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Address Standardization with Latent Semantic

AssociationAuthor : Honglei Guo, Huijia Zhu, Zhili Guo, XiaoXun Zhang, and Zhong Su

Publication : KDD’09Advisor : Chia-Hui ChangPresenter : Chia-Yi Huang

2010/08/12

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Introduction Related Works Latent Semantic Association Method Address Standardization Using LASA Model

and Informative Sampling Experiments Conclusions

Outline

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IntorductionMotivationApproachesRelated Works

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Address data are highly irregular ◦ most of them are often generated by different

people at different times.

Address should be converted to a standard consistent format.◦ Ex: “1101 Kitchawan Road, Route 134, Yorktown

Heights, N.Y. 10598”◦ [House No. : 1101], [Street : Kitchawan Road],

[Route : Route 134], [City : Yorktown Heights], [State: N.Y. ], [Zip :10598]

Introduction

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Latent semantic association (LaSA)◦ To minimize human efforts and augment the

size of labeled training data set.

Address Standardization model is learned form LaSA features and informative samples.

Introduction (cont.)

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Latent Semantic Association Method

Virtual Context DocumentLearning LaSA Model

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In order to minimize the human efforts, we expect use ps(x, y) to approximate pt(x, y).◦ X : feature space to represent word instances.◦ Y : set of semantic labels.◦ ps(x, y), pt(x, y) : the underlying distribution for the labeled

training data set and the target data set.

LaSA model θs,t to capture latent semantic association among words form the unlabeled domain data.◦ Better augments the training data set.◦ Enhance the estimate of the distribution to better

approximate the real domain distribution.

Latent Semantic Association Model

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Virtual Context Document◦ Given a word xi , virtual context document of xi is

◦ F(xiSk) : context feature set of xi in the address sample sk,

1≤k≤n.◦ n : total number of the samples which contain xi in the corpus.

Learning LaSA Model form Virtual Context Documents

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Given vdxi = {f1, …, fj, …, fm} Weight(fi, xi) = log2 {P(fj, xi) / P(fj)P(xi)}

Learning LaSA Model form Virtual Context Documents (cont.)

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Learning LaSA Model Latent dirichlet

allocation(LDA) imposes a dirichlet distrubution on the topic mixture weights corresponding to the documents in the corpus.

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Learning LaSA Model (cont.)

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Address Standardization Using LaSA Model and Informative Sampling

RRM ClassifierLatent Semantic Association FeatureInformative Sampling

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View address standardization as a sequential classification problem.◦ Employs Robust Risk Minimization(RRM) Classifier.

Latent Semantic Association Feature◦ Frequency : 10◦ Number of topic N : 50◦ Context view window size : {-3 , 3}

Address Standardization Using LaSA Model

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Informative sample selection method use a variant of uncertainty-sampling.

More uncertain fragments ate contained in the sample, more informative the sample is.

Given an address sample Si = {tokj}Nj=1,

◦ Tokj : jth token unit in Si

Confidence score of Si :◦ Score(tokj) : confidence score of tokj in Si

◦ TokNum(Si) : total number of token units in Si

◦ UncNum(Si) : the number of uncertain units in Si

Token units with lower confidence score(i.e. Score(tokj) ≤ α) are considered as uncertain units.

Informative Sampling

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Informative Sampling (cont.)

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Data set

Experiments

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Performance Enhancement by LaSA model◦ Relative F-measure enhancement◦ Relative Error Reduction

Experiments(cont.)

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Training Data Reduction by LaSA Feature

Experiments(cont.)

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Cumulative impact of LaSA model and informative sampling

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Cumulative impact of LaSA model and informative sampling

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LaSA-Info method achieves more than 45% reduction in error over the state-of-the-art RRM trained on the same material.

Compared to the supervised learning method, the present approach requires only 5% as much annotated data to achieve the same level of performance.

Conclusions