Learning and Global Inference
for
Information Access and
Natural Language Understanding
Dan Roth
Department of Computer Science
University of Illinois at Urbana-Champaign
May 2007
DIMACS ONR Workshop
Page 1
Nice to Meet You
Page 2
Learning and Inference
 Global decisions in which several local decisions play a role
but there are mutual dependencies on their outcome.
 (Learned) models/classifiers for different sub-problems
 Incorporate classifiers’ information, along with constraints, in
making coherent decisions – decisions that respect the local
models as well as domain & context specific constraints.
 Global inference for the best assignment to all variables of
interest.
Page 3
Inference
Page 4
Comprehension
A process that maintains and
updates a collection of propositions
about the state of affairs.
(ENGLAND, June, 1989) - Christopher Robin is alive and well. He lives in
England. He is the same person that you read about in the book, Winnie the
Pooh. As a boy, Chris lived in a pretty home called Cotchfield Farm. When
Chris was three years old, his father wrote a poem about him. The poem was
printed in a magazine for others to read. Mr. Robin then wrote a book. He
made up a fairy tale land where Chris lived. His friends were animals. There
was a bear called Winnie the Pooh. There was also an owl and a young pig,
called a piglet. All the animals were stuffed toys that Chris owned. Mr. Robin
made them come to life with his words. The places in the story were all near
Cotchfield Farm. Winnie the Pooh was written in 1925. Children still love to
read about Christopher Robin and his animal friends. Most people don't know
he is a real person who is grown now. He has written two books of his own.
They tell what it is like to be famous.
1. Christopher Robin was born in England.
3. Christopher Robin’s dad was a magician.
2. Winnie the Pooh is a title of a book.
4. Christopher Robin must be at least 65 now.
Page 5
What we Know: Stand Alone Ambiguity Resolution
Illinois’ bored of education
board
...Nissan Car and truck plant is …
…divide life into plant and animal kingdom
(This Art) (can N) (will MD) (rust V)
V,N,N
The dog bit the kid. He was taken to a veterinarian
a hospital
Learn a function f: X Y that
maps observations in a domain
to one of several categories or <
Broad Coverage
Page 6
A process that maintains and
updates a collection of propositions
about the state of affairs.
Comprehension
(ENGLAND, June, 1989) - Christopher Robin is alive and well. He lives in
England. He is the same person that you read about in the book, Winnie the
Pooh. As a boy, Chris lived in a pretty home called Cotchfield Farm. When
Chris was three years old, his father wrote a poem about him. The poem was
printed in a magazine for others to read. Mr. Robin then wrote a book. He
made up a fairy tale land where Chris lived. His friends were animals. There
was a bear called Winnie the Pooh. There was also an owl and a young pig,
called a piglet. All the animals were stuffed toys that Chris owned. Mr. Robin
made them come to life with his words. The places in the story were all near
Cotchfield Farm. Winnie the Pooh was written in 1925. Children still love to
read about Christopher Robin and his animal friends. Most people don't know
he is a real person who is grown now. He has written two books of his own.
They tell what it is like to be famous.
1. Christopher Robin was born in England.
3. Christopher Robin’s dad was a magician.
2. Winnie the Pooh is a title of a book.
4. Christopher Robin must be at least 65 now.
This is an Inference Problem
Page 7
This Talk
 Global Inference over Local Models/Classifiers + Expressive Constraints
 Model
 Generality of the framework
 Training Paradigms
 Global vs. Local training
 Semi-Supervised Learning
 Examples
 Semantic Parsing
 Information Extraction
 Pipeline processes
Page 8
Problem Setting

Random
y1
Variables Y:
y4
C(y1,y4)
y5
y2
y6
C(y2,y3,y6,y7,y8)
y3
y7
y8

Conditional Distributions P (learned by models/classifiers)
Constraints C– any Boolean function
defined on partial assignments (possibly: + weights W )

Goal: Find the “best” assignment

The assignment that achieves the highest global accuracy.
This is an Integer Programming Problem


Y*=argmaxY PY (+ WC) subject to constraints C
Other, more general ways to incorporate
Page 9
soft constraints here [ACL’07]
Formal Model
Penalty for violating
the constraint.
Subject to constraints
Weight Vector for
“local” models
A collection of Classifiers;
Log-linear models (HMM,
CRF) or a combination
(Soft) constraints
component
How far away is y from
a “legal” assignment
How to solve?
This is an Integer Linear Program
In many of our applications, large scale problems were solved
efficiently using a commercial ILP package to yield exact solution.
Search techniques are also possible
Page 10
A General Inference Setting

Essentially all complex models studied today can be viewed as optimizing a
linear objective function: HMMs/CRFs [Roth’99; Collins’02;Laffarty et. al 02]

Linear objective functions can be derived from probabilistic perspective:

Markov Random Field 
[standard; Kleinberg&Tardos] Optimization Problem (Metric Labeling) 
[Chekuri et. al’01] Linear Programming Problems 
Inference as Constrained Optimization
[Yih&Roth CoNLL’04]
[Punyakanok et. al COLING’04]…

The probabilistic perspective supports finding the most likely assignment
 Not necessarily what we want

Our Integer linear programming (ILP) formulation




Allows the incorporation of more general cost functions
General (non-sequential) constraint structure
Better exploitation (computationally) of hard constraints
Can find the optimal solution if desired
Page 11
Example 1: Semantic Role Labeling
Who did what to whom, when, where, why,…
I left my pearls to my daughter in my will .
[I]A0 left [my pearls]A1 [to my daughter]A2 [in my will]AM-LOC .




Special Case (structure output problem):
A0
Leaver
here, all the data is available at one time;
in general, classifiers might be learned
A1
Things left
from different sources, at different times,
A2
Benefactor
at different contexts.
AM-LOC
Location
Implications on training paradigms
I left my pearls to my daughter in my will .
Overlapping arguments
If A2 is present, A1
must also be present.
Page 12
Semantic Role Labeling (2/2)

PropBank [Palmer et. al. 05] provides a large human-annotated
corpus of semantic verb-argument relations.



Core arguments: A0-A5 and AA



It adds a layer of generic semantic labels to Penn Tree Bank II.
(Almost) all the labels are on the constituents of the parse trees.
different semantics for each verb
specified in the PropBank Frame files
13 types of adjuncts labeled as AM-arg

where arg specifies the adjunct type
Page 13
Identify Vocabulary
Algorithmic Approach


I left my nice pearls to her
[ [
[
[
[
]
] ]
]
]
Pruning [Xue&Palmer, EMNLP’04]
Argument Identifier

Binary classification (SNoW)
Classify argument candidates

Argument Classifier


candidate arguments
Identify argument candidates


I left my nice pearls to her
Multi-class classification (SNoW)
Inference



Inference over (old and
I left my nice pearls to her
[new)
[
[ Vocabulary
[
[
]I left
] my
] nice pearls] to her
]
Use the estimated probability distribution
given by the argument classifier
Use structural and linguistic constraints
Infer the optimal global output
I left my nice pearls to her
Page 14
Argument Identification & Classification
I left my nice pearls to her
[ [
[
[
[
]
] ]
]
]

Both argument identifier and argument
classifier are trained phrase-based classifiers.

Features (some examples)

voice, phrase type, head word, path, chunk,
chunk pattern, etc. [some make use of a full syntactic parse]
I left my nice pearls to her

Learning Algorithm – SNoW

Sparse network of linear functions


weights learned by regularized Winnow multiplicative update rule
Probability conversion is done via softmax pi = exp{acti}/j exp{actj}
Page 15
I left my nice pearls to her
Inference

The output of the argument classifier often violates some
constraints, especially when the sentence is long.

Finding the best legitimate output is formalized as an
optimization problem and solved via Integer Linear
Programming.
[Punyakanok et. al 04, Roth & Yih 04]

Input:



The probability estimation (by the argument classifier)
Structural and linguistic constraints
Allows incorporating expressive (non-sequential)
constraints on the variables (the arguments types).
Page 16
Integer Linear Programming Inference

For each argument ai


Set up a Boolean variable: ai,t indicating whether ai is classified as t
Goal is to maximize


 i score(ai = t ) ai,t
Subject to the (linear) constraints


Any Boolean constraints can be encoded as linear constraint(s).
If score(ai = t ) = P(ai = t ), the objective is to find the
assignment that maximizes the expected number of
arguments that are correct and satisfies the constraints.
Page 17
Constraints

No duplicate argument classes
a  POTARG x{a = A0}  1

Any Boolean rule can be encoded as
a linear constraint.
R-ARG
If there is an R-ARG phrase, there is an ARG
Phrase
 a2  POTARG , a  POTARG x{a = A0}  x{a2 = R-A0}

C-ARG

a2  POTARG ,
If there is an C-ARG phrase, there is an ARG before it
 (a  POTARG)  (a is before a2 ) x{a = A0}  x{a2 = C-A0}
Many other possible constraints:




Unique labels
No overlapping or embedding
Relations between number of arguments
If verb is of type A, no argument of type B
Universally quantified
rules
Joint inference can be used also to combine different SRL Systems.
Page 18
Semantic Parsing: Summary I

This approach produces a very good semantic parser. F1~<90%
Top ranked system in CoNLL’05 shared task
Key difference is the Inference



Easy and fast: ~7 Sentences/Second
(using Xpress-MP)
A lot of room for improvement (additional constraints)
Demo available http://L2R.cs.uiuc.edu/~cogcomp
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Extracting Relations via Semantic Analysis
Screen shot from a CCG demo
http://L2R.cs.uiuc.edu/~cogcomp

Semantic parsing reveals several
relations in the sentence along with
their arguments.
Page 20
Semantic Parsing: Summary I

This approach produces a very good semantic parser. F1~<90%
Top ranked system in CoNLL’05 shared task
Key difference is the Inference





Easy and fast: ~7 Sentences/Second
(using Xpress-MP)
A lot of room for improvement (additional constraints)
Demo available http://L2R.cs.uiuc.edu/~cogcomp
So far, shown the use of only declarative (deterministic) constraints.
In fact, this approach can be used both with statistical and declarative
constraints.
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ILP as a Unified Algorithmic Scheme

Consider a common model for sequential inference: HMM/CRF

Inference in this model is done via
the Viterbi Algorithm.
s

A
B
C
A
B
C
A
B
C
A
B
C
A
B
C
t
y y1 y2 y3 y4 y5
x x1 x2 x3 x4 x5
Viterbi is a special case of the Linear Programming based Inference.

Viterbi is a shortest path problem, which is a LP, with a canonical matrix that is
totally unimodular. Therefore, you can get integrality constraints for free.
One can now incorporate non-sequential/expressive/declarative constraints by
modifying this canonical matrix
 The extension reduces to a polynomial scheme under some conditions (e.g.,
when constraints are sequential, when the solution space does not change, etc.)
 Not necessarily increases complexity and very efficient in practice
[Roth&Yih, ICML’05]

Learn a rather simple model; make decisions with a more expressive model
Page 22
Integer Linear Programming Inference - Summary

An Inference method for the “best explanation”, used here
to induce a semantic representation of a sentence.


Allows expressive constraints


Any Boolean rule can be represented by a set of linear (in)equalities
Combining acquired (statistical) constraints with declarative
constraints



A general Information Integration framework.
Start with shortest path matrix and constraints
Add new constraints to the basic integer linear program.
Solved using off-the-shelf packages
If the additional constraints don’t change the solution, LP is enough
 Otherwise, the computational time depends on sparsity; fast in practice


Demo available http://L2R.cs.uiuc.edu/~cogcomp
Page 23
Example 2: Pipeline
Raw Data
POS Tagging






 Vocabulary is generated in phases
 Left to Right processing of sentences
is also a pipeline process
Phrases
Semantic Entities
Parsing
WSD
Relations
Semantic Role Labeling
Pipelining is a crude approximation; interactions occur across levels and
down stream decisions often interact with previous decisions.
Leads to propagation of errors
Occasionally, later stage problems are easier but upstream mistakes will not
be corrected.
There are good reasons for pipelining decisions
Global inference over the outcomes of different levels can be used to break
away from this paradigm. [between pipeline & fully global]
Allows a flexible way to incorporate linguistic and structural constraints.
Objective Function can be modified to support pipelines;
Deep Pipelines for Dependency Parsing [Chang, Do, Roth, ACL’06]
Page 24
This Talk
 Global Inference over Local Models/Classifiers + Expressive Constraints
 Model
 Generality of the framework
 Training Paradigms
 Global vs. Local training
 Semi-Supervised Learning
 Examples
 Semantic Parsing
 Information Extraction
 Pipeline processes
Page 25
Training Paradigms that Support Global Inference

Incorporating general constraints (Algorithmic Approach)



Allow both statistical and expressive declarative constraints
Allow non-sequential constraints (generally difficult)
Coupling vs. Decoupling Training and Inference.



Incorporating global constraints is important but
Should it be done only at evaluation time or also at training time?
Issues related to:

modularity, efficiency and performance, availability of training data
May not be relevant in
some problems.
Page 26
Training in the presence of Constraints

General Training Paradigm:



First Term: Learning from data
Second Term: Lead the model by constraints
Can choose if constraints are included in training or only in
evaluation
Page 27
Cartoon: each model
can be more complex
and may have a view
on a set of output
variables.
L+I: Learning plus Inference
Training w/o Constraints
Testing: Inference with Constraints
Learning the
components together!
y1
IBT: Inference-basedf1(x)
Training
y2
y3
y4
f2(x)
y5
x3
f3(x)
x4
x1
x5
Y
f5(x)
x2
x6
f4(x)
Which one is better?
When and Why?
x7
X
Page 28
Perceptron-based Global Learning
True Global Labeling
Local Predictions
Apply
Constraints:
Y
-1
1
-1
-1
1
Y’
-1
1
1
-1
1
1
f1(x)
X
f2(x)
x3
f3(x)
x4
x1
x5
f4(x)
Y
f5(x)
x2
x6
x7
Page 29
Claims

When the local classification problems are “easy”, L+I outperforms IBT.


In many applications, the components are identifiable and easy to learn (e.g.,
argument, open-close, PER).
Only when the local problems become difficult to solve in isolation, IBT
outperforms L+I, but needs a larger number of training examples.

When data is scarce, problems are not easy and constraints can be used,
along with a “weak” model, to label unlabeled data and improve mode.

Will show experimental results and theoretical intuition to support our
claims.
L+I: cheaper computationally; modular
IBT is better in the limit, and other
extreme cases.
Combinations: L+I, and then IBT are possible
Page 30
Bound Prediction
L+I vs. IBT: the more identifiable
individual problems are the better
overall performance is with L+I

Local  ≤ opt + ( ( d log m + log 1/ ) / m )1/2

Global
Indication for
hardness of
problem
 ≤ 0 + ( ( cd log m + c2d + log 1/ ) / m )1/2
Bounds
Simulated Data
opt
=0.1
=0
opt
opt=0.2
Page 31
Relative Merits: SRL
L+I is better.
When the problem
is artificially made
harder, the tradeoff
is clearer.
hard
Difficulty of the learning problem
(# features)
easy
Page 32
Semi-Supervised Learning with Constraints
Experiment is done in the context of Information extraction
Objective function:
Learning w/o Constraints: 300 examples.
Learning w Constraints
Constraints are used to
Bootstrap a semisupervised learner
Poor model + constraints
are used to annotate
unlabeled data, which in
turn is used to keep
training the model.
# of available labeled examples
Page 33
Example 3: Textual Entailment


By “semantically entailed” we
paraphrasing
mean:Phrasal
most verb
people
would agree
that[Connor&Roth’06]
one sentence implies the
other.
Entity matching [Li et. al,
AAAI’04, NAACL’04]
Given:
Q: Who acquired Overture?
Determine:
A: Eyeing the huge market potential, currently
Semantic Role Labeling
led by Google, Yahoo took over search company
Overture Services Inc last year.
Eyeing the huge market
potential, currently led by
Google, Yahoo took over
search company
Overture Services Inc. last
year
Entails
Subsumed by

Yahoo acquired Overture
Overture is a search company
Google is a search company
Google owns Overture
……….
Page 34
Conclusions



Discussed a general paradigm for learning and inference in the context of
natural language understanding tasks
A general Constraint Optimization approach for integration of learned
models with additional (declarative or statistical) expressivity
How to train?

Learn Locally and make use globally (via global inference)


Ability to make use of domain & constraints to drive supervision


[Punyakanok et. al IJCAI’05]
[Klementiev & Roth, ACL’06; Chang, Do, Roth, ACL’07]
How to drive component identification from the global decisoins?
LBJ (Learning Based Java):
A modeling language that supports programming along with
building learned models and allows the incorporation of and
inference with constraints
Page 35