ Dirichlet( a )
Mixture components ~ Dirichlet( b )
Research in Algorithms for the InterNet
14
Bayesian Network for modeling
document generation
Doc 1


T1
…
T2
Z

Z

TT
w1
w2
…
wv
W
Amruta Joshi, Stanford Univ.
Research in Algorithms for the InterNet

W
15
Topic Model: Plate Notation
Document specific
distribution over
topics
Document


Topic
Topic distribution
over words

z

w
T
Word
Nd
D
Amruta Joshi, Stanford Univ.
Research in Algorithms for the InterNet
16
Topic Model:
Geometric Representation
Amruta Joshi, Stanford Univ.
Research in Algorithms for the InterNet
17
Modeling Authors with words
Uniform
distribution over
authors of doc
Document
ad
Distribution of
authors over words
Author
x
Word


w
A
Amruta Joshi, Stanford Univ.
Nd
Research in Algorithms for the InterNet
D
18
Author-Topic Model
Uniform
distribution of
documents over
authors
Document
ad
Author
Distribution of
authors over
topics
x
Topic


z
A
Topic
distribution
over words


w
T
Amruta Joshi, Stanford Univ.
Word
Nd
Research in Algorithms for the InterNet
D
19
Inference

Expectation Maximization


But poor results (local Maxima)
Gibbs Sampling
 Parameters: , 
 Start
with initial random assignment
 Update parameter using other parameters
 Converges after ‘n’ iterations
 Burn-in time
Amruta Joshi, Stanford Univ.
Research in Algorithms for the InterNet
20
Inference and Learning for
Documents
Prob. that ith topic is
assigned to topic j
keeping other topic
assn unchanged
# of times
word m is
assigned to
topic j
Amruta Joshi, Stanford Univ.
mj
Research in Algorithms for the InterNet
# of times
topic j has
occurred in
document d
dj
21
Matrix Factorization
Amruta Joshi, Stanford Univ.
Research in Algorithms for the InterNet
22
Topic Model: Inference
River
River
Stream
Stream
Bank
Bank
Money
Money
Loan
Loan
documents
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Can we recover the original topics and topic mixtures from this data?
Slide Credit: Padhraic Smyth, UC Irvine
Amruta Joshi, Stanford Univ.
Research in Algorithms for the InterNet
23
Example of Gibbs Sampling

Assign word tokens randomly to topics
(●=topic 1; ●=topic 2 )
River
River
Stream
Stream
Bank
Bank
Money
Money
Loan
Loan
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Slide Credit: Padhraic Smyth, UC Irvine
Amruta Joshi, Stanford Univ.
Research in Algorithms for the InterNet
24
After 1 iteration

Apply sampling equation to each word
token
River
River
Stream
Stream
Bank
Bank
Money
Money
Loan
Loan
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Slide Credit: Padhraic Smyth, UC Irvine
Amruta Joshi, Stanford Univ.
Research in Algorithms for the InterNet
25
After 4 iterations
River
River
Stream
Bank
Stream
Bank
Money
Money
Loan
Loan
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Slide Credit: Padhraic Smyth, UC Irvine
Amruta Joshi, Stanford Univ.
Research in Algorithms for the InterNet
26
After 32 iterations
●
●
topic 1
stream .40
bank .35
river .25
River
River
Stream
Bank
Stream
Bank
topic 2
bank .39
money .32
loan .29
Money
Money
Loan
Loan
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Slide Credit: Padhraic Smyth, UC Irvine
Amruta Joshi, Stanford Univ.
Research in Algorithms for the InterNet
27
Results

Tested on Scientific Papers
 NIPS



Dataset
V=13,649 D=1,740 K=2,037
#Topics = 100
#tokens = 2,301,375
 CiteSeer



Dataset
V=30,799 D=162,489 K=85,465
#Topics = 300
#tokens = 11,685,514
Amruta Joshi, Stanford Univ.
Research in Algorithms for the InterNet
28
Evaluating Predictive Power

Perplexity
 Indicates
ability to predict words on new
unseen documents
Lower the
better
Amruta Joshi, Stanford Univ.
Research in Algorithms for the InterNet
29
Results: Perplexity
Amruta Joshi, Stanford Univ.
Research in Algorithms for the InterNet
30
Recap

First
Author Model
 Topic Model


Then


Author-Topic Model
Next…

Integrating Topics & Syntax
Amruta Joshi, Stanford Univ.
Research in Algorithms for the InterNet
31
Integrating topics & syntax

Probabilistic Models
 Short-range



Syntactic Constraints
Represented as distinct syntactic classes
HMM, Probabilistic CFGs
 Long-range




dependencies
dependencies
Semantic Constraints
Represented as probabilistic distribution
Bayes Model, Topic Model
New Idea! Use both
Amruta Joshi, Stanford Univ.
Research in Algorithms for the InterNet
32
How to integrate these?

Mixture of Models


Product of Models


Each word exhibits either short or long range
dependencies
Each word exhibits both short or long range
dependencies
Composite Model
Asymmetric
 All words exhibit short-range dependencies
 Subset of words exhibit long-range
Research in Algorithms for the InterNet
Amruta Joshi, Stanforddependencies
Univ.

33
The Composite Model 1

Capturing asymmetry
 Replace
probability distribution over words with
semantic model
 Syntactic model chooses when to emit content
word
 Semantic model chooses which word to emit

Methods
 Syntactic
component is HMM
 Semantic component is Topic model
Amruta Joshi, Stanford Univ.
Research in Algorithms for the InterNet
34
Generating phrases
0.9
in
with
for
on
...
0.5
0.4
0.1
network
neural
output
networks
...
image
images
object
objects
...
kernel
support
svm
vector
...
0.9
0.2
0.7
used
trained
obtained
described
...
network used for images
image obtained with kernel
output described with objects
neural network trained with svm images
Amruta Joshi, Stanford Univ.
Research in Algorithms for the InterNet
35
The Composite Model 2
(Graphical)
Doc’s distribution
over topics


Topics
z1
z2
z3
z4
Words
w1
w2
w3
w4
Classes
c1
Amruta Joshi, Stanford Univ.
c2
c3
c4
Research in Algorithms for the InterNet
36
The Composite Model 3

(d) : document’s distribution over topics
Transitions between classes ci-1 and ci follow
distribution (Ci-1)

A document is generated as:

 For each word wi in document
 Draw zi from (d)
 Draw ci from (Ci-1)
 If ci=1, then draw wi from (zi),
 else draw wi from (ci)
Amruta Joshi, Stanford Univ.
d
Research in Algorithms for the InterNet
37
Results

Tested on
 Brown
corpus (tagged with word types)
 Concatenated Brown & TASA corpus

HMM & Topic Model
 20

T
Classes
start/end Markers Class + 19 classes
= 200
Amruta Joshi, Stanford Univ.
Research in Algorithms for the InterNet
38
Results

Identifying Syntactic classes & semantic topics
 Clean

separation observed
Identifying function words & content words
 “control”

: plain verb (syntax) or semantic word
Part-of-Speech Tagging
 Identifying

syntactic class
Document Classification
 Brown
corpus: 500 docs => 15 groups
 Results similar
to plain Topic Model
Research in Algorithms for the InterNet
Amruta Joshi, Stanford Univ.
39
Extensions to Topic Model
Integrating link information (Cohn,
Hofmann 2001)
 Learning Topic Hierarchies
 Integrating Syntax & Topics
 Integrate authorship info with content
(author-topic model)
 Grade-of-membership Models
 Random sentence generation

Amruta Joshi, Stanford Univ.
Research in Algorithms for the InterNet
40
Conclusion

Identifying its latent structure

Document Content is modeled for
– topic model
 Authorship - author topic model
 Syntactic Constructs – HMM
 Semantic Associations
Amruta Joshi, Stanford Univ.
Research in Algorithms for the InterNet
41
Acknowledgements
 Prof. Rajeev Motwani
 Advice and guidance regarding topic
selection
 T.
K. Satish Kumar

Help on Probabilistic Models
Amruta Joshi, Stanford Univ.
Research in Algorithms for the InterNet
42
Thank you!
Amruta Joshi, Stanford Univ.
Research in Algorithms for the InterNet
43
References

Primary
 Steyvers, M., Smyth, P., Rosen-Zvi, M., & Griffiths, T. (2004). Probabilistic
Author-Topic Models for Information Discovery. The Tenth ACM SIGKDD
International Conference on Knowledge Discovery and Data Mining. Seattle,
Washington.
 Steyvers, M. & Griffiths, T. Probabilistic topic models.
(http://psiexp.ss.uci.edu/research/papers/SteyversGriffithsLSABookFormatted
.pdf)
 Rosen-Zvi, M., Griffiths T., Steyvers, M., & Smyth, P. (2004). The Author-Topic
Model for Authors and Documents. In 20th Conference on Uncertainty in
Artificial Intelligence. Banff, Canada
 Griffiths, T.L., & Steyvers, M., Blei, D.M., & Tenenbaum, J.B. (in press).
Integrating Topics and Syntax. In: Advances in Neural Information Processing
Systems, 17.
 Griffiths, T., & Steyvers, M. (2004). Finding Scientific Topics. Proceedings of
the National Academy of Sciences, 101 (suppl. 1), 5228-5235.
Amruta Joshi, Stanford Univ.
Research in Algorithms for the InterNet
44