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### 807 - TEXT ANALYTICS

Massimo PoesioLecture 2: Text Classification

TEXT CLASSIFICATION

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HOW DOCUMENT TAGS ARE PRODUCED

- Traditionally: by HAND by EXPERTS (curators)
- E.g., Yahoo!, Looksmart, about.com, ODP, Medline
- Also: Bridgeman, UK Data Archive
- very accurate when job is done by experts
- consistent when the problem size and team is small
- difficult and expensive to scale
- Now:
- AUTOMATICALLY
- By CROWDS

CLASSIFICATION AND CATEGORIZATION

- Given:
- A description of an instance, xX, where X is the instance language or instance space.
- Issue: how to represent text documents.
- A fixed set of categories:

C ={c1, c2,…, cn}

- Determine:
- The category of x: c(x)C, where c(x) is a categorization function whose domain is X and whose range is C.
- We want to know how to build categorization functions (“classifiers”).

AUTOMATIC TEXT CLASSIFICATION

- Supervised
- Training set
- Test set
- Clustering: next week

Text Categorization: attributes

- Representations of text are very high dimensional (one feature for each word).
- High-bias algorithms that prevent overfitting in high-dimensional space are best.
- For most text categorization tasks, there are many irrelevant and many relevant features.
- Methods that combine evidence from many or all features (e.g. naive Bayes, kNN, neural-nets) tend to work better than ones that try to isolate just a few relevant features (standard decision-tree or rule induction)*

*Although one can compensate by using many rules

Bayesian Methods

- Learning and classification methods based on probability theory.
- Bayes theorem plays a critical role in probabilistic learning and classification.
- Build a generative model that approximates how data is produced
- Uses prior probability of each category given no information about an item.
- Categorization produces a posterior probability distribution over the possible categories given a description of an item.

Maximum likelihood Hypothesis

If all hypotheses are a priori equally likely,we only

need to consider the P(D|h) term:

Naive Bayes Classifiers

Task: Classify a new instance based on a tuple of attribute values

Naïve Bayes Classifier: Assumptions

- P(cj)
- Can be estimated from the frequency of classes in the training examples.
- P(x1,x2,…,xn|cj)
- O(|X|n•|C|)
- Could only be estimated if a very, very large number of training examples was available.

Conditional Independence Assumption:

Assume that the probability of observing the conjunction of attributes is equal to the product of the individual probabilities.

X1

X2

X3

X4

X5

runnynose

sinus

cough

fever

muscle-ache

The Naïve Bayes Classifier- Conditional Independence Assumption: features are independent of each other given the class:

X1

X2

X3

X4

X5

X6

Learning the Model- Common practice:maximum likelihood
- simply use the frequencies in the data

X1

X2

X3

X4

X5

runnynose

sinus

cough

fever

muscle-ache

Problem with Max Likelihood- What if we have seen no training cases where patient had no flu and muscle aches?
- Zero probabilities cannot be conditioned away, no matter the other evidence!

Smoothing to Avoid Overfitting

# of values of Xi

overall fraction in data where Xi=xi,k

- Somewhat more subtle version

extent of

“smoothing”

Using Naive Bayes Classifiers to Classify Text: Basic method

- Attributes are text positions, values are words.

- Naive Bayes assumption is clearly violated.
- Example?
- Still too many possibilities
- Assume that classification is independent of the positions of the words
- Use same parameters for each position

Text Classification Algorithms: Learning

- From training corpus, extract Vocabulary
- Calculate required P(cj)and P(xk | cj)terms
- For each cj in Cdo
- docsjsubset of documents for which the target class is cj

- Textj single document containing all docsj
- for each word xkin Vocabulary
- nk number of occurrences ofxkin Textj

Text Classification Algorithms: Classifying

- positions all word positions in current document which contain tokens found in Vocabulary
- Return cNB, where

Underflow Prevention

- Multiplying lots of probabilities, which are between 0 and 1 by definition, can result in floating-point underflow.
- Since log(xy) = log(x) + log(y), it is better to perform all computations by summing logs of probabilities rather than multiplying probabilities.
- Class with highest final un-normalized log probability score is still the most probable.

Naïve Bayes Posterior Probabilities

- Classification results of naïve Bayes (the class with maximum posterior probability) are usually fairly accurate.
- However, due to the inadequacy of the conditional independence assumption, the actual posterior-probability numerical estimates are not.
- Output probabilities are generally very close to 0 or 1.

Feature Selection

- Text collections have a large number of features
- 10,000 – 1,000,000 unique words – and more
- Make using a particular classifier feasible
- Some classifiers can’t deal with 100,000s of feat’s
- Reduce training time
- Training time for some methods is quadratic or worse in the number of features (e.g., logistic regression)
- Improve generalization
- Eliminate noise features
- Avoid overfitting

Recap: Feature Reduction

- Standard ways of reducing feature space for text
- Stemming
- Laugh, laughs, laughing, laughed -> laugh
- Stop word removal
- E.g., eliminate all prepositions
- Conversion to lower case
- Tokenization
- Break on all special characters: fire-fighter -> fire, fighter

Feature Selection

- Yang and Pedersen 1997
- Comparison of different selection criteria
- DF – document frequency
- IG – information gain
- MI – mutual information
- CHI – chi square
- Common strategy
- Compute statistic for each term
- Keep n terms with highest value of this statistic

Feature selection via Mutual Information

- We might not want to use all words, but just reliable, good discriminators
- In training set, choose k words which best discriminate the categories.
- One way is in terms of Mutual Information:
- For each word w and each category c

Feature selection via MI (contd.)

- For each category we build a list of k most discriminating terms.
- For example (on 20 Newsgroups):
- sci.electronics: circuit, voltage, amp, ground, copy, battery, electronics, cooling, …
- rec.autos: car, cars, engine, ford, dealer, mustang, oil, collision, autos, tires, toyota, …
- Greedy: does not account for correlations between terms
- In general feature selection is necessary for binomial NB, but not for multinomial NB

Chi-Square

X^2 = N(AD-BC)^2 / ( (A+B) (A+C) (B+D) (C+D) )

Use either maximum or average X^2

Value for complete independence?

Document Frequency

- Number of documents a term occurs in
- Is sometimes used for eliminating both very frequent and very infrequent terms
- How is document frequency measure different from the other 3 measures?

Yang&Pedersen: Experiments

- Two classification methods
- kNN (k nearest neighbors; more later)
- Linear Least Squares Fit
- Regression method
- Collections
- Reuters-22173
- 92 categories
- 16,000 unique terms
- Ohsumed: subset of medline
- 14,000 categories
- 72,000 unique terms
- Ltc term weighting

Yang&Pedersen: Experiments

- Choose feature set size
- Preprocess collection, discarding non-selected features / words
- Apply term weighting -> feature vector for each document
- Train classifier on training set
- Evaluate classifier on test set

Discussion

- You can eliminate 90% of features for IG, DF, and CHI without decreasing performance.
- In fact, performance increases with fewer features for IG, DF, and CHI.
- Mutual information is very sensitive to small counts.
- IG does best with smallest number of features.
- Document frequency is close to optimal. By far the simplest feature selection method.
- Similar results for LLSF (regression).

Results

Why is selecting common terms a good strategy?

Information Gain vs Mutual Information

- Information gain is similar to MI for random variables
- Independence?
- In contrast, pointwise MI ignores non-occurrence of terms
- E.g., for complete dependence, you get:
- P(AB)/ (P(A)P(B)) = 1/P(A) – larger for rare terms than for frequent terms
- Yang&Pedersen: Pointwise MI favors rare terms

Feature Selection:Other Considerations

- Generic vs Class-Specific
- Completely generic (class-independent)
- Separate feature set for each class
- Mixed (a la Yang&Pedersen)
- Maintainability over time
- Is aggressive features selection good or bad for robustness over time?
- Ideal: Optimal features selected as part of training

Yang&Pedersen: Limitations

- Don’t look at class specific feature selection
- Don’t look at methods that can’t handle high-dimensional spaces
- Evaluate category ranking (as opposed to classification accuracy)

Feature Selection: Other Methods

- Stepwise term selection
- Forward
- Backward
- Expensive: need to do n^2 iterations of training
- Term clustering
- Dimension reduction: PCA / SVD

FEATURES IN SPAM ASSASSIN

- SpamAssassin is a spam filtering program written in Perl, developed by Justin Mason in 1997 and entirely rewritten by Mark Jeftovic in 2001 when it went on Sourceforge – now maintained by the Apache foundation
- It uses Naïve Bayes and a variety of other methods
- It uses features coming both from content and from other properties of the email message

SpamAssassin Features

100 From: address is in the user's black-list

4.0 Sender is on www.habeas.com Habeas Infringer List

3.994 Invalid Date: header (timezone does not exist)

3.970 Written in an undesired language

3.910 Listed in Razor2, see http://razor.sf.net/

3.801 Subject is full of 8-bit characters

3.472 Claims compliance with Senate Bill 1618

3.437 exists:X-Precedence-Ref

3.371 Reverses Aging

3.350 Claims you can be removed from the list

3.284 'Hidden' assets

3.283 Claims to honor removal requests

3.261 Contains "Stop Snoring"

3.251 Received: contains a name with a faked IP-address

3.250 Received via a relay in list.dsbl.org

3.200 Character set indicates a foreign language

SpamAssassin Features

3.198 Forged eudoramail.com 'Received:' header found

3.193 Free Investment

3.180 Received via SBLed relay, seehttp://www.spamhaus.org/sbl/

3.140 Character set doesn't exist

3.123 Dig up Dirt on Friends

3.090 No MX records for the From: domain

3.072 X-Mailer contains malformed Outlook Expressversion

3.044 Stock Disclaimer Statement

3.009 Apparently, NOT Multi Level Marketing

3.005 Bulk email software fingerprint (jpfree) found inheaders

2.991 exists:Complain-To

2.975 Bulk email software fingerprint (VC_IPA) found inheaders

2.968 Invalid Date: year begins with zero

2.932 Mentions Spam law "H.R. 3113"

2.900 Received forged, contains fake AOL relays

2.879 Asks for credit card details

Measuring Performance

- Classification accuracy: What % of messages were classified correctly?
- Is this what we care about?

- Which system do you prefer?

Measuring Performance

- Precision = good messages kept all messages kept
- Recall =good messages kept all good messages

Trade off precision vs. recall by setting threshold

Measure the curve on annotated dev data (or test data)

Choose a threshold where user is comfortable

F-measure = 1 / (average(1/precision, 1/recall))

OK for search engines (maybe)

would prefer to be here!

point where

precision=recall

(sometimes reported)

OK for spam filtering and legal search

Measuring Performancehigh threshold:

all we keep is good,

but we don’t keep much

low threshold:

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

600.465 - Intro to NLP - J. Eisner

Micro- vs. Macro-Averaging

- If we have more than one class, how do we combine multiple performance measures into one quantity?
- Macroaveraging: Compute performance for each class, then average.
- Microaveraging: Collect decisions for all classes, compute contingency table, evaluate.

Micro- vs. Macro-Averaging: Example

Class 1

Class 2

Micro.Av. Table

- Macroaveraged precision: (0.5 + 0.9)/2 = 0.7
- Microaveraged precision: 100/120 = .83
- Why this difference?

Confusion matrix

- Function of classifier, topics and test docs.
- For a perfect classifier, all off-diagonal entries should be zero.
- For a perfect classifier, if there are n docs in category j than entry (j,j) should be n.
- Straightforward when there is 1 category per document.
- Can be extended to n categories per document.

Decision Trees

- Tree with internal nodes labeled by terms
- Branches are labeled by tests on the weight that the term has
- Leaves are labeled by categories
- Classifier categorizes documentby descending tree following tests to leaf
- The label of the leaf node is then assigned to the document
- Most decision trees are binary trees (never disadvantageous; may require extra internal nodes)

P(class) = .9

P(class) = .2

Decision Tree Learning- Learn a sequence of tests on features, typically using top-down, greedy search
- At each stage choose unused feature with highest Information Gain (as in Lecture 5)
- Binary (yes/no) or continuous decisions

f1

!f1

f7

!f7

Top-down DT induction

- Partition training examples into good “splits”, based on values of a single “good” feature:

(1) Sat, hot, no, casual, keys -> +

(2) Mon, cold, snow, casual, no-keys -> -

(3) Tue, hot, no, casual, no-keys -> -

(4) Tue, cold, rain, casual, no-keys -> -

(5) Wed, hot, rain, casual, keys -> +

Top-down DT induction

- Partition training examples into good “splits”, based on values of a single “good” feature

(1) Sat, hot, no, casual -> +

(2) Mon, cold, snow, casual -> -

(3) Tue, hot, no, casual -> -

(4) Tue, cold, rain, casual -> -

(5) Wed, hot, rain, casual -> +

- No acceptable classification: proceed recursively

Top-down DT induction: divide and conquer algorithm

- Pick a feature
- Split your examples into subsets based on the values of the feature
- For each subsets, examine the examples:
- Zero: assign the most popular class for the parent
- All from the same class: assign this class
- Otherwise, process recursively

Top-Down DT induction

Different trees can be built for the same data, depending on the order of features:

t?

cold

hot

Walk: 2,4

day?

Mo, Thu, Fr, Su

Sat

Wed

Tue

Drive: 1

Walk: 3

Drive: 5

?

Drive

Top-down DT induction

Different trees can be built for the same data, depending on the order of features:

clothing

halloween

casual

t?

walk:?

cold

hot

Walk: 2,4

day?

Mo

Sat

Wed

Tue

Drive: 1

Walk: 3

Drive: 5

Drive:?

Selecting features

- Intuitively
- We want more “informative” features to be higher in the tree:
- Is it Monday? Is it raining? Good political news? No halloween cloths? Hat on? Coat on? Car keys? Yes?? -> Driving! (doesn't look as a good learning job)
- We want a nice compact tree..

Selecting features-2

- Formally
- Define “tree size” (number of nodes, leaves; depth,..)
- Try all the possible trees, find the smallest one
- NP-hard
- Top-down DT induction – greedy search, depends on heuristics for feature ordering (=> no guarantee)
- Information gain

Entropy

Information theory: entropy – number of bits needed to encode some information.

S – set of N examples: p*N positive (“Walk”) and q*N negative (“Drive”)

Entropy(S)= -p*lg(p) – q*lg(q)

p=1, q=0 => Entropy(S)=0

p=1/2, q=1/2 => Entropy(S)=1

Entropy and Decision Trees

keys?

E(S)=-0.6*lg(0.6)-0.4*lg(0.4)=

0.97

no

yes

Walk: 2,4

Drive: 1,3,5

E(Sno)=0

E(Skeys)=0

Entropy and Decision Trees

t?

E(S)=-0.6*lg(0.6)-0.4*lg(0.4)=

0.97

cold

hot

Walk: 2,4

Drive: 1,5

Walk: 3

E(Scold)=0

E(Shot)=-0.33*lg(0.33)-0.66*lg(0.66)=

0.92

Information gain

- For each feature f, compute the reduction in entropy on the split:

Gain(S,f)=E(S)-∑(Entropy(Si)*|Si|/|S|)

f=keys? : Gain(S,f)=0.97

f=t?: Gain(S,f)=0.97-0*2/5-0.92*3/5=0.42

f=clothing?: Gain(S,f)= ?

TEXT CATEGORIZATION WITH DT

- Build a separate decision tree for each category
- Use WORDS COUNTS as features

Reuters Data Set (21578 - ModApte split)

- 9603 training, 3299 test articles; ave. 200 words
- 118 categories
- An article can be in more than one category
- Learn 118 binary category distinctions

- Earn (2877, 1087)
- Acquisitions (1650, 179)
- Money-fx (538, 179)
- Grain (433, 149)
- Crude (389, 189)

Common categories

(#train, #test)

- Trade (369,119)
- Interest (347, 131)
- Ship (197, 89)
- Wheat (212, 71)
- Corn (182, 56)

AN EXAMPLE OF REUTERS TEXT

Foundations of Statistical Natural Language Processing, Manning and Schuetze

Decision Tree for Reuter classification

Foundations of Statistical Natural Language Processing, Manning and Schuetze

Conquer-and-divide with Information gain

- Batch learning (read all the input data, compute information gain based on all the examples simultaneously)
- Greedy search, may find local optima
- Outputs a single solution
- Optimizes depth

Complexity

- Worst case: build a complete tree
- Compute gains on all nodes: at level i, we have already examined i features; m-i remaining.
- In practice: tree is rarely complete, linear on number of features, number of examples (== very fast)

Overfitting

- Suppose we build a very complex tree.. Is it good?
- Last lecture: we measure the quality (“goodness”) of the prediction, not the performance on the training data
- Why can complex trees yield mistakes:
- Noise in the data
- Even without noise, solutions at the last levels are based on too few observations

Overfitting

Mo: Walk (50 observations), Drive (5)

Tue: Walk (40), Drive (3)

We: Drive (1)

Thu: Walk (42), Drive (14)

Fri: Walk (50)

Sa: Drive (20), Walk (20)

Su: Drive (10)

- Can we conclude that “We->Drive”?

Overfitting

- A hypothesis H is said to overfit the training data if there exist another hypothesis H' such that:

Error(H, train data) <= Error (H', train data)

Error(H, unseen data) > Error (H', unseen data)

- Overfitting is related to hypothesis complexity: a more complex hypothesis (e.g., a larger decision tree) overfits more

Overfitting Prevention for DT: Pruning

- “prune” a complex tree: produce a smaller tree that is less accurate on the training data

Original tree: ...Mo: hot->drive (2), cold -> walk (100)

Pruned tree: .. Mo-> walk (100/2)

- post-/pre- pruning

Pruning criteria

- Cross-validation
- Reserve some training data to evaluate the utility of the subtrees
- Statistical tests: use a test to determine whether observations at given level can be random
- MDL (minimum description length): compare the added complexity against memorizing exceptions

DT: issues

- Splitting criteria
- Information gain: split at features with many values
- Non-discrete features
- Non-discrete outputs (“regression trees”)
- Costs
- Missing values
- Incremental learning
- Memory issues

REFERENCES

- Tom Mitchell, Machine Learning. McGraw-Hill, 1997.
- Chapter 6, Bayesian Learning - 6.1, 6.2, 6.9, 6.10
- Chapter 3, Decision Trees – whole chapter
- FabrizioSebastiani. Machine Learning in Automated Text Categorization. ACM Computing Surveys, 34(1):1-47, 2002

ACKNOWLEDGMENTS

- Many slides borrowed from Chris Manning, Stanford

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