Outline
• Super-quick review of previous talk
• More on NER by token-tagging
– Limitations of HMMs
– MEMMs for sequential classification
• Review of relation extraction techniques
– Decomposition one: NER + segmentation + classifying
segments and entities
– Decomposition two: NER + segmentation + classifying pairs
of entities
• Some case studies
– ACE
– Webmaster
Quick review of previous talk
What is “Information Extraction”
As a family
of techniques:
Information Extraction =
segmentation + classification + association + clustering
October 14, 2002, 4:00 a.m. PT
For years, Microsoft Corporation CEO Bill
Gates railed against the economic philosophy
of open-source software with Orwellian fervor,
denouncing its communal licensing as a
"cancer" that stifled technological innovation.
Today, Microsoft claims to "love" the opensource concept, by which software code is
made public to encourage improvement and
development by outside programmers. Gates
himself says Microsoft will gladly disclose its
crown jewels--the coveted code behind the
Windows operating system--to select
customers.
"We can be open source. We love the concept
of shared source," said Bill Veghte, a
Microsoft VP. "That's a super-important shift
for us in terms of code access.“
Richard Stallman, founder of the Free
Software Foundation, countered saying…
* Microsoft Corporation
CEO
Bill Gates
* Microsoft
Gates
* Microsoft
Bill Veghte
* Microsoft
VP
Richard Stallman
founder
Free Software Foundation
What is “Information Extraction”
As a family
of techniques:
Information Extraction =
segmentation + classification + association + clustering
October 14, 2002, 4:00 a.m. PT
For years, Microsoft Corporation CEO Bill
Gates railed against the economic philosophy
of open-source software with Orwellian fervor,
denouncing its communal licensing as a
"cancer" that stifled technological innovation.
Today, Microsoft claims to "love" the opensource concept, by which software code is
made public to encourage improvement and
development by outside programmers. Gates
himself says Microsoft will gladly disclose its
crown jewels--the coveted code behind the
Windows operating system--to select
customers.
"We can be open source. We love the concept
of shared source," said Bill Veghte, a
Microsoft VP. "That's a super-important shift
for us in terms of code access.“
Richard Stallman, founder of the Free
Software Foundation, countered saying…
via token tagging
* Microsoft Corporation
CEO
Bill Gates
* Microsoft
Gates
* Microsoft
Bill Veghte
* Microsoft
VP
Richard Stallman
founder
Free Software Foundation
Token tagging and NER
NER by tagging tokens
Given a sentence:
Yesterday Pedro Domingos flew to New York.
1) Break the sentence into tokens, and
classify each token with a label
indicating what sort of entity it’s part of:
person name
location name
background
Yesterday Pedro Domingos flew to New York
2) Identify names based on the entity labels
Person name: Pedro Domingos
Location name: New York
3) To learn an NER
system, use YFCL.
HMM for Segmentation of
Addresses
Hall
0.15
Wean
0.03
N-S
0.02
…
…
CA
0.15
NY
0.11
PA
0.08
…
…
• Simplest HMM Architecture: One state per entity type
[Pilfered from Sunita Sarawagi, IIT/Bombay]
HMMs for Information Extraction
…
00 : pm Place : Wean Hall Rm 5409 Speaker : Sebastian Thrun
1.
The HMM consists of two probability tables
•
•
2.
Estimate these tables with a (smoothed) CPT
•
3.
Pr(currentState=s|previousState=t) for s=background, location, speaker,
Pr(currentWord=w|currentState=s) for s=background, location, …
Prob(location|location) = #(loc->loc)/#(loc->*) transitions
Given a new sentence, find the most likely sequence of hidden states
using Viterbi method:
MaxProb(curr=s|position k)=
Maxstate t MaxProb(curr=t|position=k-1) * Prob(word=wk-1|t)*Prob(curr=s|prev=t)
…
“Naïve Bayes” Sliding Window vs HMMs
Domain: CMU UseNet Seminar Announcements
GRAND CHALLENGES FOR MACHINE LEARNING
Jaime Carbonell
School of Computer Science
Carnegie Mellon University
3:30 pm
7500 Wean Hall
Machine learning has evolved from obscurity
in the 1970s into a vibrant and popular
discipline in artificial intelligence during
the 1980s and 1990s.
As a result of its
success and growth, machine learning is
evolving into a collection of related
disciplines: inductive concept acquisition,
analytic learning in problem solving (e.g.
analogy, explanation-based learning),
learning theory (e.g. PAC learning), genetic
algorithms, connectionist learning, hybrid
systems, and so on.
Field
Speaker:
Location:
Start Time:
F1
30%
61%
98%
Field
Speaker:
Location:
Start Time:
F1
77%
79%
98%
Design decisions:
What are the output symbols (states) ?
What are the input symbols ?
Cohen => “Cohen”, “cohen”, “Xxxxx”, “Xx”, … ?
8217 => “8217”, “9999”, “9+”, “number”, … ?
All
Numbers
3-digits
000..
...999
Words
5-digits
00000..
..99999
Others
0..99
0000..9999
Chars
000000..
A..
Delimiters
Multi-letter . , / - + ? #
..z
aa..
Sarawagi et al: choose best abstraction level using holdout set
What is a symbol?
Ideally we would like to use many, arbitrary, overlapping
features of words.
identity of word
ends in “-ski”
is capitalized
is part of a noun phrase
is in a list of city names
is under node X in WordNet
is in bold font
is indented
is in hyperlink anchor
…
S t-1
St
S t+1
…
is “Wisniewski”
part of
noun phrase
…
ends in
“-ski”
O
t -1
Ot
O t +1
Lots of learning systems are not confounded by multiple, nonindependent features: decision trees, neural nets, SVMs, …
What is a symbol?
identity of word
ends in “-ski”
is capitalized
is part of a noun phrase
is in a list of city names
is under node X in WordNet
is in bold font
is indented
is in hyperlink anchor
…
S t-1
St
S t+1
…
is “Wisniewski”
…
part of
noun phrase
ends in
“-ski”
O
t -1
Ot
O t +1
Idea: replace generative model in HMM with a maxent
model, where state depends on observations
Pr( st | xt )  ...
What is a symbol?
identity of word
ends in “-ski”
is capitalized
is part of a noun phrase
is in a list of city names
is under node X in WordNet
is in bold font
is indented
is in hyperlink anchor
…
S t-1
St
S t+1
…
is “Wisniewski”
part of
noun phrase
…
ends in
“-ski”
O
t -1
Ot
O t +1
Idea: replace generative model in HMM with a maxent
model, where state depends on observations and
previous state
Pr( st | xt , st 1, )  ...
What is a symbol?
identity of word
ends in “-ski”
is capitalized
is part of a noun phrase
is in a list of city names
is under node X in WordNet
is in bold font
is indented
is in hyperlink anchor
…
S t-1
St
S t+1
…
is “Wisniewski”
part of
noun phrase
…
ends in
“-ski”
O
t -1
Ot
O t +1
Idea: replace generative model in HMM with a maxent
model, where state depends on observations and
previous state history
Pr( st | xt , st 1, st  2, ...)  ...
Ratnaparkhi’s MXPOST
• Sequential learning problem:
predict POS tags of words.
• Uses MaxEnt model
described above.
• Rich feature set.
• To smooth, discard features
occurring < 10 times.
Conditional Markov Models (CMMs) aka
MEMMs aka Maxent Taggers vs HMMS
St-1
St
St+1
...
Pr( s, o)   Pr( si | si 1 ) Pr(oi | si 1 )
i
Ot-1
Ot
St-1
Ot+1
St
St+1
...
Pr( s | o)   Pr( si | si 1 , oi 1 )
i
Ot-1
Ot
Ot+1
Extracting Relationships
What is “Information Extraction”
As a family
of techniques:
Information Extraction =
segmentation + classification + association + clustering
October 14, 2002, 4:00 a.m. PT
For years, Microsoft Corporation CEO Bill
Gates railed against the economic philosophy
of open-source software with Orwellian fervor,
denouncing its communal licensing as a
"cancer" that stifled technological innovation.
Today, Microsoft claims to "love" the opensource concept, by which software code is
made public to encourage improvement and
development by outside programmers. Gates
himself says Microsoft will gladly disclose its
crown jewels--the coveted code behind the
Windows operating system--to select
customers.
"We can be open source. We love the concept
of shared source," said Bill Veghte, a
Microsoft VP. "That's a super-important shift
for us in terms of code access.“
Richard Stallman, founder of the Free
Software Foundation, countered saying…
* Microsoft Corporation
CEO
Bill Gates
* Microsoft
Gates
* Microsoft
Bill Veghte
* Microsoft
VP
Richard Stallman
founder
Free Software Foundation
What is “Information Extraction”
As a task:
Filling slots in a database from sub-segments of text.
23rd July 2009 05:51 GMT
Microsoft was in violation of the GPL (General Public
License) on the Hyper-V code it released to open source
this week.
After Redmond covered itself in glory by opening up the
code, it now looks like it may have acted simply to head off
any potentially embarrassing legal dispute over violation of
the GPL. The rest was theater.
As revealed by Stephen Hemminger - a principal engineer
with open-source network vendor Vyatta - a network driver
in Microsoft's Hyper-V used open-source components
licensed under the GPL and statically linked to binary parts.
The GPL does not permit the mixing of closed and opensource elements. …
Hemminger said he uncovered the apparent violation and
contacted Linux Driver Project lead Greg Kroah-Hartman, a
Novell programmer, to resolve the problem quietly with
Microsoft. Hemminger apparently hoped to leverage
Novell's interoperability relationship with Microsoft.
NAME
Stephen Hemminger
Greg Kroah-Hartman
Greg Kroah-Hartman
TITLE
ORGANIZATION
principal engineer Vyatta
Novell
programmer
Linux Driver Proj.
lead
What is “Information Extraction”
Technique 1:
NER + Segment + Classify Segments and Entities
23rd July 2009 05:51 GMT
Microsoft was in violation of the GPL (General Public
License) on the Hyper-V code it released to open source
this week.
After Redmond covered itself in glory by opening up the
code, it now looks like it may have acted simply to head off
any potentially embarrassing legal dispute over violation of
the GPL. The rest was theater.
As revealed by Stephen Hemminger - a principal engineer
with open-source network vendor Vyatta - a network driver
in Microsoft's Hyper-V used open-source components
licensed under the GPL and statically linked to binary parts.
The GPL does not permit the mixing of closed and opensource elements. …
Hemminger said he uncovered the apparent violation and
contacted Linux Driver Project lead Greg Kroah-Hartman, a
Novell programmer, to resolve the problem quietly with
Microsoft. Hemminger apparently hoped to leverage
Novell's interoperability relationship with Microsoft.
What is “Information Extraction”
Technique 1:
NER + Segment + Classify Segments and Entities
23rd July 2009 05:51 GMT
Microsoft was in violation of the GPL (General Public
License) on the Hyper-V code it released to open source
this week.
After Redmond covered itself in glory by opening up the
code, it now looks like it may have acted simply to head off
any potentially embarrassing legal dispute over violation of
the GPL. The rest was theater.
As revealed by Stephen Hemminger - a principal engineer
with open-source network vendor Vyatta - a network driver
in Microsoft's Hyper-V used open-source components
licensed under the GPL and statically linked to binary parts.
The GPL does not permit the mixing of closed and opensource elements. …
Hemminger said he uncovered the apparent violation and
contacted Linux Driver Project lead Greg Kroah-Hartman, a
Novell programmer, to resolve the problem quietly with
Microsoft. Hemminger apparently hoped to leverage
Novell's interoperability relationship with Microsoft.
What is “Information Extraction”
Technique 1:
NER + Segment + Classify Segments and Entities
23rd July 2009 05:51 GMT
Microsoft was in violation of the GPL (General Public
License) on the Hyper-V code it released to open source
this week.
Does not contain worksAt fact
After Redmond covered itself in glory by opening up the
code, it now looks like it may have acted simply to head off
any potentially embarrassing legal dispute over violation of
the GPL. The rest was theater.
Does not contain worksAt fact
As revealed by Stephen Hemminger - a principal engineer
with open-source network vendor Vyatta - a network driver
in Microsoft's Hyper-V used open-source components
licensed under the GPL and statically linked to binary parts.
The GPL does not permit the mixing of closed and opensource elements. …
Does contain worksAt fact
Hemminger said he uncovered the apparent violation and
contacted Linux Driver Project lead Greg Kroah-Hartman, a
Novell programmer, to resolve the problem quietly with
Microsoft. Hemminger apparently hoped to leverage
Novell's interoperability relationship with Microsoft.
Does contain worksAt fact
What is “Information Extraction”
Technique 1:
NER + Segment + Classify Segments and Entities
As revealed by Stephen Hemminger - a principal engineer
with open-source network vendor Vyatta - a network driver
in Microsoft's Hyper-V used open-source components
licensed under the GPL and statically linked to binary parts.
The GPL does not permit the mixing of closed and opensource elements. …
Stephen Hemminger
principal engineer
Microsoft
Vyatta
Is in the worksAt fact
Is in the worksAt fact
Is not in the worksAt fact
Is in the worksAt fact
Does contain worksAt fact
NAME
Stephen Hemminger
TITLE
ORGANIZATION
principal engineer
Vyatta
What is “Information Extraction”
Technique 1:
NER + Segment + Classify Segments and Entities
Because of Stephen Hemminger’s discovery, Vyatta was
soon purchased by Microsoft for $1.5 billion…
Microsoft
Vyatta
Stephen Hemminger
$1.5 billion
Does contain an acquired fact
Is in a acquired fact: role=acquirer
Is in the acquired fact: role=acquiree
Is not in the acquired fact
Is in the acquired fact: role=price
What is “Information Extraction”
Technique 1:
NER + Segment + Classify Segments and Entities
23rd July 2009 05:51 GMT
Hemminger said he uncovered the apparent violation and
contacted Linux Driver Project lead Greg Kroah-Hartman, a
Novell programmer, to resolve the problem quietly with
Microsoft. Hemminger apparently hoped to leverage
Novell's interoperability relationship with Microsoft.
Does contain worksAt fact
(actually two of them)
- and that’s a problem
What is “Information Extraction”
Technique 2:
NER + Segment + Classify EntityPairs from same segment
23rd July 2009 05:51 GMT
Hemminger said he uncovered the apparent violation and
contacted Linux Driver Project lead Greg Kroah-Hartman, a
Novell programmer, to resolve the problem quietly with
Microsoft. Hemminger apparently hoped to leverage
Novell's interoperability relationship with Microsoft.
Hemminger
Microsoft
Linux Driver Project
programmer
Novell
lead
Greg Kroah-Hartman
ACE: Automatic Content Extraction
A case study, or:
yet another NIST bake-off
About ACE
•
•
http://www.nist.gov/speech/tests/ace/ and http://projects.ldc.upenn.edu/ace/
The five year mission: “develop technology to extract and characterize meaning
in human language”…in newswire text, speech, and images
– EDT: Develop NER for: people, organizations, geo-political entities (GPE),
location, facility, vehicle, weapon, time, value … plus subtypes (e.g.,
educational organizations)
– RDC: identify relation between entities: located, near, part-whole,
membership, citizenship, …
– EDC: identify events like interaction, movement, transfer, creation,
destruction and their arguments
… and their
arguments
(entities)
Event = sentence plus a trigger word
Events, entities and mentions
• In ACE there is a distinction between an entity—a thing that
exists in the Real World—and an entity mention—which is
something that exists in the text (a substring).
• Likewise, and event is something that (will, might, or did)
happen in the Real World, and an event mention is some text
that refers to that event.
– An event mention lives inside a sentence (the “extent”)
• with a “trigger” (or anchor)
– An event mention is defined by its type and subtype (e.g,
Life:Marry, Transaction:TransferMoney) and its arguments
– Every argument is an entity mention that has been assigned a role.
– Arguments belong to the same event if they are associated with the
same trigger.
• The entity-mention, trigger, extent, argument are markup
and also define a possible decomposition of the eventextraction task into subtask.
The Webmaster Project:
A Case Study
with Einat Minkov (LTI, now Haifa U),
Anthony Tomasic (ISRI)
See IJCAI-2005 paper
Overview and Motivations
•
•
What’s new:
– Adaptive NLP components
– Learn to adapt to changes in
domain of discourse
– Deep analysis in limited but
evolving domain
Compared to past NLP systems:
– Deep analysis in narrow domain
(Chat-80, SHRDLU,...)
...something in between...
– Shallow analysis in broad
domain (POS taggers, NE
recognizers, NP-chunkers, ...)
– Learning used as tool to develop
non-adaptive NLP components
•
Details:
– Assume DB-backed website,
where schema changes over time
• No other changes allowed (yet)
– Interaction:
• User requests (via NL email)
changes in factual content of
website (assume update of one
tuple)
• System analyzes request
• System presents preview page
and editable form version of
request
•
Key points:
– partial correctness is useful
– user can verify correctness (vs
case for DB queries, q/a,...) =>
source of training data
Shallow NLP
email
msg
Classification
LEARNER
POS tags
C
requestType
NP chunks
C
targetRelation
C
targetAttrib
words, ...
NER
features
offline
training
data
Feature Building
entity1, entity2, ....
newEntity1,...
C
oldEntity1,...
keyEntity1,...
otherEntity1,...
preview page
user-editable form
version of request
User
Update Request Construction
confirm?
database
web page
templates
Outline
• Training data/corpus
– look at feasibility of learning the components that need to be
adaptive, using a static corpus
• Analysis steps:
–
–
–
–
–
–
request type
entity recognition
role-based entity classification
target relation finding
target attribute finding
[request building]
• Conclusions/summary
Training data
User1
User2
User3
....
Mike Roborts should be Micheal
Roberts in the staff listing, pls fix it.
Thanks - W
On the staff page, change Mike to
Michael in the listing for “Mike
Roberts”.
Training data
Add this as Greg Johnson’s phone
number: 412 281 2000
User1
Please add “412-281-2000” to greg
johnson’s listing on the staff page.
User2
User3
....
Training data – entity names are made distinct
Add this as Greg Johnson’s phone
number: 412 281 2000
User1
Please add “543-341-8999” to fred
flintstone’s listing on the staff page.
User2
User3
....
Modification: to make entity-extraction
reasonable, remove duplicate entities by replacing
them with alternatives (preserving case, typos, etc)
Training data
message(user 1,req 1)
User1
Request1
message(user 2,req 1)
....
message(user 1,req 2)
User2
Request2
User3
Request3
message(user 2,req 2)
....
message(user 1,req 3)
message(user 2,req 3)
....
....
....
Training data – always test on a novel user?
message(user 1,req 1)
User1
Request1
message(user 2,req 1)
....
message(user 1,req 2)
User2
Request2
User3
Request3
Simulate
of
.... a distribution ....
many users (harder to learn)
test
train
message(user 2,req 2)
....
message(user 1,req 3)
message(user 2,req 3)
....
train
Training data – always test on a novel request?
message(user 1,req 1)
User1
Request1
message(user 2,req 1)
....
message(user 1,req 2)
User2
Request2
User3
Request3
train
test
message(user 2,req 2)
....
message(user 1,req 3)
train
message(user 2,req 3)
Simulate a distribution of
....
many requests (much harder to
learn)
617 emails total + 96 similar ones
Training data – limitations
• One DB schema, one off-line dataset
– May differ from data collected on-line
– So, no claims made for tasks where data will be substantially
different (i.e., entity recognition)
– No claims made about incremental learning/transfer
• All learning problems considered separate
• One step of request-building is trivial for the schema
considered:
– Given entity E and relation R, to which attribute of R does E
correspond?
– So, we assume this mapping is trivial (general case requires
another entity classifier)
Shallow NLP
POS tags
C
requestType
NP chunks
C
targetRelation
C
targetAttrib
words, ...
Information
Extraction
entity1, entity2, ....
features
email
msg
Feature Building
newEntity1,...
C
oldEntity1,...
keyEntity1,...
otherEntity1,...
Entity Extraction Results
• We assume a fixed set of entity types
– no adaptivity needed
(unclear if data can be collected)
• Evaluated:
– hand-coded rules (approx cascaded FST in “Mixup” language)
– learned classifiers with standard feature set and also a “tuned”
feature set, which Einat tweaked
– results are in F1 (harmonic avg of recall and precision)
– two learning methods, both based on “token tagging”
• Conditional Random Fields (CRF)
• Voted-perception discriminative training for an HMM (VP-HMM)
Entity Extraction Results – v2
(CV on users)
Shallow NLP
POS tags
C
requestType
NP chunks
C
targetRelation
C
targetAttrib
words, ...
Information
Extraction
entity1, entity2, ....
features
email
msg
Feature Building
newEntity1,...
C
oldEntity1,...
keyEntity1,...
otherEntity1,...
Entity Classification Results
• Entity “roles”:
– keyEntity: value used to
retrieve a tuple that will
be updated (“delete
greg’s phone number”)
– newEntity: value to be
added to database
(“William’s new office #
is 5307 WH”).
– oldEntity: value to be
overwritten or deleted
(“change mike to
Michael in the listing for
...”)
– irrelevantEntity: not
needed to build the
request (“please add ....
– thanks, William”)
Features:
• closest preceding preposition
• closest preceding “action verb”
(add, change, delete, remove, ...)
• closest preceding word which is a
preposition, action verb, or
determiner (in “determined” NP)
• is entity followed by ‘s
Shallow NLP
POS tags
C
requestType
NP chunks
C
targetRelation
C
targetAttrib
words, ...
Information
Extraction
entity1, entity2, ....
features
email
msg
Feature Building
newEntity1,...
C
oldEntity1,...
keyEntity1,...
otherEntity1,...
Shallow NLP
POS tags
C
requestType
NP chunks
C
targetRelation
C
targetAttrib
words, ...
Information
Extraction
entity1, entity2, ....
features
email
msg
Feature Building
newEntity1,...
C
oldEntity1,...
keyEntity1,...
otherEntity1,...
Reasonable
results with
“bag of words”
features.
Shallow NLP
POS tags
C
requestType
NP chunks
C
targetRelation
C
targetAttrib
words, ...
Information
Extraction
entity1, entity2, ....
features
email
msg
Feature Building
newEntity1,...
C
oldEntity1,...
keyEntity1,...
otherEntity1,...
Request type classification:
addTuple, alterValue, deleteTuple, or deleteValue?
•
Can be determined from entity
roles, except for deleteTuple and
deleteValue.
– “Delete the phone # for Scott”
vs “Delete the row for Scott”
•
Features:
– counts of each entity role
– action verbs
– nouns in NPs which are
(probably) objects of action
verb
– (optionally) same nouns,
tagged with a dictionary
Target attributes are similar
•
Comments:
• Very little data is available
• Twelve words of schema-specific
knowledge: dictionary of terms like
phone, extension, room, office, ...
Shallow NLP
POS tags
C
requestType
NP chunks
C
targetRelation
C
targetAttrib
words, ...
Information
Extraction
entity1, entity2, ....
features
email
msg
Feature Building
newEntity1,...
C
oldEntity1,...
keyEntity1,...
otherEntity1,...
Shallow NLP
POS tags
C
requestType
NP chunks
C
targetRelation
C
targetAttrib
words, ...
Information
Extraction
entity1, entity2, ....
features
email
msg
Feature Building
newEntity1,...
C
oldEntity1,...
keyEntity1,...
otherEntity1,...
Training data
message(user 1,req 1)
User1
Request1
message(user 2,req 1)
....
message(user 1,req 2)
User2
Request2
User3
Request3
message(user 2,req 2)
....
message(user 1,req 3)
message(user 2,req 3)
....
....
....
Training data – always test on a novel user?
message(user 1,req 1)
User1
Request1
message(user 2,req 1)
....
message(user 1,req 2)
User2
Request2
User3
Request3
Simulate
of
.... a distribution ....
many users (harder to learn)
test
train
message(user 2,req 2)
....
message(user 1,req 3)
message(user 2,req 3)
....
train
Training data – always test on a novel request?
message(user 1,req 1)
User1
Request1
message(user 2,req 1)
....
message(user 1,req 2)
User2
Request2
User3
Request3
train
test
message(user 2,req 2)
....
message(user 1,req 3)
train
message(user 2,req 3)
Simulate a distribution of
....
many requests (much harder to
learn)
617 emails total + 96 similar ones
Other issues: a large pool of users and/or
requests
usr
Webmaster: the punchline
Conclusions?
• System architecture allows all schema-dependent knowledge
to be learned
– Potential to adapt to changes in schema
– Data needed for learning can be collected from user
• Learning appears to be possible on reasonable time-scales
– 10s or 100s of relevant examples, not thousands
– Schema-independent linguistic knowledge is useful
• F1 is eighties is possible on almost all subtasks.
– Counter-examples are rarely changed relations (budget) and
distinctions for which little data is available
• There is substantial redundancy in different subtasks
– Opportunity for learning suites of probabilistic classifiers, etc
• Even an imperfect IE system can be useful….
– With the right interface…
Shallow NLP
POS tags
C
requestType
NP chunks
C
targetRelation
C
targetAttrib
words, ...
Information
Extraction
entity1, entity2, ....
features
email
msg
Feature Building
microform
newEntity1,...
C
oldEntity1,...
keyEntity1,...
otherEntity1,...
query
DB
Webmaster: the Epilog (VIO)
Tomasic et
al, IUI 2006
• Faster for request-submitter
• Zero time for webmaster
• Zero latency
• More reliable (!)
• Entity F1 ~= 84,
• Micro-form selection accuracy =~ 80
• Used UI for experiments on real
people (human-human, human-VIO)
Conclusions and comments
• Two case studies of non-trivial IE pipelines illustrate:
– In any pipeline, errors propogate
– What’s the right way of training components in a pipeline?
Independently? How can (and when should) we make decisions
using some flavor of joint inference?
• Some practical questions for pipeline components:
– What’s downstream? What do errors cost?
– Often we can’t see the end of the pipeline…
• How robust is the method ?
– new users, new newswire sources, new upsteam components…
– Do different learning methods/feature sets differ in robustness?
• Some concrete questions for learning relations between entities:
– (When) is classifying pairs of things the right approach? How do
you represent pairs of objects? How to you represent structure, like
dependency parses? Kernels? Special features?