machine learning for information extraction: an overview kamal nigam google pittsburgh with input,...

Machine Learning for Information Extraction: An Overview

Kamal NigamGoogle Pittsburgh

With input, slides and suggestions from William Cohen, Andrew McCallum and Ion Muslea

Example: A Problem

Genomics job

Mt. Baker, the school district

Baker Hostetler, the company

Baker, a job opening

Example: A Solution

Job Openings:Category = Food ServicesKeyword = Baker Location = Continental U.S.

Extracting Job Openings from the Web

Title: Ice Cream Guru

Description: If you dream of cold creamy…

Contact: [email protected]

Category: Travel/Hospitality

Function: Food Services

mailto:[email protected]

Potential Enabler of Faceted Search

Lots of Structured Information in Text

IE from Research Papers

What is Information Extraction?

• Recovering structured data from formatted text


• Recovering structured data from formatted text– Identifying fields (e.g. named entity recognition)


• Recovering structured data from formatted text– Identifying fields (e.g. named entity recognition)– Understanding relations between fields (e.g. record

association)



association)– Normalization and deduplication



association)– Normalization and deduplication

• Today, focus mostly on field identification &a little on record association

IE Posed as a Machine Learning Task

• Training data: documents marked up with ground truth

• In contrast to text classification, local features crucial. Features of:– Contents– Text just before item– Text just after item– Begin/end boundaries

00 : pm Place : Wean Hall Rm 5409 Speaker : Sebastian Thrun

prefix contents suffix

… …

Good Features for Information Extraction

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”

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

Creativity and Domain Knowledge Required!

Is Capitalized

Is Mixed Caps

Is All Caps

Initial Cap

Contains Digit

All lowercase

Is Initial

Punctuation

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)

Word Features– lists of job titles, – Lists of prefixes– Lists of suffixes– 350 informative phrases

HTML/Formatting Features– {begin, end, in} x

{, , <a>, <hN>} x{lengths 1, 2, 3, 4, or longer}

– {begin, end} of line

Creativity and Domain Knowledge Required!Good Features for Information Extraction

IE HistoryPre-Web• Mostly news articles

– De Jong’s FRUMP [1982]• Hand-built system to fill Schank-style “scripts” from news wire

– Message Understanding Conference (MUC) DARPA [’87-’95], TIPSTER [’92-’96]

• Most early work dominated by hand-built models– E.g. SRI’s FASTUS, hand-built FSMs.– But by 1990’s, some machine learning: Lehnert, Cardie, Grishman and

then HMMs: Elkan [Leek ’97], BBN [Bikel et al ’98]

Web• AAAI ’94 Spring Symposium on “Software Agents”

– Much discussion of ML applied to Web. Maes, Mitchell, Etzioni.

• Tom Mitchell’s WebKB, ‘96– Build KB’s from the Web.

• Wrapper Induction– Initially hand-build, then ML: [Soderland ’96], [Kushmeric ’97],…

Landscape of ML Techniques for IE:

Any of these models can be used to capture words, formatting or both.

Classify Candidates

Abraham Lincoln was born in Kentucky.

Classifier

which class?

Sliding Window


Classifier

which class?

Try alternatewindow sizes:

Boundary Models


Classifier

which class?

BEGIN END BEGIN END

BEGIN

Finite State Machines


Most likely state sequence?

Wrapper Induction

Abraham Lincoln was born in Kentucky.

Learn and apply pattern for a website





PersonName

Sliding Windows & Boundary Detection

Information Extraction by Sliding Windows

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.

CMU UseNet Seminar Announcement

E.g.Looking forseminarlocation

Information Extraction by Sliding Window







Information Extraction with Sliding Windows[Freitag 97, 98; Soderland 97; Califf 98]

00 : pm Place : Wean Hall Rm 5409 Speaker : Sebastian Thrunw t-m w t-1 w t w t+n w t+n+1 w t+n+m

prefix contents suffix

… …

• Standard supervised learning setting– Positive instances: Candidates with real label– Negative instances: All other candidates– Features based on candidate, prefix and suffix

• Special-purpose rule learning systems work wellcourseNumber(X) :-

tokenLength(X,=,2), every(X, inTitle, false), some(X, A, <previousToken>, inTitle, true),some(X, B, <>. tripleton, true)

Rule-learning approaches to sliding-window classification: Summary

• Representations for classifiers allow restriction of the relationships between tokens, etc

• Representations are carefully chosen subsets of even more powerful representations based on logic programming (ILP and Prolog)

• Use of these “heavyweight” representations is complicated, but seems to pay off in results

IE by Boundary Detection







BWI: Learning to detect boundaries

• Another formulation: learn three probabilistic classifiers:– START(i) = Prob( position i starts a field)– END(j) = Prob( position j ends a field)– LEN(k) = Prob( an extracted field has length k)

• Then score a possible extraction (i,j) bySTART(i) * END(j) * LEN(j-i)

• LEN(k) is estimated from a histogram

[Freitag & Kushmerick, AAAI 2000]


• BWI uses boosting to find “detectors” for START and END

• Each weak detector has a BEFORE and AFTER pattern (on tokens before/after position i).

• Each “pattern” is a sequence of tokens and/or wildcards like: anyAlphabeticToken, anyToken, anyUpperCaseLetter, anyNumber, …

• Weak learner for “patterns” uses greedy search (+ lookahead) to repeatedly extend a pair of empty BEFORE,AFTER patterns


Field F1 Person Name: 30%Location: 61%Start Time: 98%

Problems with Sliding Windows and Boundary Finders

• Decisions in neighboring parts of the input are made independently from each other.

– Naïve Bayes Sliding Window may predict a “seminar end time” before the “seminar start time”.

– It is possible for two overlapping windows to both be above threshold.

– In a Boundary-Finding system, left boundaries are laid down independently from right boundaries, and their pairing happens as a separate step.

Finite State Machines

Hidden Markov Models

St -1

St

Ot

St+1

Ot +1

Ot -1

...

...

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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11 )|()|(),(

o

ttttt soPssPosP

HMMs are the standard sequence modeling tool in genomics, music, 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.

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

Generative Extraction with HMMs

• Parameters: {P(st|st-1), P(ot|st), for all states st, words ot}

• Parameters define generative model:

[McCallum, Nigam, Seymore & Rennie ‘00]

||

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o

ttttt soPssPosP

HMM Example: “Nymble”

Other examples of HMMs in IE: [Leek ’97; Freitag & McCallum ’99; Seymore et al. 99]

Task: Named Entity Extraction

Train on 450k words of news wire text.

Case Language F1 .Mixed English 93%Upper English 91%Mixed Spanish 90%

[Bikel, et al 97]

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

Results:

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.

• If successful, this answers the challenge of integrating the ability to handle many arbitrary features with the full power of finite state automata.

Conditional Markov Models

St -1

St

Ot

St+1

Ot +1

Ot -1

...

...

Generative (traditional HMM)

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o

ttttt soPssPosP

...transitions

observations

St -1

St

Ot

St+1

Ot +1

Ot -1

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Conditional

...transitions

observations

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11 ),|()|(

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tttt ossPosP

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]

Exponential Form for “Next State” Function

kttkk

ttttsttt sof

soZosPossP

t),(exp

),(

1)|(),|(

11 1

Capture dependency on sCapture dependency on st-1 t-1 with |S| with |S|

independent functions, Pindependent functions, Psst-1t-1(s(stt|o|ott).).

Each state contains a “next-state classifier”Each state contains a “next-state classifier”that, given the next observation, produces a that, given the next observation, produces a probability of the next state, Pprobability of the next state, Psst-1t-1(s(stt|o|ott).).

st-1

st

Recipe:- Labeled data is assigned to transitions.- Train each state’s exponential model by maximum entropy

weight feature

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

HMM

MEMM

CRF

St-1 St

Ot

St+1

Ot+1Ot-1

St-1 St

Ot

St+1

Ot+1Ot-1

St-1 St

Ot

St+1

Ot+1Ot-1

...

...

...

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...

...

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),(

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exp1

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1

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ossPosP

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exp1

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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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1)|(

o

t kttkk

o

tossfZ

osP

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|2)—just like HMMs.

[Lafferty, McCallum, Pereira ‘2001]

Training CRFs

),,,(),(

),( )|(),(

:gradient likelihood-Log

}),{|}({

:data ninggiven trai parameters of likelihood-log Maximize

1

2)()(}{

)()(

penalty smoothing parameterscurrent by assigned labels usingcount feature labelscorrect usingcount feature

)(

ttt

kk

ki s

ik

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sstofosC

osCosPosCL

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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]

Sample IE Applications of CRFs

• Noun phrase segmentation [Sha & Pereira, 03]• Named entity recognition [McCallum & Li 03]• Protein names in bio abstracts [Settles 05]• Addresses in web pages [Culotta et al. 05]• Semantic roles in text [Roth & Yih 05]• RNA structural alignment [Sato & Satakibara 05]

Examples of Recent CRF Research

• Semi-Markov CRFs [Sarawagi & Cohen 05]– Awkwardness of token level decisions for segments– Segment sequence model alleviates this– Two-level model with sequences of segments,

which are sequences of tokens

• Stochastic Meta-Descent [Vishwanathan 06]– Stochastic gradient optimization for training– Take gradient step with small batches of examples– Order of magnitude faster than L-BFGS– Same resulting accuracies for extraction

Further Reading about CRFs

Charles Sutton and Andrew McCallum. An Introduction to Conditional Random Fields for Relational Learning. In Introduction to Statistical Relational Learning. Edited by Lise Getoor and Ben Taskar. MIT Press. 2006.

http://www.cs.umass.edu/~mccallum/papers/crf-tutorial.pdf

http://www.cs.umass.edu/%7Emccallum/papers/crf-tutorial.pdf

http://www.cs.umass.edu/%7Emccallum/papers/crf-tutorial.pdf

machine learning for information extraction: an overview kamal nigam google pittsburgh with input,...

Documents

structured data

formatted textwhat

local features crucial

record associationie

record associationwhat

entity recognitionwhat

etcin person suffix

extracting job openings