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Information Retrieval (3). Prof. Dragomir R. Radev [email protected] 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

Information Retrieval(3)

Prof. Dragomir R. Radev

[email protected]


Information retrieval 3

SI650 Winter 2010

5. Evaluation of IR systems

Reference collections

TREC


Relevance

Relevance

  • Difficult to change: fuzzy, inconsistent

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


Contingency table

Contingency table

retrieved

not retrieved

relevant

w=tp

x=fn

n1 = w + x

not relevant

y=fp

z=tn

N

n2 = w + y


Precision and recall

Precision and Recall

w

Recall:

w+x

w

Precision:

w+y


Exercise

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.


Information retrieval 3

[From Salton’s book]


Information retrieval 3

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

Interpolation – what is precision at recall=0.5?


Issues

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

Kappa

  • N: number of items (index i)

  • n: number of categories (index j)

  • k: number of annotators


Kappa example

Kappa example


Kappa cont d

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

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


Information retrieval 3

<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

TREC (cont’d)

  • http://trec.nist.gov/tracks.html

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


Most used reference collections

Most used reference collections

  • Generic retrieval: OHSUMED, CRANFIELD, CACM

  • Text classification: Reuters, 20newsgroups

  • Question answering: TREC-QA

  • Web: DOTGOV, wt100g

  • Blogs: Buzzmetrics datasets

  • TREC ad hoc collections, 2-6 GB

  • TREC Web collections, 2-100GB


Comparing two systems

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

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

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


Information retrieval 3

IR Winter 2010

6. Automated indexing/labeling

Compression


Indexing methods

Indexing methods

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

  • Automatic: e.g., TF*IDF based


Loc subject headings

LOC subject headings

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

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

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

Compression

  • Methods

    • Fixed length codes

    • Huffman coding

    • Ziv-Lempel codes


Fixed length codes

Fixed length codes

  • Binary representations

    • ASCII

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


Variable length codes

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 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

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


Information retrieval 3

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


Exercise1

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

Extensions

  • Word-based

  • Domain/genre dependent models


Ziv lempel coding

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


Information retrieval 3

  • <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


Links on text compression

Links on text compression

  • Data compression:

    • http://www.data-compression.info/

  • Calgary corpus:

    • http://links.uwaterloo.ca/calgary.corpus.html

  • Huffman coding:

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

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

  • LZ

    • http://en.wikipedia.org/wiki/LZ77


100 alternative search engines

100 alternative search engines

  • http://rss.slashdot.org/~r/Slashdot/slashdot/~3/83468703/article.pl


Readings

Readings

  • 2: MRS9

  • 3: MRS13, MRS14

  • 4: MRS15, MRS16


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