Optimizing Local Probability Models for Statistical Parsing Kristina Toutanova, Mark Mitchell, Christopher Manning Computer Science Department Stanford University

Highlights Choosing a local probability model P(expansion(n)|history(n)) for statistical parsing – a comparison of commonly used models A new player – memory based models and their relation to interpolated models Joint likelihood, conditional likelihood and classification accuracy for models of this form

Motivation Many problems in natural language processing are disambiguation problems word senses jaguar – a big cat, a car, name of a Java package line - phone, queue, in mathematics, air line, etc. part-of-speech tags (noun, verb, proper noun, etc.) ? ? ? Joy makes progress every day. NN VB DTNN NNP VBZ NNS

Parsing as Classification “I would like to meet with you again on Monday” Input: a sentence Classify to one of the possible parses

Motivation – Classification Problems There are two major differences from typical ML domains: The number of classes can be very large or even infinite; the set of available classes for an input varies (and depends on a grammar) Data is usually very sparse and the number of possible features is large (e.g. words)

Solutions The possible parse trees are broken down into small pieces defining features features are now functions of input and class, not input only Discriminative or generative models are built using these features we concentrate on generative models here; when a huge number of analyses are possible, they are the only practical ones

History-Based Generative Parsing Models “ Tuesday Marks bought Brooks”. S TOP NPNP-CVP NNP Tuesday The generative models learn a distribution P(S,T) on pairs: select a single most likely parse based on:

Factors in the Performance of Generative History-Based Models The chosen decomposition of parse tree generation, including the representation of parse tree nodes and the independence assumptions The model family chosen for representing local probability distributions: Decision Trees, Naïve Bayes, Log-linear Models The optimization method for fitting major and smoothing parameters: Maximum likelihood, maximum conditional likelihood, minimum error rate, etc.

Previous Studies and This Work The influence of the previous three factors has not been isolated in previous work: authors presented specific choices for all components and the importance of each was unclear. We assume the generative history-based model and set of features (the representation of parse tree nodes) are fixed and we study carefully the other two factors.

Deleted Interpolation Estimating the probability P(y|X) by interpolating relative frequency estimates for lower-order distributions Most commonly used: linear feature subsets order Jelinek-Mercer with fixed weight, Witten Bell with varying d, Decision Trees with path interpolation,Memory-Based Learning

Memory-Based Learning as Deleted Interpolation In k-NN, the probability of a class given features is estimated as: If the distance function depends only on the positions of the matching features*, it is a case of deleted interpolation

Memory-Based Learning as Deleted Interpolation P(eye-color=blue|hair-color=blond) We have N=12 samples of people d=1 or d=0 (match), w(1)=w1, w(0)=w0, K=12 Deleted Interpolation where the interpolation weights depend on the counts and weights of nearest neighbors at all accepted distances

The Task and Features Used NoNameExample 1Node label HCOMP 2Parent node label HCOMP 3Node direction left 4Parent node direction none 5Grandparent node label IMPER 6Great grandparent node label TOP 7Left sister node label none 8Category of node verb sentlengt h struct amb random % Maximum ambiguity – 507, minimum - 2 see letus LET_V1US IMPER SEE_V3 HCOMP TOP

Experiments Linear Feature Subsets Order Jelinek-Mercer with fixed weight Witten Bell with varying d Linear Memory-Based Learning Arbitrary Feature Subsets Order Decision Trees Memory-Based Learning Log-linear Models Experiments on the connection among likelihoods and accuracy

Experiments – Linear Sequence The features {1,2,…,8} ordered by gain ratio {1,8,2,3,5,4,7,6} Jelinek Mercer Fixed Weight Witten-Bell Varying d

Experiments – Linear Sequence heavy smoothing for best results

MBL Linear Subsets Sequence Restrict MBL to be an instance of the same linear subsets sequence deleted interpolation as follows: Weighting functions INV3 and INV4 performed best: LKNN3 best at K=3, % LKNN4 best at K=15, % LKNN4 is best of all

Experiments Linear Subsets Feature Order Jelinek-Mercer with fixed weight Witten Bell with varying d Linear Memory-Based Learning Arbitrary Subsets Feature Order Decision Trees Memory-Based Learning Log-linear Models Experiments on the connection among likelihoods and accuracy

Model Implementations – Decision Trees ( DecTreeWBd ) n-ary decision trees; If we choose a feature f to split on, all its values form subtrees splitting criterion – gain ratio final probabilities estimates at the leaves are Witten Bell d interpolations of estimates on the path to the root feat: 1 feat:2 HCOMP instances of deleted interpolation models! NOPTCOMP

Model Implementations – Log-linear Models Binary features formed by instantiating templates Three models with different allowable features Single attributes only LogLinSingle Pairs of attributes, only pairs involving the most important feature (node label) LogLinPairs Linear feature subsets – comparable to previous models LogLinBackoff Gaussian smoothing was used Trained by Conjugate Gradient (Stanford Classify Package)

Model Implementations – Memory-Based Learning Weighting functions INV3 and INV4 KNN4 better than DecTreeWBd and Log-linear models KNN4 has 5.8% error reduction from WBd (significant at the 0.01 level) Model KNN4DecTree WBd LogLin Single LogLin Pairs LogLin Backof f Accurac y 80.79% 79.66%78.65 % % %

Accuracy Curves for MBL and Decision Trees

Experiments Linear Subsets Feature Order Jelinek-Mercer with fixed weight Witten Bell with varying d Linear Memory-Based Learning Arbitrary Subsets Feature Order Decision Trees Memory-Based Learning Log-linear Models Experiments on the connection among likelihoods and accuracy

Joint Likelihood, Conditional Likelihood, and Classification Accuracy Our aim is to maximize parsing accuracy, but: Smoothing parameters are usually fit on held- out data to maximize joint likelihood Sometimes conditional likelihood is optimized We look at the relationship among the maxima of these three scoring functions, depending on the amount of smoothing, finding that: Much heavier smoothing is needed to maximize accuracy than joint likelihood Conditional likelihood also increases with smoothing, even long after the maximum for joint likelihood

Test Set Performance versus Amount of Smoothing - I

Test Set Performance versus Amount of Smoothing

Test Set Performance versus Amount of Smoothing –PP Attachment Witten-Bell Varying d

Summary The problem of effectively estimating local probability distributions for compound decision models used for classification is under-explored We showed that the chosen local distribution model matters We showed the relationship between MBL and deleted interpolation models MBL with large numbers of neighbors and appropriate weighting outperformed more expensive and popular algorithms – Decision Trees and Log- linear Models Fitting a small number of smoothing parameters to maximize classification accuracy is promising for improving performance

Future Work Compare MBL to other state-of-the art smoothing methods Better ways of fitting MBL weight functions Theoretical investigation of bias-variance tradeoffs for compound decision systems with strong independence assumptions