Information Extraction from the World Wide Web Discriminative Finite State Models, Feature Induction & Scoped Learning Andrew McCallum University of Massachusetts,

Information Extractionfrom the World Wide Web

Discriminative Finite State Models, Feature Induction & Scoped Learning

Andrew McCallumUniversity of Massachusetts, Amherst

An HR office

Jobs, but not HR jobs

Jobs, but not HR jobs

Extracting Job Openings from the Web

foodscience.com-Job2

JobTitle: Ice Cream Guru

Employer: foodscience.com

JobCategory: Travel/Hospitality

JobFunction: Food Services

JobLocation: Upper Midwest

Contact Phone: 800-488-2611

DateExtracted: January 8, 2001

Source: www.foodscience.com/jobs_midwest.html

OtherCompanyJobs: foodscience.com-Job1

This took place in ‘99

Not in Maryland

Courses from all over the world

Why prefer “knowledge base search” over “page search”

• Targeted, restricted universe of hits– Don’t show resumes when I’m looking for job openings.

• Specialized queries– Topic-specific

– Multi-dimensional

– Based on information spread on multiple pages.

• Get correct granularity– Site, page, paragraph

• Specialized display– Super-targeted hit summarization in terms of DB slot values

• Ability to support sophisticated data mining

What is “Information Extraction”

Filling slots in a database from sub-segments of text.As a task:

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 open-source 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…

NAME TITLE ORGANIZATION


Filling slots in a database from sub-segments of text.As a task:

October 14, 2002, 4:00 a.m. PT





NAME TITLE ORGANIZATIONBill Gates CEO MicrosoftBill Veghte VP MicrosoftRichard Stallman founder Free Soft..

IE


Information Extraction = segmentation + classification + clustering + association

As a familyof techniques:

October 14, 2002, 4:00 a.m. PT





Microsoft CorporationCEOBill GatesMicrosoftGatesMicrosoftBill VeghteMicrosoftVPRichard StallmanfounderFree Software Foundation


Information Extraction = segmentation + classification + association + clustering


October 14, 2002, 4:00 a.m. PT









October 14, 2002, 4:00 a.m. PT









October 14, 2002, 4:00 a.m. PT





Microsoft CorporationCEOBill GatesMicrosoftGatesMicrosoftBill VeghteMicrosoftVPRichard StallmanfounderFree Software Foundation N

AME

TITLE ORGANIZATION

Bill Gates

CEO

Microsoft

Bill Veghte

VP

Microsoft

Richard Stallman

founder

Free Soft..

*

*

*

*

IE in Context

Create ontology

SegmentClassifyAssociateCluster

Load DB

Spider

Query,Search

Data mine

IE

Documentcollection

Database

Filter by relevance

Label training data

Train extraction models

Hidden Markov Models

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Finite state model Graphical model

Parameters: for all states S={s1,s2,…} Start state probabilities: P(st) Transition probabilities: P(st|st-1) Observation (emission) probabilities: P(ot|st)Training: Maximize probability of training observations (w/ prior)

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HMMs are the standard sequence modeling tool in genomics, speech, NLP, …

...transitions

observations

o1 o2 o3 o4 o5 o6 o7 o8

Generates:

State sequenceObservation sequence

Usually a multinomial over atomic, fixed alphabet

IE with Hidden Markov Models

Yesterday Lawrence Saul spoke this example sentence.


Person name: Lawrence Saul

Given a sequence of observations:

and a trained HMM:

Find the most likely state sequence: (Viterbi)

Any words said to be generated by the designated “person name”state extract as a person name:

),(maxarg osPs

HMM Example: “Nymble”

Other examples of shrinkage for HMMs in IE: [Freitag and McCallum ‘99]

Task: Named Entity Extraction

[Bikel, et al 1998], [BBN “IdentiFinder”]

Person

Org

Other

(Five other name classes)

start-of-sentence

end-of-sentence

Transitionprobabilities

Observationprobabilities

P(st | st-1, ot-1 ) P(ot | st , st-1 )

Back-off to: Back-off to:

P(st | st-1 )

P(st )

P(ot | st , ot-1 )

P(ot | st )

P(ot )

or

Regrets from Atomic View of Tokens

Would like richer representation of text: multiple overlapping features, whole chunks of text.

line, sentence, or paragraph features:– length– is centered in page– percent of non-alphabetics– white-space aligns with next line– containing sentence has two verbs– grammatically contains a question– contains links to “authoritative” pages– emissions that are uncountable– features at multiple levels of granularity

Example word features:– identity of word– is in all caps– ends in “-ski”– is part of a noun phrase– is in a list of city names– is under node X in WordNet or Cyc– is in bold font– is in hyperlink anchor– features of past & future– last person name was female– next two words are “and Associates”

Problems with Richer Representationand a Generative Model

• These arbitrary features are not independent:– Overlapping and long-distance dependences

– Multiple levels of granularity (words, characters)

– Multiple modalities (words, formatting, layout)

– Observations from past and future

• HMMs are generative models of the text:

• Generative models do not easily handle these non-independent features. Two choices:– Model the dependencies. Each state would have its own

Bayes Net. But we are already starved for training data!

– Ignore the dependencies. This causes “over-counting” of evidence (ala naïve Bayes). Big problem when combining evidence, as in Viterbi!

),( osP

Conditional Sequence Models

• We would prefer a conditional model:P(s|o) instead of P(s,o):– Can examine features, but not responsible for generating

them.

– Don’t have to explicitly model their dependencies.

– Don’t “waste modeling effort” trying to generate what we are given at test time anyway.

• This answers the challenge of integrating the ability to handle many arbitrary features with the full power of finite state automata.

Locally Normalized Conditional Sequence Model

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Generative (traditional HMM)

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...transitions

observations

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Conditional

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observations

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Standard belief propagation: forward-backward procedure.Viterbi and Baum-Welch follow naturally.

Maximum Entropy Markov Models [McCallum, Freitag & Pereira, 2000]MaxEnt POS Tagger [Ratnaparkhi, 1996]

SNoW-based Markov Model [Punyakanok & Roth, 2000]

Locally Normalized Conditional Sequence Model

St -1

St

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St+1

Ot +1

Ot -1

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Generative (traditional HMM)

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...transitions

observations

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Conditional

...transitions

entire observation sequence

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Standard belief propagation: forward-backward procedure.Viterbi and Baum-Welch follow naturally.

Maximum Entropy Markov Models [McCallum, Freitag & Pereira, 2000]MaxEnt POS Tagger [Ratnaparkhi, 1996]

SNoW-based Markov Model [Punyakanok & Roth, 2000]

Or, more generally:

...

Exponential Form for “Next State” Function

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Overall Recipe:- Labeled data is assigned to transitions.- Train each state’s exponential model by maximum likelihood (iterative scaling or conjugate gradient).

weight feature

)|(1 tts osP

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Black-box classifier

st-1

Feature Functions

:),,,( Example 1ttk sstof

otherwise 0

s s )d(Capitalize if 1),,,( j1i

1,d,Capitalizettt

ttss

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ji


s3

s1 s2

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o = o1 o2 o3 o4 o5 o6 o7

Exponential Form for “Next State” Function

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1)|(

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Overall Recipe:- Labeled data is assigned to transitions.- Train each state’s exponential model by maximum likelihood (iterative scaling or conjugate gradient).

weight feature

)|(1 tts osP

t

Black-box classifier

st-1

Experimental Data

38 files belonging to 7 UseNet FAQs

Example:

<head> X-NNTP-Poster: NewsHound v1.33<head> Archive-name: acorn/faq/part2<head> Frequency: monthly<head><question> 2.6) What configuration of serial cable should I use?<answer><answer> Here follows a diagram of the necessary connection<answer> programs to work properly. They are as far as I know <answer> agreed upon by commercial comms software developers fo<answer><answer> Pins 1, 4, and 8 must be connected together inside<answer> is to avoid the well known serial port chip bugs. The

Procedure: For each FAQ, train on one file, test on other; average.

Features in Experiments

begins-with-number

begins-with-ordinal

begins-with-punctuation

begins-with-question-word

begins-with-subject

blank

contains-alphanum

contains-bracketed-number

contains-http

contains-non-space

contains-number

contains-pipe

contains-question-mark

contains-question-word

ends-with-question-mark

first-alpha-is-capitalized

indented

indented-1-to-4

indented-5-to-10

more-than-one-third-space

only-punctuation

prev-is-blank

prev-begins-with-ordinal

shorter-than-30

Models Tested

• ME-Stateless: A single maximum entropy classifier applied to each line independently.

• TokenHMM: A fully-connected HMM with four states, one for each of the line categories, each of which generates individual tokens (groups of alphanumeric characters and individual punctuation characters).

• FeatureHMM: Identical to TokenHMM, only the lines in a document are first converted to sequences of features.

• MEMM: The Maximum Entropy Markov Model described in this talk.

Results

Learner Segmentationprecision

Segmentationrecall

ME-Stateless 0.038 0.362

TokenHMM 0.276 0.140

FeatureHMM 0.413 0.529

MEMM 0.867 0.681

nn oooossss ,...,,..., 2121

HMM

MEMM

CRF

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(A special case of MEMMs and CRFs.)

Conditional Random Fields (CRFs)[Lafferty, McCallum, Pereira ‘2001]

From HMMs to MEMMs to CRFs

Conditional Random Fields (CRFs)

St St+1 St+2

O = Ot, Ot+1, Ot+2, Ot+3, Ot+4

St+3 St+4

Markov on s, conditional dependency on o.

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Hammersley-Clifford-Besag theorem stipulates that the CRFhas this form—an exponential function of the cliques in the graph.

Assuming that the dependency structure of the states is tree-shaped (linear chain is a trivial tree), inference can be done by dynamic programming in time O(|o| |S|)—just like HMMs.

[Lafferty, McCallum, Pereira ‘2001]

General CRFs vs. HMMs

• More general and expressive modeling technique

• Comparable computational efficiency

• Features may be arbitrary functions of any or all observations

• Parameters need not fully specify generation of observations; require less training data

• Easy to incorporate domain knowledge

• State means only “state of process”, vs“state of process” and “observational history I’m keeping”

Training CRFs

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:gradient likelihood-Log

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penalty smoothing parameterscurrent by assigned labels usingcount feature labelscorrect usingcount feature

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Methods:• iterative scaling (quite slow)• conjugate gradient (much faster)• conjugate gradient with preconditioning (super fast)• limited-memory quasi-Newton methods (also super fast)

Complexity comparable to standard Baum-Welch

[Sha & Pereira 2002]& [Malouf 2002]

Voted Perceptron Sequence Models

before as ),,,(),( where

),(),( :k

),,,(expmaxarg

i instances, trainingallfor

:econvergenc toIterate

0k :zero toparameters Initialize

},{ :data ningGiven trai

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tossfosC

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[Collins 2002]

Like CRFs with stochastic gradient ascent and a Viterbi approximation.

Avoids calculating the partition function (normalizer), Zo, but gradient ascent, not 2nd-order or conjugate gradient method.

Analogous tothe gradientfor this onetraining instance

MEMM & CRF Related Work• Maximum entropy for language tasks:

– Language modeling [Rosenfeld ‘94, Chen & Rosenfeld ‘99]– Part-of-speech tagging [Ratnaparkhi ‘98]– Segmentation [Beeferman, Berger & Lafferty ‘99]– Named entity recognition “MENE” [Borthwick, Grishman,…’98]

• HMMs for similar language tasks– Part of speech tagging [Kupiec ‘92]– Named entity recognition [Bikel et al ‘99]– Other Information Extraction [Leek ‘97], [Freitag & McCallum ‘99]

• Serial Generative/Discriminative Approaches– Speech recognition [Schwartz & Austin ‘93]– Reranking Parses [Collins, ‘00]

• Other conditional Markov models– Non-probabilistic local decision models [Brill ‘95], [Roth ‘98]– Gradient-descent on state path [LeCun et al ‘98]– Markov Processes on Curves (MPCs) [Saul & Rahim ‘99]– Voted Perceptron-trained FSMs [Collins ’02]

Part-of-speech Tagging

The asbestos fiber , crocidolite, is unusually resilient once

it enters the lungs , with even brief exposures to it causing

symptoms that show up decades later , researchers said .

DT NN NN , NN , VBZ RB JJ IN

PRP VBZ DT NNS , IN RB JJ NNS TO PRP VBG

NNS WDT VBP RP NNS JJ , NNS VBD .

45 tags, 1M words training data, Penn Treebank

Error oov error error err oov error err

HMM 5.69% 45.99%

CRF 5.55% 48.05% 4.27% -24% 23.76% -50%

Using spelling features*

* use words, plus overlapping features: capitalized, begins with #, contains hyphen, ends in -ing, -ogy, -ed, -s, -ly, -ion, -tion, -ity, -ies.

[Pereira 2001]

Chinese Word Segmentation[McCallum & Feng 2003]

~100k words data, Penn Chinese Treebank

Lexicon features:

Adjective ending character digit characters provincesadverb ending character foreign name chars punctuation charsbuilding words function words Roman alphabeticsChinese number characters job title Roman digitsChinese period locations stopwordscities and regions money surnamescountries negative characters symbol charactersdates organization indicator verb charsdepartment characters preposition characters wordlist (188k lexicon)

Chinese Word Segmentation Results[McCallum & Feng 2003]

Precision and recall of segments with perfect boundaries:

# training testing segmentationMethod sentences prec. recall F1

[Peng] ~5M 75.1 74.0 74.2[Ponte] ? 84.4 87.8 86.0[Teahan] ~40k ? ? 94.4[Xue] ~10k 95.2 95.1 95.2CRF 2805 97.3 97.8 97.5CRF 140 95.4 96.0 95.7CRF 56 93.9 95.0 94.4CRF+ 2805 99.6 99.7 99.6

Prev. world’s best error 50%

Person name Extraction[McCallum 2001]

Features in Experiment

Capitalized Xxxxx

Mixed Caps XxXxxx

All Caps XXXXX

Initial Cap X….

Contains Digit xxx5

All lowercase xxxx

Initial X

Punctuation .,:;!(), etc

Period .

Comma ,

Apostrophe ‘

Dash -

Preceded by HTML tag

Character n-gram classifier says string is a person name (80% accurate)

In stopword list(the, of, their, etc)

In honorific list(Mr, Mrs, Dr, Sen, etc)

In person suffix list(Jr, Sr, PhD, etc)

In name particle list (de, la, van, der, etc)

In Census lastname list;segmented by P(name)

In Census firstname list;segmented by P(name)

In locations lists(states, cities, countries)

In company name list(“J. C. Penny”)

In list of company suffixes(Inc, & Associates, Foundation)

Hand-built FSM person-name extractor says yes, (prec/recall ~ 30/95)

Conjunctions of all previous feature pairs, evaluated at the current time step.

Conjunctions of all previous feature pairs, evaluated at current step and one step ahead.

All previous features, evaluated two steps ahead.

All previous features, evaluated one step behind.

Total number of features = ~200k

Training and Testing

• Trained on 65469 words from 85 pages, 30 different companies’ web sites.

• Training takes 4 hours on a 1 GHz Pentium.• Training precision/recall is 96/96.

• Tested on different set of web pages with similar size characteristics.

• Testing precision is 0.92 - 0.95, recall is 0.89 - 0.91.

Feature Induction

WORD=“interest” & WORD@+1=“rates”

WORD=“put” & WORD@+1=“and” & WORD@+2=“call” (options)

WORD=“to” & POS@+1=VB

# features without conjunctions = ~90k# features with conjunctions = ~1 million

Feature Gain

)||~()||~(),(Gain fq qpDqpDf

Currentloss

Loss if feature fwere added andhad optimal weight,

Feature Induction Procedure

• Partially train model with current features• Find tokens significantly contributing to D(p||q).• “Cluster” these errors by different cells of the

confusion matrix.• For each cell

– Generate ~50k candidate new features– Measure the Gain of each– Add to the model the top N features

~

Feature Induction Discussion

• Decision trees partition the training data.• Boosting is like feature induction

– (Generate a new conjunction or tree & give it a weight)– but it typically adds just one feature for each round– and it doesn’t change the weight once set.

• [Della Piettra2 & Lafferty 97]– also add one feature at a time, and would be quite

inefficient for CRFs.

• Here we carefully select several approximations to their scheme.

Feature Induction Results (Preliminary)

Noun Phrase Segmentation

Method # features F1

[Sha & Pereira 2003] 3.8M 94.4Feature Induction 140K 94.4

Named Entity Extraction (in Spanish)

Method F1

With no conjunctions ~30With fixed conjunctions 51With induced conjunctions 65

(extra preliminary)

Person name Extraction

Local features, like formatting, exhibit regularity on a particular subset of the data (e.g. web site or document). Note that future data will probably not have the same regularities as the training data.

Global features, like word content, exhibit regularity over an entire data set. Traditional classifiers are generally trained on these kinds of features.

Local and Global Features

w

f

Scoped Learning Generative Model

1. For each of the D documents:a) Generate the multinomial formatting

feature parameters from p(|)

2. For each of the N words in the document:

a) Generate the nth category cn from

p(cn).

b) Generate the nth word (global feature)

from p(wn|cn,)

c) Generate the nth formatting feature

(local feature) from p(fn|cn,)

w f

c

N

D

Inference

Given a new web page, we would like to classify each wordresulting in c = {c1, c2,…, cn}

This is not feasible to compute because of the integral andsum in the denominator. We experimented with twoapproximations: - MAP point estimate of - Variational inference

MAP Point Estimate

If we approximate with a point estimate, , then the integral disappears and c decouples. We can then label each word with:

E-step:

M-step:

A natural point estimate is the posterior mode: a maximum likelihood estimate for the local parameters given the document in question:

^

Global Extractor: Correctly extracts 3 out of 9

Scoped Learning Extractor: Correctly extracts 8 out of 9

Coverage

Acc

ura

cy

Global Extractor: Precision = 46%, Recall = 75%

Scoped Learning Extractor: Precision = 58%, Recall = 75% Error = -22%

Discriminative Model

w f

c

N

D

When there are many non-independent formatting features, may prefer a non-generative model.

Use a point estimate of .

E-step computes p(c|w, f, ).

M-step sets to maximize conditional likelihood, p(c|f)—uses MaxEnt.

More Experimental Results

• 42,548 web pages from 330 web sites. Each page labeled as PRESS_RELEASE, OTHER.

• Locale: web site• Global features: words on page

– E.g. “immediate” “release” “products”

• Local features: Character 4-gram window slid across URL.– E.g. “prel”, “/pr/”, “pres”…

Global Extractor: Precision = 54%, Recall = 90%

Scoped Learning Extractor: Precision = 77%, Recall = 90% Error = -50%

Scoped Learning Related Work

• Co-training [Blum & Mitchell 1998]– Although it has no notion of scope, it also has an independence

assumption about two independent views of data.

• PRMs for classification [Taskar, Segal & Koller 2001]– Extends notion of multiple views to multiple kinds of relationships– This model can be cast as a PRM where each locale is a separate group

of nodes with separate parameters. Their inference corresponds our MAP estimate inference.

• Classification with Labeled & Unlabeled Data [Nigam et al, 1999, Joachims 1999, etc.]– Particularly transduction, in which the unlabeled set is the test set. – However, we model locales, represent a difference between local and

global features, and can use locales at training time to learn hyper-parameters over local features.

• Classification with Hyperlink Structure [Slattery 2001]– Adjusts a web page classifier using ILP and a hubs & authorities

algorithm.

Future Directions• Further work in feature selection and induction: automatically

choose the fk functions (efficiently).

• Factorial CRFs

• Tree-structured Markov random fields for hierarchical parsing.

• Induction of finite state structure.

• Combine CRFs and Scoped Learning.

• Data mine the results of information extraction, and integrate the data mining with extraction.

• Create a text extraction and mining system that can be assembled and trained to a new vertical application by non-technical users.

Papers on MEMMs, CRFs, Scoped Learning and more available at http://www.cs.umass.edu/~mccallum

Documents

Information Extraction from the World Wide Web Discriminative Finite State Models, Feature Induction & Scoped Learning Andrew McCallum University of Massachusetts,