Name-Ethnicity Classification and Ethnicity ... - Dr. C. Lee Giles

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Smith–Waterman algorithm is applied to find the optimalalignment between the two tokenized names. Features arethen extracted from the optimal alignment. A logistic regressionmodel is then applied to convert features into the matchprobability. Using the name-ethnicity classifier to separatenames into ethnic groups, we train separate logistic regressionmodels for each name-ethnicity. Now we discuss eachstep in more detail.Computing Optimal AlignmentWe use a modified version of Smith–Waterman algorithmto find the optimal alignment between two names.The Smith–Waterman algorithm is a popular alignment algorithmfor identifying common molecular subsequences(Smith and Waterman 1981). However, the standardSmith–Waterman operates on sequences of characters (e.g.aligning characters in ‘ACAT‘ with ‘AGCA’) and only allowsexact match between characters. We, on the other hand,are interested in aligning sequences of name tokens, notcharacters. In addition, name tokens can align without beingan exact match (such as ‘Kathy’ and ‘Katharine’). Therefore,we extend the Smith–Waterman algorithm to tokenalignment and replace its exact match comparison with anapproximate matching function.For a given name pair p = (p 0 ,...,p m ) andq = (q 0 ,...,q n ), where p i ,q j are word tokens in pand q respectively, we define the token similarity betweenthe token p i and q j , denoted by S(p i ,q j ), as follows:⎧⎪⎨ Jaro-Winkler(p i,q j) if |p i|≥2 and |q j|≥2S(p i,q j)= 0.95⎪⎩if (|p i|=1 or |q j|=1) and init(p i)=init(q j)0 otherwisewhere init(a) = the first character of a. Basically, we measurethe similarity between tokens with the Jaro-Winklersimilarity, which works well for short strings. But in thecase where one of the token is just an initial, the similarityis 0.95 if their initials are compatible, such as between p i=‘E’ and q j =‘Edward,’ and 0 otherwise. The value 0.95is arbitrary chosen, because we want the similarity of theinitial-match cases to be sufficiently high, but still less thanthose of exact matches.We then define the scoring matrix, H, where H(i, j) representsthe maximum similarity score between (p 0 ,...,p i )and (q 0 ,...,q j ), in term of a similarity function S as follows:H(i,0)=0,H(0,j)=0,0≤i≤m0≤j≤nH(i+1,j+1)=⎧H(i,j)+S(p i,q j) if S(p i,q j) > 0.8 //p i matches q j⎪⎨ H(i,j−1)+1 if p i=q j−1q j //p i spans 2 tokensmax H(i−1,j)+1 if p i−1p i=q j //q j spans 2 tokens⎪⎩H(i+1,j)H(i,j+1)for 0≤i≤m−1, 0≤j≤n−1// skip q j// skip p iThe matrix H can be computed using dynamic programming.Unlike in the standard Smith–Waterman, where eachcell can align with at most one other cell, our name token canalign with upto two tokens in the corresponding name. This− Marcelo Bicho Dos Santos− 0.0 0.0 0.0 0.0 0.0Marcelino 0.0 0.96 0.96 0.96 0.96B 0.0 0.96 1.91 1.91 1.91Santos 0.0 0.96 1.91 1.91 2.91Figure 1: Scoring matrix H for aligning ‘Marcelo Bicho DosSantos’ and ‘Marcelino B Santos.’ The arrows show the pathof the optimal alignment generated by the traceback procedure.Table 4: Examples of the optimal alignments between (a)‘Marcelo Bicho Dos Santos’ and ‘Marcelino B Santos.’ (b)‘Juan Ginés Sánchez Moreno’ and ‘G Lopez Moreno.’(a) p Marcelo Bicho Dos Santosq Marcelino B - SantosS(p i ,q j ) 0.96 0.95 1.0(b) p Juan Ginés Sánchez Morenoq - G Lopez MorenoS(p i ,q j ) 0.95 1.0allows our alignment algorithm to account for a commonproblem in name matching, where multiple name segmentationsexist for instance (‘DeFélice’ ⇔ ‘De Félice’) and(‘Min Seo Kim’ ⇔ ‘Minseo Kim’), without any special preprocessingsteps. After the matrix H is computed, the tracebackprocedure similar to the one in Smith–Waterman algorithm,can be used to find the optimal alignment. Figure 1shows an example of the matrix H for aligning ‘MarcelinoB Santos’ and ‘Marcelo Bicho Dos Santos’ and the resultingoptimal alignment are shown in Figure 4a.Since name field reversal such as (‘Min Seo Kim’ ⇒‘Kim Min Seo’) is very common, especially with East Asiannames, we introduce a shift operator α such that α t (p) shiftseach token in p, t positions to the right for t > 0 and tothe left for t < 0. So α 1 (p) = (p m ,p 0 ,...,p m−1 ) andα −1 (p) =(p 1 ,...,p m ,p 0 ). Let Ω(p, q) denotes the optimalalignment between p and q, and Ω s (p, q) denotes the optimalalignment with the shift operation. ThenΩ s (p, q) =arg max M(Ω(α t (p),q)))Ω(α t (p),q),t∈ {−1, 0, 1}where M(Ω(p, q)) is the similarity score for the alignmentΩ(p, q) in the matrix H. So Ω s (p, q) is the best alignmentout of all alignments with various shifts.Name Matching ProbabilityFrom the resulting optimal alignment Ω s (p, q), we thencategorize each match/mismatch based on its location. Forexample, we differentiate unmatched tokens at the endof a name from unmatched tokens at the beginning of aname. The probability P , that a name p matches a nameq is then computed based on the number of each types ofmatch/mismatch in their optimal alignment Ω s (p, q). More1144
specifically, the probability P depends on the feature vectorf =(x 1 ,...,x 7 ) wherex 1 = # of skip tokens before the first aligned token pairx 2 = # of skip tokens after the last aligned token pairx 3 = # of skip tokens in the middle that are initialsx 4 = # of skip tokens in the middle that are non-initialsx 5 = # conflicting tokensx 6 = # of shift operation in the optimal alignment = |t|x 7 = products of similarity scores of aligned token pairs=S(p u ,q v )u,vp u align with q v in Ω s(p,q)Two tokens p i , q j are considered to be conflicting ()if they both are unaligned and are between the same alignedtoken pairs, for example ‘Sánchez’ and ‘Lopez’ in Table 4b.Unaligned tokens that do not conflict with any tokens areconsidered skip tokens (), for example ‘Dos’ in Table4a and ‘Juan’ in Table 4b.To illustrate, consider the two optimal alignments shownin Table 4. In (a), there are one skip token in the middle andthree aligned tokens pairs. So x 3 =1and x 7 =0.96×0.95×1 thus f =(0, 0, 1, 0, 0, 0,.91). In (b), there are two alignedtoken pairs, two conflicting tokens and one skip token. Sox 1 =1,x 5 =2and x 7 =0.95 × 1.0.To compute P , we assume that the odds ratio of P is directlyproportion toP1 − P ∝ D 1 x1 D 2x 2... D 6x 6D 7log(x 7)where D 1 ,...,D 6 ,D 7 are discounting factors for differenttypes of alignment/misalignment (x 1 ,...,x 6 ,x 7 ). The oddsratio of P can be rewritten as:P1 − P = D x0D 1 x1 ... D 6 log(x6 D 7)7 (1)Plog(1 − P )=β 0 + β 1 x 1 + ... + β 7 log(x 7 ) (2)logit(P )=β 0 + β 1 x 1 + ... + β 7 log(x 7 ) (3)The equation (3) above is simply a logistic regression model.Thus the optimal values for the coefficient β 1 ,...,β 7 withrespect to a dataset can be easily estimated.In the training phase, we use the name-ethnicity classifierto classify names in the training data according to their ethnicities.Separate logistic regression models are then builtfor each name-ethnicity, e.g. one for Spanish names, onefor Middle Eastern names and so on. In addition, a backoffmodel is train over all the training data. In the evaluationphase, if both names to be compared are classified as ofthe same ethnicity, the ethnicity specific regression model isused, otherwise the default model is used.Currently, the token similarity function S(pi, qj) is ethnicityindependent. However, in the future, different tokensimilarity functions can be used for different ethnicitynames. For instance, if the system detects that the namesbeing compared are Chinese, a special similarity functionTable 5: The precision, recall and F1 measure of the nameethnicityclassifier for each ethnicity.Ethnicity Precision Recall F1MEA 0.79 0.78 0.79IND 0.89 0.86 0.87ENG 0.79 0.85 0.82FRN 0.80 0.80 0.80GER 0.84 0.85 0.85ITA 0.85 0.86 0.85SPA 0.82 0.79 0.81RUS 0.90 0.85 0.81CHI 0.92 0.90 0.91JAP 0.97 0.95 0.96KOR 0.93 0.92 0.92VIE 0.93 0.83 0.88Accuracy 0.85that includes the mapping between Hanyu-Pinyin and Wade-Giles (two different transliteration systems for MandarinChinese) can be used instead. Additionally, in a real system,one can also improve the similarity function S(pi, qj)by incorporating nickname dictionaries for different ethnicgroups; for example, mapping ‘Bartholomew’ with ‘Bart,’or ‘Meus’ for English names, and mapping the Chinese firstname ‘Jian’ to its Western nickname ‘Jerry.’ExperimentsName-Ethnicity Classification on WikipediaTo assess the performance of our name-ethnicity classifier,we randomly split the name list collected from Wikipidiainto 70% training data and 30% test data. Table 5 shows theprecision, recall and F1 measure of the classifier for eachethnicity. The overall classification accuracy is 0.85, withJapanese as the most identifiable name-ethnicity (F1=0.96),followed by Korean (F1=0.92). In general, the classifier doeswell identifying East Asian names (CHI, JAP, KOR, andVIE) with over 90% precision and recall with just one exception,VIE’s recall. The most problematic class is MiddleEastern names (MEA) with 0.79 F1, followed by Frenchwith 0.80 F1. We also ran another experiment without anydiacritic features; every name is normalized to its ASCIIequivalence. With just ASCII features, the classification accuracydrops down slightly to 0.83. This suggests that evenwithout diacritics, each name-ethnicity still has identifiablecharacteristics that can be used for classification.The confusion matrix between different name-ethnicitiesis shown in Figure 2. We observe that Middle Eastern namesare mostly confused with Indian names. This is not unexpected,since India has a large population of Muslims. Mostof the confusion for European names are with other Europeannames, especially between English, French, and German.The majority of confusion for Russian names are withGerman names.We also examine the coefficient of each feature learnedby the classifier. The most predictive features for each non-English ethnic group, are listed in Table 6 according to theircoefficients in the logistic regression. For instance, the ‘bh’1145
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Page 3: Table 2: Name-Ethnicity data gather
Page 7: Figure 3: The recall-precision curv

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