text classification

Text ClassificationText Classification

The Naïve Bayes algorithmThe Naïve Bayes algorithm

IP notice: most slides from: Chris Manning, plus some from William Cohen, Chien Chin Chen, Jason Eisner, David Yarowsky, Dan Jurafsky, P. Nakov, Marti Hearst, Barbara Rosario

OutlineOutline

Introduction to Text ClassificationIntroduction to Text Classification Also called “text categorization”

Naïve Bayes text classificationNaïve Bayes text classification

Is this spam?Is this spam?

More Applications of Text More Applications of Text ClassificationClassification Authorship identificationAuthorship identification Age/gender identificationAge/gender identification Language IdentificationLanguage Identification Assigning topics such as Yahoo-categoriesAssigning topics such as Yahoo-categories

e.g., "finance," "sports," "news>world>asia>business"e.g., "finance," "sports," "news>world>asia>business" Genre-detectionGenre-detection

e.g., "editorials" "movie-reviews" "news“e.g., "editorials" "movie-reviews" "news“ Opinion/sentiment analysis on a person/productOpinion/sentiment analysis on a person/product

e.g., “like”, “hate”, “neutral”e.g., “like”, “hate”, “neutral” Labels may be domain-specificLabels may be domain-specific

e.g., “contains adult language” : “doesn’t”e.g., “contains adult language” : “doesn’t”

Text Classification: Text Classification: definitiondefinition The classifier: The classifier:

Input: a document d Output: a predicted class c from some fixed set of

labels c1,...,cK

The learner:The learner: Input: a set of m hand-labeled documents (d1,c1),....,

(dm,cm) Output: a learned classifier f:d → c

Slide from William Cohen

Multimedia GUIGarb.Coll.SemanticsML Planning

planningtemporalreasoningplanlanguage...

programmingsemanticslanguageproof...

learningintelligencealgorithmreinforcementnetwork...

garbagecollectionmemoryoptimizationregion...

“planning language proof intelligence”

TrainingData:

TestData:

Classes:(AI)

Document ClassificationDocument Classification

Slide from Chris Manning

(Programming) (HCI)

... ...

Classification Methods: Classification Methods: Hand-coded rulesHand-coded rules Some spam/email filters, etc. Some spam/email filters, etc. E.g., assign category if document contains a given E.g., assign category if document contains a given

boolean combination of wordsboolean combination of words Accuracy is often very high if a rule has been Accuracy is often very high if a rule has been

carefully refined over time by a subject expertcarefully refined over time by a subject expert Building and maintaining these rules is expensiveBuilding and maintaining these rules is expensive


Classification Methods: Classification Methods: Machine LearningMachine Learning Supervised Machine LearningSupervised Machine Learning To learn a function from documents (or To learn a function from documents (or

sentences) to labelssentences) to labels Naive Bayes (simple, common method) Others

• k-Nearest Neighbors (simple, powerful)• Support-vector machines (new, more powerful)• … plus many other methods

No free lunch: requires hand-classified training data• But data can be built up (and refined) by amateurs


Naïve Bayes IntuitionNaïve Bayes Intuition

Representing text for Representing text for classificationclassification


ARGENTINE 1986/87 GRAIN/OILSEED REGISTRATIONSBUENOS AIRES, Feb 26Argentine grain board figures show crop registrations of grains, oilseeds and their

products to February 11, in thousands of tonnes, showing those for future shipments month, 1986/87 total and 1985/86 total to February 12, 1986, in brackets:

• Bread wheat prev 1,655.8, Feb 872.0, March 164.6, total 2,692.4 (4,161.0).• Maize Mar 48.0, total 48.0 (nil).• Sorghum nil (nil)• Oilseed export registrations were:• Sunflowerseed total 15.0 (7.9)• Soybean May 20.0, total 20.0 (nil)

The board also detailed export registrations for subproducts, as follows....

f( )=c

? What is the best representation for the document d being classified?

simplest useful

Bag of words Bag of words representationrepresentation


ARGENTINE 1986/87 GRAIN/OILSEED REGISTRATIONSBUENOS AIRES, Feb 26Argentine grain board figures show crop registrations of grains, oilseeds and their

products to February 11, in thousands of tonnes, showing those for future shipments month, 1986/87 total and 1985/86 total to February 12, 1986, in brackets:

• Bread wheat prev 1,655.8, Feb 872.0, March 164.6, total 2,692.4 (4,161.0).• Maize Mar 48.0, total 48.0 (nil).• Sorghum nil (nil)• Oilseed export registrations were:• Sunflowerseed total 15.0 (7.9)• Soybean May 20.0, total 20.0 (nil)The board also detailed export registrations for subproducts, as follows....

Categories: grain, wheat


xxxxxxxxxxxxxxxxxxx GRAIN/OILSEED xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx grain xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx grains, oilseeds xxxxxxxxxx

xxxxxxxxxxxxxxxxxxxxxxxxxxx tonnes, xxxxxxxxxxxxxxxxx shipments xxxxxxxxxxxx total xxxxxxxxx total xxxxxxxx xxxxxxxxxxxxxxxxxxxx:

• Xxxxx wheat xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx, total xxxxxxxxxxxxxxxx• Maize xxxxxxxxxxxxxxxxx• Sorghum xxxxxxxxxx• Oilseed xxxxxxxxxxxxxxxxxxxxx• Sunflowerseed xxxxxxxxxxxxxx• Soybean xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx....




xxxxxxxxxxxxxxxxxxx GRAIN/OILSEED xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx grain xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx grains, oilseeds

xxxxxxxxxx xxxxxxxxxxxxxxxxxxxxxxxxxxx tonnes, xxxxxxxxxxxxxxxxx shipments xxxxxxxxxxxx total xxxxxxxxx total xxxxxxxx xxxxxxxxxxxxxxxxxxxx:

• Xxxxx wheat xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx, total xxxxxxxxxxxxxxxx

• Maize xxxxxxxxxxxxxxxxx• Sorghum xxxxxxxxxx• Oilseed xxxxxxxxxxxxxxxxxxxxx• Sunflowerseed xxxxxxxxxxxxxx• Soybean xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx....


grain(s) 3

oilseed(s) 2

total 3

wheat 1

maize 1

soybean 1

tonnes 1

... ...

word freq


Formalizing Naïve Formalizing Naïve BayesBayes

Bayes’ RuleBayes’ Rule

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•Allows us to swap the conditioning

•Sometimes easier to estimate one kind of dependence than the other

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Conditional ProbabilityConditional Probability

let let AA and and BB be events be events PP((BB||AA)) = the = the probabilityprobability of event of event BB occurring givenoccurring given event event

AA occursoccurs definition:definition: PP((BB||AA) = ) = PP((AA BB) / ) / PP((AA))

Bayes’ Rule Applied to Bayes’ Rule Applied to Documents and ClassesDocuments and Classes


P (C , D ) P (C | D )P (D ) P (D | C )P (C )

P(C | D) P (D | C)P (C)

P (D)

The Text Classification The Text Classification ProblemProblem

Using a supervised Using a supervised learning methodlearning method, we want to learn a , we want to learn a classifierclassifier (or classification function): (or classification function):

We denote the supervised learning method by We denote the supervised learning method by ::

((TT) =) =

The learning method takes the training set T as input and returns the learned classifier .

Once we have learned Once we have learned , we can apply it to the , we can apply it to the test settest set (or (or test data).test data).

Slide from Chien Chin Chen

CX :

Naïve Bayes Text Naïve Bayes Text ClassificationClassification TheThe Multinomial Naïve Bayes modelMultinomial Naïve Bayes model (NB) is a (NB) is a

probabilistic learning method.probabilistic learning method.

In text classification, our goal is to find the “In text classification, our goal is to find the “bestbest” ” class for the document:class for the document:


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The probability of a document d being in class c.



We can ignore the denominator


Naive Bayes ClassifiersNaive Bayes ClassifiersWe represent an instance We represent an instance DD based on some attributes. based on some attributes.

Task: Classify a new instance Task: Classify a new instance DD based on a tuple of attribute values based on a tuple of attribute values into one of the classes into one of the classes ccjj CC


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Naïve Bayes Classifier: Naïve Naïve Bayes Classifier: Naïve Bayes AssumptionBayes Assumption PP((ccjj))

Can be estimated from the frequency of classes in the training examples.

PP((xx11,x,x22,…,x,…,xnn|c|cjj) ) O(|X|n•|C|) parameters Could only be estimated if a very, very large number of

training examples was available.Naïve Bayes Conditional Independence Assumption:Naïve Bayes Conditional Independence Assumption: Assume that the probability of observing the conjunction Assume that the probability of observing the conjunction

of attributes is equal to the product of the individual of attributes is equal to the product of the individual probabilities probabilities PP((xxii||ccjj))..


Flu

X1 X2 X5X3 X4

feversinus coughrunnynose muscle-ache

The Naïve Bayes The Naïve Bayes ClassifierClassifier

Conditional Independence Assumption:Conditional Independence Assumption: features are independent of each other given the

class:

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Using Multinomial Naive Bayes Using Multinomial Naive Bayes Classifiers to Classify TextClassifiers to Classify Text Attributes are Attributes are text positionstext positions, values are , values are wordswords..

Still too many possibilities Assume that classification is independent of the

positions of the words Use same parameters for each position Result is bag of words model (over tokens not types)

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Learning the ModelLearning the Model

Simplest: maximum likelihood estimateSimplest: maximum likelihood estimate simply use the frequencies in the data

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What if we have seen no training cases where patient had no flu and What if we have seen no training cases where patient had no flu and muscle aches?muscle aches?

Zero probabilities cannot be conditioned away, no matter the other Zero probabilities cannot be conditioned away, no matter the other evidence!evidence!

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Smoothing to Avoid Smoothing to Avoid OverfittingOverfitting

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extent of“smoothing”

• Laplace:

• Textj single document containing all docsj

• for each word wk in Vocabulary

nkj number of occurrences of wk in Textj

nk number of occurrences of wk in all docs

Naïve Bayes: LearningNaïve Bayes: Learning

From training corpus, extract From training corpus, extract VocabularyVocabulary Calculate required Calculate required PP((ccjj)) and and PP((wwkk | c | cjj)) termsterms

For each cj in C do• docsj subset of documents for which the target class is cj

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Naïve Bayes: ClassifyingNaïve Bayes: Classifying

positions positions all word positions in current document all word positions in current document which contain tokens found in which contain tokens found in

VocabularyVocabulary

Return Return ccNBNB, where, where

positionsi

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Underflow Prevention: log Underflow Prevention: log spacespace Multiplying lots of probabilities, which are between 0 and Multiplying lots of probabilities, which are between 0 and

1 by definition, can result in floating-point underflow.1 by definition, can result in floating-point underflow. Since Since log(log(xyxy) = log() = log(xx) + log() + log(yy)), it is better to perform all , it is better to perform all

computations by summing logs of probabilities rather computations by summing logs of probabilities rather than multiplying probabilities.than multiplying probabilities.

Class with highest final un-normalized log probability Class with highest final un-normalized log probability score is still the most probable.score is still the most probable.

Note that model is now just max of sum of weights…Note that model is now just max of sum of weights…

positionsi

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NB cxPcPc )|(log)(logargmaxj


Naïve Bayes Generative Naïve Bayes Generative Model for TextModel for Text

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Slide from Ray Mooney

cNB argmaxcj C

P(c j ) P(x i | c j )ipositions

Choose a class c according to P(c)

Choose a class c according to P(c)

Then choose a word from that class with probability P(x|c)

Then choose a word from that class with probability P(x|c)

Essentially model probability of each class as class-specific unigram language model

Essentially model probability of each class as class-specific unigram language model

Naïve Bayes and Naïve Bayes and Language ModelingLanguage Modeling Naïve Bayes classifiers can use any sort of Naïve Bayes classifiers can use any sort of

featuresfeatures URL, email address, dictionary

But, if:But, if: We use only word features We use all of the words in the text (not subset)

ThenThen Naïve Bayes bears similarity to language modeling

Each class = Unigram Each class = Unigram language modellanguage model Assign to each word: Assign to each word: PP((wordword | | cc)) Assign to each sentence: Assign to each sentence: PP((c c | | ss) = ) = PP((cc)∏)∏PP((wwi i | | cc))

w P(w | c)

I 0.1

love 0.1

this 0.01

fun 0.05

film 0.1

I love this fun film

0.1 0.1 0.05 0.01 0.1

P(s | c) = 0.0000005

Naïve Bayes Language Naïve Bayes Language ModelModel Two classes: in language, out languageTwo classes: in language, out language

In Language

I 0.1

love 0.1

this 0.01

fun 0.05

film 0.1

Out Language

I 0.2

love 0.001

this 0.01

fun 0.005

film 0.1

I love this fun film

0.1 0.1 0.05 0.01 0.1

0.2 0.001 0.01 0.005 0.1

P(s | in) = P(s | out)

Naïve Bayes Naïve Bayes Classification Classification

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Slide from Ray Mooney

Naïve Bayes Text Classification Naïve Bayes Text Classification ExampleExample

Training: Training: Vocabulary V = {Chinese, Beijing, Shanghai,

Macao, Tokyo, Japan} and |V | = 6.

P(c) = 3/4 and P(~c) = 1/4.

P(Chinese|c) = (5+1) / (8+6) = 6/14 = 3/7

P(Chinese|~c) = (1+1) / (3+6) = 2/9 P(Tokyo|c) = P(Japan|c) = (0+1)/(8+6) =1/14 P(Chinese|~c) = (1+1)/(3+6) = 2/9 P(Tokyo|~c) = p(Japan|~c) = (1+1/)3+6) = 2/9

Testing:Testing: P(c|d) 3/4 * (3/7)3 * 1/14 * 1/14

≈ 0.0003

P(~c|d) 1/4 * (2/9)3 * 2/9 * 2/9 ≈ 0.0001


Set Doc Words Class

Train 1 Chinese Bejing Chinese c

2 Chinese Chinese Shanghai c

3 Chinese Macao c

4 Tokyo Japan Chinese ~c

Test 5 Chinese Chinese Chinese Tokyo Japan ?

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Naïve Bayes Text Naïve Bayes Text ClassificationClassification Naïve Bayes algorithm – training phase.Naïve Bayes algorithm – training phase.


TrainMultinomialNB(C, D)V ExtractVocabulary(D)N CountDocs(D)

for each c in C Nc CountDocsInClass(D, c) prior[c] Nc / Count(C) textc TextOfAllDocsInClass(D, c) for each t in V Ftc CountOccurrencesOfTerm(t, textc) for each t in V condprob[t][c] (Ftc+1) / ∑(Ft’c+1)

return V, prior, condprob

TrainMultinomialNB(C, D)V ExtractVocabulary(D)N CountDocs(D)

for each c in C Nc CountDocsInClass(D, c) prior[c] Nc / Count(C) textc TextOfAllDocsInClass(D, c) for each t in V Ftc CountOccurrencesOfTerm(t, textc) for each t in V condprob[t][c] (Ftc+1) / ∑(Ft’c+1)

return V, prior, condprob

Naïve Bayes Text Naïve Bayes Text ClassificationClassification Naïve Bayes algorithm – testing phase.Naïve Bayes algorithm – testing phase.


ApplyMultinomialNB(C, V, prior, condProb, d)W ExtractTokensFromDoc(V, d)for each c in C score[c] log prior[c] for each t in W score[c] += log condprob[t][c]

return argmaxcscore[c]

ApplyMultinomialNB(C, V, prior, condProb, d)W ExtractTokensFromDoc(V, d)for each c in C score[c] log prior[c] for each t in W score[c] += log condprob[t][c]

return argmaxcscore[c]

Evaluating CategorizationEvaluating Categorization

Evaluation must be done on test data that are Evaluation must be done on test data that are independentindependent of the training data of the training data usually a disjoint set of instances

Classification accuracyClassification accuracy: : cc//nn where where nn is the total is the total number of test instances and number of test instances and cc is the number of is the number of test instances correctly classified by the system.test instances correctly classified by the system. Adequate if one class per document

Results can vary based on sampling error due to Results can vary based on sampling error due to different training and test sets.different training and test sets. Average results over multiple training and test sets

(splits of the overall data) for the best results.


Measuring PerformanceMeasuring Performance

PrecisionPrecision = = good messages keptgood messages kept all messages kept all messages kept

RecallRecall = =good messages keptgood messages kept all good messages all good messages

Trade off precision vs. recall by setting thresholdMeasure the curve on annotated dev data (or test data)Choose a threshold where user is comfortable

Trade off precision vs. recall by setting thresholdMeasure the curve on annotated dev data (or test data)Choose a threshold where user is comfortable

Precision vs. Recall of Good (non-spam) Email

0%

25%

50%

75%

100%

0% 25% 50% 75% 100%

Recall

Pre

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Slide from Jason Eisner

Measuring PerformanceMeasuring Performance


Precision vs. Recall of Good (non-spam) Email

0%

25%

50%

75%

100%

0% 25% 50% 75% 100%

Recall

Pre

cisi

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low threshold:keep all the good stuff,but a lot of the bad too

high threshold:all we keep is good,but we don’t keep much

OK for spam filtering and legal search

OK for search engines (maybe)

would prefer to be here!

point whereprecision=recall(often reported)

The 2-by-2 contingency The 2-by-2 contingency tabletable

Correct Incorrect

Selected True Positive False Positive

Not selected False Negative True Negative

Precision and RecallPrecision and Recall

Precision: % of selected items that are Precision: % of selected items that are correctcorrect

Recall: % of correct items that are selectedRecall: % of correct items that are selected

A Combined measure: FA Combined measure: F

The F measure assesses the P/R tradeoff, The F measure assesses the P/R tradeoff, through the weighted harmonic mean:through the weighted harmonic mean:

Multiclass ClassificationMulticlass Classification

Dealing with > 2 classesDealing with > 2 classes For each class cFor each class c

Build a binary classifier Yc to distinguish c from ~c

Given a test document dGiven a test document d Evaluate membership in each class using Yc

Assign d to each class c for which Yc returns true

Micro- vs. Macro-Micro- vs. Macro-AveragingAveraging If we have more than one class, how do we If we have more than one class, how do we

combine multiple performance measures into combine multiple performance measures into one quantityone quantity

MacroaveragingMacroaveraging: compute performance for : compute performance for each class, then averageeach class, then average

MicroaveragingMicroaveraging: collect decision for all : collect decision for all classes, compute contingency table, evaluateclasses, compute contingency table, evaluate

More Complicated Cases of More Complicated Cases of Measuring PerformanceMeasuring Performance For multiclass classifiers:For multiclass classifiers:

Average accuracy (or precision or recall) of 2-way distinctions: Sports or not, News or not, etc.

Better, estimate the cost of different kinds of errors• e.g., how bad is each of the following?

– putting Sports articles in the News section– putting Fashion articles in the News section– putting News articles in the Fashion section

• Now tune system to minimize total cost

For ranking systems:For ranking systems: Correlate with human rankings? Get active feedback from user? Measure user’s wasted time by tracking clicks?


Which articles are most Sports-like?Which articles / webpages most relevant?

Evaluation BenchmarkEvaluation Benchmark

Reuters-21578 Data SetReuters-21578 Data Set Most (over)used data set, 21,578 docs (each 90 Most (over)used data set, 21,578 docs (each 90

types, 200 tokens)types, 200 tokens) 9603 training, 3299 test articles 9603 training, 3299 test articles

(ModApte/Lewis split)(ModApte/Lewis split) 118 categories118 categories

An article can be in more than one category Learn 118 binary category distinctions

Average document (with at least one category) Average document (with at least one category) has 1.24 classeshas 1.24 classes

Only about 10 out of 118 categories are largeOnly about 10 out of 118 categories are large

Training sizeTraining size

The more the better! (usually)The more the better! (usually) Results for text classificationResults for text classification**

*From: Improving the Performance of Naive Bayes for Text Classification, Shen and Yang, Slide from Nakov/Hearst/Rosario

Test error vs training size on the newsgroups rec.sport.baseball and rec.sport.hockey



Test error vs training size on the newsgroups alt.atheism and talk.religion.misc

Training SizeTraining Size Author identificationAuthor identification

Authorship Attribution a Comparison Of Three Methods, Matthew Care, Slide from Nakov/Hearst/Rosario

Violation of NB Violation of NB AssumptionsAssumptions Conditional independenceConditional independence ““Positional independence”Positional independence” Examples?Examples?


Naïve Bayes is Not So Naïve Bayes is Not So NaïveNaïve Naïve Bayes: first and second place in KDD-CUP 97 Naïve Bayes: first and second place in KDD-CUP 97

competition, among 16 (then) state of the art algorithmscompetition, among 16 (then) state of the art algorithmsGoal: Financial services industry direct mail response prediction model: Predict if the recipient of mail will actually respond to the advertisement – 750,000 records.

Robust to Irrelevant FeaturesRobust to Irrelevant FeaturesIrrelevant Features cancel each other without affecting resultsInstead Decision Trees can heavily suffer from this.

Very good in domains with many Very good in domains with many equally importantequally important features featuresDecision Trees suffer from fragmentation in such cases – especially if little data

A good dependable baseline for text classification (but not the A good dependable baseline for text classification (but not the best)!best)!


Naïve Bayes is Not So Naïve Bayes is Not So NaïveNaïve Optimal if the Independence Assumptions Optimal if the Independence Assumptions

hold:hold:If assumed independence is correct, then it is the Bayes Optimal If assumed independence is correct, then it is the Bayes Optimal Classifier for problemClassifier for problem

Very Fast:Very Fast:Learning with one pass of counting over the data; testing linear in Learning with one pass of counting over the data; testing linear in the number of attributes, and document collection sizethe number of attributes, and document collection size

Low Storage requirementsLow Storage requirements Online Learning AlgorithmOnline Learning Algorithm

Can be trained incrementally, on new examplesCan be trained incrementally, on new examples

SpamAssassinSpamAssassin

Naïve Bayes widely used in spam filteringNaïve Bayes widely used in spam filtering Paul Graham’s A Plan for Spam

• A mutant with more mutant offspring... Naive Bayes-like classifier with weird parameter

estimation But also many other things: black hole lists, etc.

Many email topic filters also use NB classifiersMany email topic filters also use NB classifiers


SpamAssassin TestsSpamAssassin Tests Mentions Generic ViagraMentions Generic Viagra Online PharmacyOnline Pharmacy No prescription neededNo prescription needed Mentions millions of (dollar) ((dollar) NN,NNN,NNN.NN)Mentions millions of (dollar) ((dollar) NN,NNN,NNN.NN) Talks about Oprah with an exclamation!Talks about Oprah with an exclamation! Phrase: impress ... girlPhrase: impress ... girl From: starts with many numbersFrom: starts with many numbers Subject contains "Your Family”Subject contains "Your Family” Subject is all capitalsSubject is all capitals HTML has a low ratio of text to image areaHTML has a low ratio of text to image area One hundred percent guaranteedOne hundred percent guaranteed Claims you can be removed from the listClaims you can be removed from the list 'Prestigious Non-Accredited Universities''Prestigious Non-Accredited Universities' http://spamassassin.apache.org/tests_3_3_x.html

Naïve Bayes: Word Naïve Bayes: Word Sense DisambiguationSense Disambiguation ww an ambiguous wordan ambiguous word ss11, …, s, …, sKK senses for word senses for word ww v1, …, vJ words in the context of words in the context of ww PP((ssjj)) prior probability of sense prior probability of sense ssjj PP((vvjj||sskk)) probability that word probability that word vvjj occurs in context of sense occurs in context of sense sskk

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text classification

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text classificationalso

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sunflowerseed total

crop registrations of