Slide 1 EE3J2 Data Mining EE3J2 Data Mining Lecture 7 Topic Spotting & Query Expansion Martin Russell.

EE3J2 Data MiningSlide 1

EE3J2 Data Mining

Lecture 7Topic Spotting & Query Expansion

Martin Russell


Objectives

To introduce Topic Spotting– Salience and Usefulness

– Example: The AT&T “How May I Help You?” system

Text retrieval revisited– Query expansion

– Synonyms & hyponyms

– WordNet


Topic Spotting

Type of dedicated IR system– Always looking for the same type of documents

– Documents about a particular topic

– Corpus from which data is retrieved is dynamic

Examples– Detect all weather forecasts in BBC radio 4 broadcasts

– Find all documents written by Charlotte Bronte

– …


Topic Spotting Queries

Examples rather than explicit queries ‘Find more like this’ May have substantial quantity of example data But why is Topic Spotting different to IR? Can exploit large amount of query data Calculate better measures of the usefulness of a term than simple IDF


IDF Revisited

Recall the definition of IDF

IDF is concerned only about whether or not a term is contained in a document

But there is also information in the number of times the term occurs in documents

tND

NDtIDF log


Salience

Suppose we have a set of ‘training’ documents– Some ‘on topic’

– Some ‘off topic’

Then, for a term t, we can estimate the probabilities:– P(t | Topic)

– P(t | Not Topic)

– P(t)


‘Salience’ and ‘Usefulness’

Given a term t and a topic T, define the salience of t (relative to T) by:

Similarly, the usefulness of t (relative to T) is given by:

TP

tTPtTPtS

|log|

tP

TtPTtPtU

|log|


Bayes’ Theorem

Remember Bayes’ Theorem?

)(

)()|(|

tp

TPTtptTP


Salience and Usefulness

tUtp

TP

tp

TtpTtp

tp

TP

tpTP

TPTtp

tp

TPTtp

TP

tTPtTPtS

|log|

|log

|

|log|



Now, T is the topic, so P(T) is fixed. Therefore

tUtp

TPtS

tUtp

tS1



So, main difference between Salience and Usefulness is that to have high usefulness, a term must occur frequently

tUtp

tS1


Example

A term w occurs:– t1 times in documents about topic T

– t2 times in documents which are not about topic T

Total number of terms:– in documents about topic T is N1

– in documents not about topic T is N2

Then: P(w|T) = t1/N1 , P(w) = (t1+t2)/(N1+N2)– So

21

21

1

1

1

1 log

NNtt

Nt

N

twU


Example A term w occurs:

– 150 times in documents about topic T

– 230 times in documents which are not about topic T

Total number of terms:– in documents about topic T is 12,500

– in documents not about topic T is 23,100

So:– p(w|T)=0.012, p(w)=0.0107, log(p(w|T)/p(w)) = 0.051

– U(w) = 0.00054


Example (continued)

P(T) =NT /N , where NT and N are the total number of documents about topic T and the total number of documents, respectively

So, if, say, 1 document in 100 is about the topic, then P(T) = 0.01

Then S(w) = (P(T)/p(w))*U(w)

= (0.01/0.0107)*U(w)


Topic spotter

Topic Spotter

Data stream


Example

The AT&T “How May I Help You?” system Task: to understand what AT&T customers say to

operators Look HMIHY? Up on the web


AT&T How May I Help You?

Speech Recognition

Language Processing

Salient word list

Service 1

Service 2

Service 3

Service 15


AT&T How May I Help You? HMIHY? Treats telephone network services as topics

or documents, to be detected or retrieved Example salient words:

Word Salience Word SalienceDifference 4.04 Dialled 1.29

Cost 3.39 Area 1.28Rate 3.37 Time 1.23Much 3.24 Person 1.23

Emergency 2.23 Charge 1.22Misdialed 1.43 Home 1.13Wrong 1.37 Information 1.11code 1.36 credit 1.11

Allen Gorin, “Processing of semantic information in fluent spoken language, Proc. ICSLP 1996


HMIHY Demonstrations.

See http://www.research.att.com/~algor/hmihy/samples.html


Query Processing

Remember how we previously processed a query: Example:

– “I need information on distance running” Stop word removal

– information, distance, running Stemming

– information, distance, run But what about:

– “The London marathon will take place…”


Synonyms, hyponyms, hypernyms & antonyms We know there is a relationship between

– run, distance, and

– marathon

We know that a ‘marathon’ is a ‘long distance run’ Words with the same meaning are synonyms If a query q contains a word w1 and w2 is a synonym

of w1, then w2 should be added to q

This is an example of query expansion


Thesaurus

A thesaurus is a ‘dictionary’ of synonyms and semantically related words and phrases

E.G: Roget’s Thesaurus Example:

physiciansyn: || croaker, doc, doctor, MD, medical, mediciner, medico ||rel: medic, general practitioner, surgeon


Hyponyms

Not only synonyms are useful for query expansion Query q = “Tell me about England” Document d = “A visit to London should be on

everyone’s itinerary” ‘London’ is a hyponym of ‘England’ Hyponym ~ subordinate ~ subset If a query q contains a word w1 and w2 is a hyponym

of w1, then w2 should be added to q


Hypernyms

Hypernyms are also useful for query expansion Query q = “Tell me about England” Document d = “Places to visit in the British Isles” ‘British Isles’ is a hypernym of ‘England’ Hyponym ~ generalisation ~ superset If a query q contains a word w1 and w2 is a

hypernym of w1, then w2 should be added to q


Antonyms

An antonym is a word which is opposite in meaning to another (e.g. bad and good)

The occurrence of an antonym can also be relevant


WordNet

Online lexical database for the English Language http://www.cogsci.princeton.edu/~wn

Category Forms Meanings (syn sets)

Nouns 57,000 48,800

Adjectives 19,500 10,000

Verbs 21,000 8,400

See Belew, chapter 6


WordNet

WordNet is organised as a set of hierarchical trees For nouns, there are 25 trees Children of a node correspond to hyponyms Words become more specific as you move deeper

into the tree


Noun Categories

act, action, activity natural object

animal, fauna natural phenomenon

artefact person, human being

attribute, property plant, flora

body, corpus possession

cognition, knowledge process

communication quantity, amount

event, happening relation

feeling, emotion shape

food state, condition

group, collection substance

location, place time

motive


Query-document scoring

A query q is expanded to include hyponyms and synonyms

Recall that for a document d

tIDFfw tdtd

qd

ww

dqSim dqttqtd

,


Query expansion

Suppose:– t is the original term in the query,

– t’ is a synonym or hyponym of t which occurs in d

Then we could define:

Where tt’ is a weighting depending on how ‘far’ t and t’ are apart according to WordNet

tIDFfw dtttdt ''' 10 ' tt


Summary

Topic spotting:– Salience and usefulness

– How May I Help You?

Query expansion WordNet