AI: Natural Language Processing

AI:

Natural Language Processing

Dr. Muhammad Humayoun Assistant Professor

University of Central Punjab, Lahore.

[email protected]

Slides of Prof. Dr. Dan Jurafsky and Prof. Dr. Chris Manning, Stanford

Text Classification

and Naïve Bayes

The Task of Text

Classification

Dan Jurafsky

Dan Jurafsky

Authorship attribution

Who wrote which Federalist papers?

to ratify U.S Constitution: Jay, Madison, Hamilton.

• Authorship of 12 of the letters in dispute

• 1963: solved by Mosteller and Wallace using Bayesian methods

James Madison Alexander Hamilton Later gave rise to

Dan Jurafsky

Gender Identification

Male

or

female

author?

1. By 1925 present-day Vietnam was divided into three parts under French colonial rule. The southern region embracing Saigon and the Mekong delta was the colony of Cochin-China;

the central area with its imperial capital at Hue was the

protectorate of Annam…

2. Clara never failed to be astonished by the extraordinary felicity of her own name. She found it hard to trust herself to the

mercy of fate, which had managed over the years to convert

her greatest shame into one of her greatest assets…

S. Argamon, M. Koppel, J. Fine, A. R. Shimoni, 2003. “Gender, Genre, and Writing Style in Formal Written Texts,” Text, volume 23, number 3, pp. 321–346

More use of facts and determinants

Dan Jurafsky

Sentiment Analysis

Positive or negative movie review?

• unbelievably disappointing

• Full of zany characters and richly applied satire, and some great plot twists

• this is the greatest screwball comedy ever filmed

• It was pathetic. The worst part about it was the boxing scenes.

6

Very important commercial application

Dan Jurafsky

Topic Extraction

What is the subject of this article?

• Antagonists and Inhibitors

• Blood Supply • Chemistry • Drug Therapy • Embryology • Epidemiology • … 7

MeSH Subject Category Hierarchy

?

Dan Jurafsky

Text Classification Tasks

•

Assigning subject categories, topics, or genres

•

Spam detection

•

Authorship identification

•

Age/gender identification

•

Language Identification

•

Sentiment analysis

•

…

Dan Jurafsky

Text Classification: definition

•

Input:

•

a document

d

•

a fixed set of classes

C = {c

, c

,…, c

}

Dan Jurafsky

Classification Methods:

Hand-coded rules

• Rules based on combinations of words or other features

• spam: black-list-address OR (“dollars” AND “have been selected”)

• Accuracy can be high

• If rules carefully refined by expert

• But building and maintaining these rules is expensive • These rules may be used as a part of a larger systems

Dan Jurafsky

Classification Methods:

Supervised Machine Learning

•

Illustrative Example

Another example

Movie review data example

From text to vectors

14

Target Label Y Features

Target Label Feature

vector

• We train function f on

particular known inputs X_m. • We may test them for

unknown inputs X for which we want to classify or predict etc.

Supervised Learning

h is a function

Dan Jurafsky

A training set of

m

•

Dan Jurafsky

Classification Methods:

Supervised Machine Learning

•

Any kind of classifier

• Naïve Bayes

• Logistic regression

• Support-vector machines (SVM) • k-Nearest Neighbors

Text Classification

and Naïve Bayes

Dan Jurafsky

Naïve Bayes Intuition

•

Simple (“naïve”) classification method based on

Bayes rule

•

Relies on very simple representation of document

•

Bag of words

Dan Jurafsky

The bag of words representation

I love this movie! It's sweet, but with satirical humor. The dialogue is great and the

adventure scenes are fun… It manages to be whimsical and

romantic while laughing at the conventions of the fairy tale genre. I would recommend it to just about anyone. I've seen it several times, and I'm

always happy to see it again whenever I have a friend who hasn't seen it yet.

Dan Jurafsky

The bag of words representation

I love this movie! It's sweet, but with satirical humor. The dialogue is great and the

adventure scenes are fun… It manages to be whimsical and

romantic while laughing at the conventions of the fairy tale genre. I would recommend it to just about anyone. I've seen it several times, and I'm

always happy to see it again

whenever I have a friend who hasn't seen it yet.

Dan Jurafsky

The bag of words representation:

using a subset of words

x love xxxxxxxxxxxxxxxx sweet xxxxxxx satirical xxxxxxxxxx xxxxxxxxxxx great xxxxxxx xxxxxxxxxxxxxxxxxxx fun xxxx xxxxxxxxxxxxx whimsical xxxx romantic xxxx laughing xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxx recommend xxxxx xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xx several xxxxxxxxxxxxxxxxx xxxxx happy xxxxxxxxx again xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxx

γ(

_)=c

Dan Jurafsky

The bag of words representation

γ(

_)=c

great

2

love

2

recommend

1

laugh

1

happy

1

...

...

All we care about are counts

Vector of words and corresponding counts

Dan Jurafsky Planning GUI Garbage Collection Machine Learning NLP parser tag training translation language... learning training algorithm shrinkage network... garbage collection memory optimization region... Test document parser language label translation …

Bag of words for document classification

More than two classes

... planning temporal reasoning plan language...

?

Text Classification

and Naïve Bayes

Formalizing the Naïve

Bayes Classifier

Dan Jurafsky

Bayes’ Rule Applied to Documents and

Classes

Dan Jurafsky

Naïve Bayes Classifier (I)

MAP is “maximum a posteriori” = most likely class

Bayes Rule

Dropping the denominator

Dan Jurafsky

Dan Jurafsky

Naïve Bayes Classifier (IV)

How often does this class occur?

O(|X|n•|C|) parameters

We can just count the relative frequencies in a corpus

Could only be estimated if a very, very large number of training examples was

Dan Jurafsky

Multinomial Naïve Bayes Independence

Assumptions

•

Bag of Words assumption: Assume position doesn’t

matter

•

Conditional Independence: Assume the feature

Dan Jurafsky

Dan Jurafsky

Applying Multinomial Naive Bayes

Classifiers to Text Classification

positions

← all word positions in text document

Text Classification

and Naïve Bayes

Naïve Bayes:

Learning

Dan Jurafsky

Learning the Multinomial Naïve Bayes Model

•

First attempt: maximum likelihood estimates

•

simply use the frequencies in the data

Dan Jurafsky

• Create mega-document for topic j by concatenating all docs in this topic

• Use frequency of w in mega-document

Parameter estimation

fraction of times word w_i appears among all words in documents of topic c_j

Doc_Pos Doc_Neg

Dan Jurafsky

Problem with Maximum Likelihood

• What if we have seen no training documents with the word

fantastic and classified in the topic positive (thumbs-up)?

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

Dan Jurafsky

Laplace (add-1) smoothing for Naïve Bayes

Dan Jurafsky

Multinomial Naïve Bayes: Learning

• Calculate P(c_j) terms

• For each c_j in C do

docs_j ← all docs with class =c_j

• Calculate P(w_k | c_j) terms

• Text_j ← single doc containing all docs_j

• Foreach word w_k in Vocabulary

n_k ← # of occurrences of w_k in Text_j

Text Classification

and Naïve Bayes

Multinomial Naïve

Bayes: A Worked

Dan Jurafsky Choosing a class: P(c1|d5) P(j1|d5) 1/4 * (2/9)3 * 2/9 * 2/9 ≈ 0.0001

Doc Words Class

Training 1 Chinese Beijing Chinese c1

2 Chinese Chinese Shanghai c1

3 Chinese Macao c1

4 Tokyo Japan Chinese j1

Test 5 Chinese Chinese Chinese Tokyo Japan ?

41 Conditional Probabilities: P(Chinese|c1) = P(Tokyo|c1) = P(Japan|c1) = P(Chinese|j1) = P(Tokyo|j1) = P(Japan|j1) = Priors: P(c1)= P(j1)= 3 4 1 4 (5+1) / (8+6) = 6/14 = 3/7 (0+1) / (8+6) = 1/14 (1+1) / (3+6) = 2/9 (0+1) / (8+6) = 1/14 (1+1) / (3+6) = 2/9 (1+1) / (3+6) = 2/9 3/4 * (3/7)3 * 1/14 * 1/14 ≈ 0.0003

Dan Jurafsky

Naïve Bayes in Spam Filtering

• Mentions Generic Viagra • Online Pharmacy

• Mentions millions of (dollar) ((dollar) NN,NNN,NNN.NN) • Phrase: impress ...

• From: starts with many numbers • Subject is all capitals

• HTML has a low ratio of text to image area • One hundred percent guaranteed

• Claims you can be removed from the list • 'Prestigious Non-Accredited Universities'

Dan Jurafsky

Summary: Naive Bayes is Not So Naive

• Robust to Irrelevant Features

Irrelevant Features cancel each other without affecting results

• Very good in domains with many equally important features

Decision Trees suffer from fragmentation in such cases – especially if little data

• Optimal if the independence assumptions hold: If assumed

independence is correct, then it is the Bayes Optimal Classifier for problem

• A good dependable baseline for text classification

Text Classification

and Naïve Bayes

Precision, Recall, and

the F measure

Dan Jurafsky

The 2-by-2 contingency table

correct not correct Selected(SPAM) tp fp not selected(NS) fn tn Predicted Class by System Actual Class

SPAM Not SPAM

Dan Jurafsky

Not good for Skewed classes

correct not correct

Selected tp fp

not selected Fn = 10 tn = 99,990

Actual Class

Shoe brand Anything else

0 100,000

Dan Jurafsky

Precision and recall

• Precision: % of selected items that are correct

• Recall: % of correct items that are selected

correct not correct

Selected (SPAM) tp fp not selected(NS) fn tn Predicted Class by System Actual Class

Precision(P) Recall (R) Average F₁ Score

Algorithm 1 0.5 0.4 0.45 0.444

Algorithm 2 0.7 0.1 0.4 0.175

Algorithm 3 0.02 1.0 0.51 0.0392 F₁ Score (F score)

How to compare precision/recall numbers?

Average:

F₁ Score:

Text Classification

and Naïve Bayes

Text Classification:

Evaluation

Dan Jurafsky

Development Test Sets and Cross-validation

• Metric: P/R/F1 or Accuracy

• Unseen test set

• avoid overfitting (‘tuning to the test set’) • Independent estimate of performance

• Cross-validation over multiple splits

• Handle sampling errors from different datasets

• Pool results over each split

• Compute pooled dev set performance

Training set Development Test Set Test Set

Test Set Training Set Training Set Dev Test Training Set Dev Test Dev Test

Dan Jurafsky

Cross-Validation

• Break up data into 10 folds

• (Equal positive and negative inside each fold?)

• For each fold

• Choose the fold as a temporary test set

• Train on 9 folds, compute performance on the test fold

• Report average

Dan Jurafsky

END