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Information Retrieval (3). Prof. Dragomir R. Radev radev@umich.edu. SI650 Winter 2010. … 5. Evaluation of IR systems Reference collections TREC …. Relevance. Difficult to change: fuzzy, inconsistent Methods: exhaustive, sampling, pooling, search-based. Contingency table.

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Information Retrieval (3)

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## Information Retrieval(3)

SI650 Winter 2010

5. Evaluation of IR systems

Reference collections

TREC

### Relevance

• Difficult to change: fuzzy, inconsistent

• Methods: exhaustive, sampling, pooling, search-based

retrieved

not retrieved

relevant

w=tp

x=fn

n1 = w + x

not relevant

y=fp

z=tn

N

n2 = w + y

w

Recall:

w+x

w

Precision:

w+y

### Exercise

Go to Google (www.google.com) and search for documents on Tolkien’s “Lord of the Rings”. Try different ways of phrasing the query: e.g., Tolkien, “JRR Tolkien”, +”JRR Tolkien” +Lord of the Rings”, etc. For each query, compute the precision (P) based on the first 10 documents returned by AltaVista.

Note! Before starting the exercise, have a clear idea of what a relevant document for your query should look like. Try different information needs.

Later, try different queries.

[From Salton’s book]

Interpolated average precision (e.g., 11pt)

Interpolation – what is precision at recall=0.5?

### Issues

• Why not use accuracy A=(w+z)/N?

• Average precision

• Average P at given “document cutoff values”

• Report when P=R

• F measure: F=(b2+1)PR/(b2P+R)

• F1 measure: F1 = 2/(1/R+1/P) : harmonic mean of P and R

### Kappa

• N: number of items (index i)

• n: number of categories (index j)

• k: number of annotators

### Kappa (cont’d)

• P(A) = 370/400 = 0.925

• P (-) = (10+20+70+70)/800 = 0.2125

• P (+) = (10+20+300+300)/800 = 0.7875

• P (E) = 0.2125 * 0.2125 + 0.7875 * 0.7875 = 0.665

• K = (0.925-0.665)/(1-0.665) = 0.776

• Kappa higher than 0.67 is tentatively acceptable; higher than 0.8 is good

### Sample TREC query

<top>

<num> Number: 305

<title> Most Dangerous Vehicles

<desc> Description:

Which are the most crashworthy, and least crashworthy,

passenger vehicles?

<narr> Narrative:

A relevant document will contain information on the crashworthiness of a given vehicle or vehicles that can be used to draw a comparison with other vehicles. The document will have to describe/compare vehicles, not drivers. For instance, it should be expected that vehicles preferred by 16-25 year-olds would be involved in more crashes, because that age group is involved in more crashes. I would view number of fatalities per 100 crashes to be more revealing of a vehicle's crashworthiness than the number of crashes per 100,000 miles, for example.

</top>

LA031689-0177

FT922-1008

LA090190-0126

LA101190-0218

LA082690-0158

LA112590-0109

FT944-136

LA020590-0119

FT944-5300

LA052190-0048

LA051689-0139

FT944-9371

LA032390-0172

LA042790-0172LA021790-0136LA092289-0167LA111189-0013LA120189-0179LA020490-0021LA122989-0063LA091389-0119LA072189-0048FT944-15615LA091589-0101LA021289-0208

<DOCNO> LA031689-0177 </DOCNO>

<DOCID> 31701 </DOCID>

<DATE><P>March 16, 1989, Thursday, Home Edition </P></DATE>

<SECTION><P>Business; Part 4; Page 1; Column 5; Financial Desk </P></SECTION>

<LENGTH><P>586 words </P></LENGTH>

<HEADLINE><P>AGENCY TO LAUNCH STUDY OF FORD BRONCO II AFTER HIGH RATE OF ROLL-OVER ACCIDENTS </P></HEADLINE>

<BYLINE><P>By LINDA WILLIAMS, Times Staff Writer </P></BYLINE>

<TEXT>

<P>The federal government's highway safety watchdog said Wednesday that the Ford Bronco II appears to be involved in more fatal roll-over

accidents than other vehicles in its class and that it will seek to determine if the vehicle itself contributes to the accidents. </P>

<P>The decision to do an engineering analysis of the Ford Motor Co. utility-sport vehicle grew out of a federal accident study of the

Suzuki Samurai, said Tim Hurd, a spokesman for the National Highway Traffic Safety Administration. NHTSA looked at Samurai accidents after

Consumer Reports magazine charged that the vehicle had basic design flaws. </P>

<P>Several Fatalities </P>

<P>However, the accident study showed that the "Ford Bronco II appears to have a higher number of single-vehicle, first event roll-overs,

particularly those involving fatalities," Hurd said. The engineering analysis of the Bronco, the second of three levels of investigation

conducted by NHTSA, will cover the 1984-1989 Bronco II models, the agency said. </P>

<P>According to a Fatal Accident Reporting System study included in the September report on the Samurai, 43 Bronco II single-vehicle

roll-overs caused fatalities, or 19 of every 100,000 vehicles. There were eight Samurai fatal roll-overs, or 6 per 100,000; 13 involving

the Chevrolet S10 Blazers or GMC Jimmy, or 6 per 100,000, and six fatal Jeep Cherokee roll-overs, for 2.5 per 100,000. After the

accident report, NHTSA declined to investigate the Samurai. </P>

...

</TEXT>

<GRAPHIC><P> Photo, The Ford Bronco II "appears to have a higher

number of single-vehicle, first event roll-overs," a federal official

said. </P></GRAPHIC>

<SUBJECT>

<P>TRAFFIC ACCIDENTS; FORD MOTOR CORP; NATIONAL HIGHWAY TRAFFIC SAFETY ADMINISTRATION; VEHICLE INSPECTIONS;

RECREATIONAL VEHICLES; SUZUKI MOTOR CO; AUTOMOBILE SAFETY </P>

</SUBJECT>

</DOC>

### TREC (cont’d)

• http://trec.nist.gov/tracks.html

• http://trec.nist.gov/presentations/presentations.html

### Most used reference collections

• Generic retrieval: OHSUMED, CRANFIELD, CACM

• Text classification: Reuters, 20newsgroups

• Web: DOTGOV, wt100g

• Blogs: Buzzmetrics datasets

• TREC ad hoc collections, 2-6 GB

• TREC Web collections, 2-100GB

### Comparing two systems

• Comparing A and B

• One query?

• Average performance?

• Need: A to consistently outperform B

[this slide: courtesy James Allan]

### The sign test

• Example 1:

• A > B (12 times)

• A = B (25 times)

• A < B (3 times)

• p < 0.035 (significant at the 5% level)

• Example 2:

• A > B (18 times)

• A < B (9 times)

• p < 0.122 (not significant at the 5% level)

• http://www.fon.hum.uva.nl/Service/Statistics/Sign_Test.html

[this slide: courtesy James Allan]

### Other tests

• Student t-test: takes into account the actual performances, not just which system is better

• http://www.fon.hum.uva.nl/Service/Statistics/Student_t_Test.html

• http://www.socialresearchmethods.net/kb/stat_t.php

• Wilcoxon Matched-Pairs Signed-Ranks Test

• http://www.fon.hum.uva.nl/Service/Statistics/Signed_Rank_Test.html

IR Winter 2010

6. Automated indexing/labeling

Compression

### Indexing methods

• Manual: e.g., Library of Congress subject headings, MeSH

• Automatic: e.g., TF*IDF based

A -- GENERAL WORKSB -- PHILOSOPHY. PSYCHOLOGY. RELIGIONC -- AUXILIARY SCIENCES OF HISTORYD -- HISTORY (GENERAL) AND HISTORY OF EUROPEE -- HISTORY: AMERICAF -- HISTORY: AMERICAG -- GEOGRAPHY. ANTHROPOLOGY. RECREATIONH -- SOCIAL SCIENCESJ -- POLITICAL SCIENCEK -- LAWL -- EDUCATIONM -- MUSIC AND BOOKS ON MUSICN -- FINE ARTSP -- LANGUAGE AND LITERATUREQ -- SCIENCER -- MEDICINES -- AGRICULTURET -- TECHNOLOGYU -- MILITARY SCIENCEV -- NAVAL SCIENCEZ -- BIBLIOGRAPHY. LIBRARY SCIENCE. INFORMATION RESOURCES (GENERAL)

http://www.loc.gov/catdir/cpso/lcco/lcco.html

### Medicine

CLASS R - MEDICINE

Subclass R

R5-920Medicine (General)

R5-130.5General works

R131-687History of medicine. Medical expeditions

R690-697Medicine as a profession. Physicians

R702-703Medicine and the humanities. Medicine and disease in relation to history, literature, etc.

R711-713.97Directories

R722-722.32Missionary medicine. Medical missionaries

R723-726Medical philosophy. Medical ethics

R726.5-726.8Medicine and disease in relation to psychology. Terminal care. Dying

R727-727.5Medical personnel and the public. Physician and the public

R728-733Practice of medicine. Medical practice economics

R735-854Medical education. Medical schools. Research

R855-855.5Medical technology

R856-857Biomedical engineering. Electronics. Instrumentation

R858-859.7Computer applications to medicine. Medical informatics

R864Medical records

R895-920Medical physics. Medical radiology. Nuclear medicine

### Automatic methods

• TF*IDF: pick terms with the highest TF*IDF scores

• Centroid-based: pick terms that appear in the centroid with high scores

• The maximal marginal relevance principle (MMR)

• Related to summarization, snippet generation

### Compression

• Methods

• Fixed length codes

• Huffman coding

• Ziv-Lempel codes

### Fixed length codes

• Binary representations

• ASCII

• Representational power (2k symbols where k is the number of bits)

### Variable length codes

• Alphabet:

A .- N -. 0 -----

B -... O --- 1 .----

C -.-. P .--. 2 ..---

D -.. Q --.- 3 ...—

E . R .-.4 ....-

F ..-.S ...5 .....

G --.T - 6 -....

H ....U ..- 7 --...

I .. V ...- 8 ---..

J .--- W .-- 9 ----.

K -.- X -..-

L .-.. Y -.—

M -- Z --..

• Demo:

• http://www.scphillips.com/morse/

### Most frequent letters in English

• Most frequent letters:

• E T A O I N S H R D L U

• Demo:

• http://www.amstat.org/publications/jse/secure/v7n2/count-char.cfm

• Also: bigrams:

• TH HE IN ER AN RE ND AT ON NT

### Huffman coding

• Developed by David Huffman (1952)

• Average of 5 bits per character (37.5% compression)

• Based on frequency distributions of symbols

• Algorithm: iteratively build a tree of symbols starting with the two least frequent symbols

0

1

0

1

1

0

g

0

1

0

1

0

1

i

j

f

c

0

1

0

1

b

d

a

0

1

e

h

### Exercise

• Consider the bit string: 01101101111000100110001110100111000110101101011101

• Use the Huffman code from the example to decode it.

• Try inserting, deleting, and switching some bits at random locations and try decoding.

### Extensions

• Word-based

• Domain/genre dependent models

### Ziv-Lempel coding

• Two types - one is known as LZ77 (used in GZIP)

• Code: set of triples <a,b,c>

• a: how far back in the decoded text to look for the upcoming text segment

• b: how many characters to copy

• c: new character to add to complete segment

• <0,0,p>p

• <0,0,e>pe

• <0,0,t>pet

• <2,1,r>peter

• <0,0,_>peter_

• <6,1,i>peter_pi

• <8,2,r>peter_piper

• <6,3,c>peter_piper_pic

• <0,0,k>peter_piper_pick

• <7,1,d>peter_piper_picked

• <7,1,a>peter_piper_picked_a

• <9,2,e>peter_piper_picked_a_pe

• <9,2,_>peter_piper_picked_a_peck_

• <0,0,o>peter_piper_picked_a_peck_o

• <0,0,f>peter_piper_picked_a_peck_of

• <17,5,l>peter_piper_picked_a_peck_of_pickl

• <12,1,d>peter_piper_picked_a_peck_of_pickled

• <16,3,p>peter_piper_picked_a_peck_of_pickled_pep

• <3,2,r>peter_piper_picked_a_peck_of_pickled_pepper

• <0,0,s>peter_piper_picked_a_peck_of_pickled_peppers

• Data compression:

• http://www.data-compression.info/

• Calgary corpus:

• Huffman coding:

• http://www.compressconsult.com/huffman/

• http://en.wikipedia.org/wiki/Huffman_coding

• LZ

• http://en.wikipedia.org/wiki/LZ77