Administrivia. What is Information Extraction. Finite State Models. Graphical Models. Hidden Markov Models (HMMs) for Information Extraction

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1 Administrivia Hidden Markov Models (HMMs) for Information Extraction Group meetings next week Feel free to rev proposals thru weekend Daniel S. Weld CSE 454 What is Information Extraction Landscape of IE Techniques: Models As a task: Filling slots in a database from sub-segments of text. Lexicons Classify Pre-segmented Candidates Sliding Window Abraham Lincoln was born in entucky. Abraham Lincoln was born in entucky. Abraham Lincoln was born in entucky. October 4, 00, 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. member? Alabama Alaska Wisconsin Wyoming Classifier which class? Classifier which class? Try alternate window sizes: 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. IE NAME TITLE ORGANIZATION Bill Gates CEO Microsoft Bill Veghte VP Microsoft Richard Stallman founder Free Soft.. Boundary Models Abraham Lincoln was born in entucky. BEGIN Classifier which class? BEGIN END BEGIN END Finite State Machines Abraham Lincoln was born in entucky. Most likely state sequence? Context Free Grammars Abraham Lincoln was born in entucky. NNP NNP V V P NP NP S VP PP VP Most likely parse? Richard Stallman, founder of the Free Software Foundation, countered saying Slides from Cohen & McCallum Each model can capture words, formatting, or both 4/6/009 :39 PM Slides from Cohen & McCallum4 Naïve Bayes Finite State Models Sequence HMMs General Graphs Generative directed models Conditional Conditional Conditional Graphical Models Family of probability Node is independent of its nondescendants a given its parents distributions that factorize in certain way Directed (Bayes Nets) x0 x4 Node is independent all other nodes given its neighbors Undirected (Markov Random Field) x x x3 x0 x4 Logistic Regression Linear-chain CRFs General CRFs Factor Graphs x x x3 x5 Sequence General Graphs x x0 x4 x3 x5 x

2 Warning Graphical models add another set of arcs between nodes where the arcs mean something completely different confusing. Skip for 454 New sldies are too abstract Recap: Naïve Bayes Classifier Hidden state Random s (Boolean) Observable Spam? y x x x 3 Nigeria? Widow? CSE 454? Causal dependency (probabilistic) P(x i y=spam) P(x i y spam) Recap: Naïve Bayes Assumption: features independent given label Generative Classifier Model joint distribution p(x,y) Inference Learning: counting Can we use for IE directly? The article appeared in the Seattle Times. city? capitalization length suffix... Labels of neighboring words dependent! Need to consider sequence! other person Hidden Markov Models Finite state model location state sequence Generative Sequence Model assumptions make joint distribution tractable observation x x x 3 x 4 x 5 x 6 x 7 x 8 person sequence Yesterday Pedro. Each state depends only on its immediate predecessor.. Each observation depends only on current state. other person y y y 3 y 4 y 5 y 6 y 7 y 8 Graphical Model transitions observations other person Hidden Markov Models Finite state model location state sequence other person y y y 3 y 4 y 5 y 6 y 7 y 8 observation x x x 3 x 4 x 5 x 6 x 7 x 8 person sequence Yesterday Pedro Generative Sequence Model Model Parameters Start state probabilities Transition probabilities Observation probabilities Graphical Model transitions observations HMM Formally Set of states: {y i } Set of possible observations {x i } Probability of initial state Transition Probabilities Emission Probabilities

3 Example: Dishonest Casino Casino has two dice: Fair die P() = P() = P(3) = P(5) = P(6) = /6 Loaded die P() = P() = P(3) = P(5) = /0 P(6) = / Casino player switches dice Approx once every 0 turns Game:. You bet $. You roll (always with a fair die) 3. Casino player rolls (maybe with fair die, maybe with loaded die) 4. Highest number wins $ Slides from Serafim Batzoglou The dishonest casino model 0.95 P( F) = /6 P( F) = /6 P(3 F) = /6 P(4 F) = /6 P(5 F) = /6 P(6 F) = /6 FAIR LOADED 0.95 P( L) = /0 P( L) = /0 P(3 L) = /0 P(4 L) = /0 P(5 L) = /0 P(6 L) = / Slides from Serafim Batzoglou IE with Hidden Markov Models Given a sequence of observations: Yesterday Pedro Domingos spoke this example sentence. and a trained HMM: Find the most likely state sequence: (Viterbi) person name location name background arg max Yesterday Pedro Domingos spoke this example sentence. v s v v P( s, o) IE with Hidden Markov Models For sparse extraction tasks : Separate HMM for each type of target Each HMM should Model entire document Consist of target and non-target states Not necessarily fully connected Any words said to be generated by the designated person name state extract as a person name: Slide by Cohen & McCallum Person name: Pedro Domingos Slide by Okan Basegmez 6 Or Combined HMM Example Research Paper Headers HMM Example: Nymble Task: Named Entity Extraction [Bikel, et al 998], [BBN IdentiFinder ] Person Org start-ofsentence end-ofsentence Transition probabilities Observation probabilities or (Five other name classes) Back-off to: Back-off to: Other Train on ~500k words of news wire text. Results: Case Language F. Mixed English 93% Upper English 9% Mixed Spanish 90% Slide by Okan Basegmez 7 Slide by Cohen & McCallum Other examples of shrinkage for HMMs in IE: [Freitag and McCallum 99]

4 HMM Example: Nymble Task: Named Entity Extraction Person Org (Five other name classes) Other [Bikel, et al 998], [BBN IdentiFinder ] Train on ~500k words of news wire text. Finite State Model start-ofsentence end-ofsentence start-ofsentence Person end-ofsentence Org (Five other name classes) Other y vs. Path y y 3 y 4 y 5 y 6 Results: Slide adapted from Cohen & McCallum Case Language F. Mixed English 93% Upper English 9% Mixed Spanish 90% x x x 3 x 4 x 5 x 6 GIVEN Question # Evaluation A sequence of observations x x x 3 x 4 x N A trained HMM θ=(,, ) QUESTION How likely is this sequence, given our HMM? P(x,θ) Why do we care? Need it for learning to choose among competing models! GIVEN Question # - Decoding A sequence of observations x x x 3 x 4 x N A trained HMM θ=(,, ) QUESTION How dow we choose the corresponding parse (state sequence) y y y 3 y 4 y N, which best explains x x x 3 x 4 x N? There are several reasonable optimality criteria: single optimal sequence, average statistics for individual states, A parse of a sequence Given a sequence x = x x N, A parse of o is a sequence of states y = y,, y N GIVEN Question #3 - Learning A sequence of observations x x x 3 x 4 x N person QUESTION other How do we learn the model parameters location θ =(,, ) which maximize P(x, θ )? Slide by Serafim Batzoglou x x x 3 x

5 Three Questions Evaluation Forward algorithm (Could also go other direction) Decoding Viterbialgorithm Learning Baum-Welch Algorithm (aka forward-backward ) A kind of EM (expectation maximization) A Solution to #: Evaluation Given observations x=x x N and HMM θ, what is p(x)? Enumerate every possible state sequence y=y y N Probability of x and given particular y Probability of particular y T multiplications per sequence Summing over all possible state sequences we get For small HMMs T=0, N=0 there are 0 N T state sequences! billion sequences! Solution to #: Evaluation Forward Variable α t (i) Use Dynamic Programming: Define forward Prob - that the state at time t vas value S i and - the partial obs sequence x=x x t has been seen probability that at time t -the state is S i - the partial observation sequence x=x x t has been emitted person other location x x x 3 x t Forward Variable α t (i) Solution to #: Evaluation Use Dynamic Programming prob - that the state at t vas value S i and - the partial obs sequence x=x x t has been seen person other S i Cache and reuse inner sums Define forward s location x x x 3 x t probability that at time t - the state is y t =S i - the partial observation sequence x=x x t has been omitted

6 The Forward Algorithm The Forward Algorithm INITIALIZATION INDUCTION TERMINATION Time: O( N) Space: O(N) = S N #states length of sequence The Backward Algorithm The Backward Algorithm INITIALIZATION INDUCTION TERMINATION Time: O( N) Space: O(N) Three Questions Evaluation Forward algorithm (Could also go other direction) Decoding Viterbialgorithm Learning Baum-Welch Algorithm (aka forward-backward ) A kind of EM (expectation maximization) Solution to # - Decoding Given x=x x N and HMM θ, what is best parse y y N? Several optimal solutions. States which are individually most likely: most likely state y * t at time t is then But some transitions may have 0 probability!

7 Need new slide! Looks like this but deltas Solution to # - Decoding Given x=x x N and HMM θ, what is best parse y y N? Several optimal solutions. States which are individually most likely. Single best state sequence We want to find sequence y y N, such that P(x,y) is maximized y * = argmax y P( x, y ) o o o 3 o Again, we can use dynamic programming! The Viterbi Algorithm The Viterbi Algorithm DEFINE INITIALIZATION INDUCTION State i x x x j- x j..x T Max δ j (i) TERMINATION Backtracking to get state sequence y* Time: O( T) Space: O(T) Linear in length of sequence Remember: δ k (i) = probability of most likely state seq ending with state S k Slides from Serafim Batzoglou The Viterbi Algorithm Three Questions Evaluation Forward algorithm (Could also go other direction) Decoding Viterbialgorithm Learning Baum-Welch Algorithm (aka forward-backward ) A kind of EM (expectation maximization) Pedro Domingos 4

8 Solution to #3 - Learning Given x x N, how do we learn θ =(,, ) to maximize P(x)? Unfortunately, there is no known way to analytically find a global maximum θ * such that θ * = arg max P(o θ) But it is possible to find a local maximum; given an initial model θ, we can always find a model θ such that P(o θ ) P(o θ) Chicken & Egg Problem If we knew the actual sequence of states It would be easy to learn transition and emission probabilities But we can t observe states, so we don t! If we knew transition & emission probabilities Then it d be easy to estimate the sequence of states (Viterbi) But we don t know them! 44 Simplest Version Mixture of two distributions Input Looks Like now: form of distribution & variance, % =5 Just need mean of each distribution We Want to Predict Chicken & Egg Note that coloring instances would be easy if we knew Gausians.?

9 Chicken & Egg And finding the Gausians would be easy If we knew the coloring Pretend we do know the parameters Initialize randomly: set θ =?; θ =? Pretend we do know the parameters Initialize randomly Pretend we do know the parameters Initialize randomly Pretend we do know the parameters Initialize randomly [M step] Treating each instance as fractionally having both values compute the new parameter values ML Mean of Single Gaussian U ml = argmin u Σ i (x i u)

10 [M step] Treating each instance as fractionally having both values compute the new parameter values [M step] Treating each instance as fractionally having both values compute the new parameter values [M step] Treating each instance as fractionally having both values compute the new parameter values EM for HMMs Compute the forward and backward probabilities for given model parameters and our observations [M step] Treating each instance as fractionally having both values compute the new parameter values - Re-estimate the model parameters - Simple Counting Summary - Learning Use hill-climbing Called the forward-backward (or Baum/Welch) algorithm Idea Use an initial parameter instantiation Loop Compute the forward and backward probabilities for given model parameters and our observations Re-estimate the parameters Until estimates don t change much 59

11 The Problem with HMMs Following slides are unclear We want more than an Atomic View of Words We want many arbitrary, overlapping features of words identity of word ends in -ski is capitalized is part of a noun phrase is Wisniewski is in a list of city names is under node X in WordNet part of is in bold font noun phrase is indented is in hyperlink anchor last person name was female next two words are and Associates y t- ends in -ski x t - y t x t y t+ x t+ Slide by Cohen & McCallum Naïve Bayes Logistic Regression Finite State Models HMMs Sequence Linear-chain CRFs General Graphs Generative directed models Conditional Conditional Conditional? General CRFs Problems with Richer Representation and a Joint Model These arbitrary features are not independent. Multiple levels of granularity (chars, words, phrases) Multiple dependent modalities (words, formatting, layout) Past & future Two choices: Model the dependencies. Each state would have its own Bayes Net. But we are already starved for training data! S t- S t S t+ Ignore the dependencies. This causes over-counting of evidence (ala naïve Bayes). Big problem when combining evidence, as in Viterbi! S t- S t S t+ Sequence General Graphs O Slide by Cohen & tmccallum - O t O t + O t - O t O t+ Discriminative and Generative Models So far: all models generative Generative Models model P(x,y) Discriminative Models model P(x y) Discriminative Models often better Eventually, what we care about is p(y x)! Bayes Net describes a family of joint distributions of, whose conditionals take certain form But there are many other joint models, whose conditionals also have that form. We want to make independence assumptions among y, but not among x. P(x y) does not include a model of P(x), so it does not need to model the dependencies between features!

12 Conditional Sequence Models We prefer a model that is trained to maximize a conditional probability rather than joint probability: P(y x) instead of P(y,x): 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. Naïve Bayes Logistic Regression Finite State Models Sequence HMMs Linear-chain CRFs General Graphs Generative directed models Conditional Conditional Conditional General CRFs Sequence General Graphs Slide by Cohen & McCallum Linear-Chain Conditional Random Fields From HMMs to CRFs can also be written as (set, ) We let new parameters vary freely, so we need normalization constant Z. Linear-Chain Conditional Random Fields Introduce feature functions One feature per transition One feature per state-observation pair (, ) Then the conditional distribution is This is a linear-chain CRF, but includes only current word s identity as a feature Linear-Chain Conditional Random Fields Conditional p(y x) that follows from joint p(y,x) of HMM is a linear CRF with certain feature functions! Linear-Chain Conditional Random Fields Definition: A linear-chain CRF is a distribution that takes the form parameters feature functions where Z(x) is a normalization function

13 Linear-Chain Conditional Random Fields HMM-like linear-chain CRF y x Linear-chain CRF, in which transition score depends on the current observation y x