(c) 2003, the university of michigan1 information retrieval handout #2 january 14, 2005

(C) 2003, The University of Michigan 1

Information Retrieval

Handout #2

January 14, 2005


Course Information

• Instructor: Dragomir R. Radev ([email protected])

• Office: 3080, West Hall Connector

• Phone: (734) 615-5225

• Office hours: M 11-12 & Th 12-1 or via email

• Course page: http://tangra.si.umich.edu/~radev/650/

• Class meets on Fridays, 2:10-4:55 PM in 409 West Hall


Evaluation


Relevance

• Difficult to change: fuzzy, inconsistent

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


Contingency table

w x

y z

n2 = w + y

n1 = w + x

N

relevant

not relevant

retrieved not retrieved


Precision and Recall

Recall:

Precision:

w

w+y

w+x

w


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 Melville”, +”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.


n Doc. no Relevant? Recall Precision1 588 x 0.2 1.00

2 589 x 0.4 1.00

3 576 0.4 0.67

4 590 x 0.6 0.75

5 986 0.6 0.60

6 592 x 0.8 0.67

7 984 0.8 0.57

8 988 0.8 0.50

9 578 0.8 0.44

10 985 0.8 0.40

11 103 0.8 0.36

12 591 0.8 0.33

13 772 x 1.0 0.38

14 990 1.0 0.36

[From Salton’s book]


P/R graph

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Recall

Pre

cis

ion

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

P and R• When do F and F1 report the wrong results?


Kappa

• N: number of items (index i)

• n: number of categories (index j)

• k: number of annotators

)(1

)()(

EP

EPAP

N

i

n

jij k

mkNk

AP1 1

2

1

1

)1(

1)(

2

1

1

)(

Nk

mEP

N

iijn

j


Kappa example (from Manning, Schuetze, Raghavan)

J1+ J1-

J2+ 300 10

J2- 20 70


Kappa (cont’d)

• P(A) = 370/400• P (-) = (10+20+20+70)/800 = 0.2125• P (+) = (10+20+300+300)/800 = 0.7878• P (E) = 0.2125 * 0.2125 + 0.7878 * 0.7878

= 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


Relevance collections

• TREC ad hoc collections, 2-6 GB

• TREC Web collections, 2-100GB

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>

LA031689-0177FT922-1008LA090190-0126LA101190-0218LA082690-0158LA112590-0109FT944-136LA020590-0119FT944-5300LA052190-0048LA051689-0139FT944-9371LA032390-0172

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

<DOCNO> LA031689-0177 </DOCNO><DOCID> 31701 </DOCID><DATE>March 16, 1989, Thursday, Home Edition </DATE><SECTION>Business; Part 4; Page 1; Column 5; Financial Desk </SECTION><LENGTH>586 words </LENGTH><HEADLINE>AGENCY TO LAUNCH STUDY OF FORD BRONCO II AFTER HIGH RATE OF ROLL-OVER ACCIDENTS </HEADLINE><BYLINE>By LINDA WILLIAMS, Times Staff Writer </BYLINE><TEXT>The federal government's highway safety watchdog said Wednesday that the Ford Bronco II appears to be involved in more fatal roll-overaccidents than other vehicles in its class and that it will seek to determine if the vehicle itself contributes to the accidents. The decision to do an engineering analysis of the Ford Motor Co. utility-sport vehicle grew out of a federal accident study of theSuzuki Samurai, said Tim Hurd, a spokesman for the National Highway Traffic Safety Administration. NHTSA looked at Samurai accidents afterConsumer Reports magazine charged that the vehicle had basic design flaws. Several Fatalities 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 investigationconducted by NHTSA, will cover the 1984-1989 Bronco II models, the agency said. According to a Fatal Accident Reporting System study included in the September report on the Samurai, 43 Bronco II single-vehicleroll-overs caused fatalities, or 19 of every 100,000 vehicles. There were eight Samurai fatal roll-overs, or 6 per 100,000; 13 involvingthe 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 theaccident report, NHTSA declined to investigate the Samurai. ...</TEXT><GRAPHIC> Photo, The Ford Bronco II "appears to have a highernumber of single-vehicle, first event roll-overs," a federal officialsaid. </GRAPHIC><SUBJECT>TRAFFIC ACCIDENTS; FORD MOTOR CORP; NATIONAL HIGHWAY TRAFFIC SAFETY ADMINISTRATION; VEHICLE INSPECTIONS;RECREATIONAL VEHICLES; SUZUKI MOTOR CO; AUTOMOBILE SAFETY </SUBJECT></DOC>


TREC (cont’d)

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

trec.nist.gov/presentations/presentations.html

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

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


Word distribution models


Shakespeare

• Romeo and Juliet:• And, 667; The, 661; I, 570; To, 515; A, 447; Of, 382; My, 356; Is, 343; That, 343; In, 314; You, 289; Thou, 277; Me,

262; Not, 257; With, 234; It, 224; For, 223; This, 215; Be, 207; But, 181; Thy, 167; What, 163; O, 160; As, 156; Her, 150; Will, 147; So, 145; Thee, 139; Love, 135; His, 128; Have, 127; He, 120; Romeo, 115; By, 114; She, 114; Shall, 107; Your, 103; No, 102; Come, 96; Him, 96; All, 92; Do, 89; From, 86; Then, 83; Good, 82; Now, 82; Here, 80; If, 80; An, 78; Go, 76; On, 76; I'll, 71; Death, 69; Night, 68; Are, 67; More, 67; We, 66; At, 65; Man, 65; Or, 65; There, 64; Hath, 63; Which, 60;

• …

• A-bed, 1; A-bleeding, 1; A-weary, 1; Abate, 1; Abbey, 1; Abhorred, 1; Abhors, 1; Aboard, 1; Abound'st, 1; Abroach, 1; Absolved, 1; Abuse, 1; Abused, 1; Abuses, 1; Accents, 1; Access, 1; Accident, 1; Accidents, 1; According, 1; Accursed, 1; Accustom'd, 1; Ache, 1; Aches, 1; Aching, 1; Acknowledge, 1; Acquaint, 1; Acquaintance, 1; Acted, 1; Acting, 1; Action, 1; Acts, 1; Adam, 1; Add, 1; Added, 1; Adding, 1; Addle, 1; Adjacent, 1; Admired, 1; Ado, 1; Advance, 1; Adversary, 1; Adversity's, 1; Advise, 1; Afeard, 1; Affecting, 1; Afflicted, 1; Affliction, 1; Affords, 1; Affray, 1; Affright, 1; Afire, 1; Agate-stone, 1; Agile, 1; Agree, 1; Agrees, 1; Aim'd, 1; Alderman, 1; All-cheering, 1; All-seeing, 1; Alla, 1; Alliance, 1; Alligator, 1; Allow, 1; Ally, 1; Although, 1;

http://www.mta75.org/curriculum/english/Shakes/indexx.html


The BNC (Adam Kilgarriff)• 1 6187267 the det• 2 4239632 be v• 3 3093444 of prep• 4 2687863 and conj• 5 2186369 a det• 6 1924315 in prep• 7 1620850 to infinitive-marker• 8 1375636 have v• 9 1090186 it pron• 10 1039323 to prep• 11 887877 for prep• 12 884599 i pron• 13 760399 that conj• 14 695498 you pron• 15 681255 he pron• 16 680739 on prep• 17 675027 with prep• 18 559596 do v• 19 534162 at prep• 20 517171 by prep

Kilgarriff, A. Putting Frequencies in the Dictionary.International Journal of Lexicography10 (2) 1997. Pp 135--155


Stop lists• 250-300 most common words in English

account for 50% or more of a given text.

• Example: “the” and “of” represent 10% of tokens. “and”, “to”, “a”, and “in” - another 10%. Next 12 words - another 10%.

• Moby Dick Ch.1: 859 unique words (types), 2256 word occurrences (tokens). Top 65 types cover 1132 tokens (> 50%).

• Token/type ratio: 2256/859 = 2.63


Zipf’s law

Rank x Frequency Constant

Rank Term Freq. Z Rank Term Freq. Z

1 the 69,971 0.070 6 in 21,341 0.128

2 of 36,411 0.073 7 that 10,595 0.074

3 and 28,852 0.086 8 is 10,099 0.081

4 to 26.149 0.104 9 was 9,816 0.088

5 a 23,237 0.116 10 he 9,543 0.095


Zipf's law is fairly general!

• Frequency of accesses to web pages • in particular the access counts on the Wikipedia page,with s approximately equal to 0.3 • page access counts on Polish Wikipedia (data for late July 2003) approximately obey Zipf's law with s about 0.5

• Words in the English language • for instance, in Shakespeare’s play Hamlet with s approximately 0.5

• Sizes of settlements• Income distributions amongst individuals • Size of earthquakes• Notes in musical performances

http://en.wikipedia.org/wiki/Zipf's_law


Zipf’s law (cont’d)

• Limitations:– Low and high frequencies– Lack of convergence

• Power law with coefficient c = -1– Y=kxc

• Li (1992) – typing words one letter at a time, including spaces


Heap’s law• Size of vocabulary: V(n) = Kn

• In English, K is between 10 and 100, β is between 0.4 and 0.6.

n

V(n)

http://en.wikipedia.org/wiki/Heaps%27_law


Heap’s law (cont’d)

• Related to Zipf’s law: generative models

• Zipf’s and Heap’s law coefficients change with language

Alexander Gelbukh, Grigori Sidorov. Zipf and Heaps Laws’ Coefficients Depend on Language. Proc.CICLing-2001, Conference on Intelligent Text Processing and Computational Linguistics, February 18–24, 2001, Mexico City. Lecture Notes in Computer Science N 2004, ISSN 0302-9743, ISBN 3-540-41687-0, Springer-Verlag, pp. 332–335.

http://www.cic.ipn.mx/%7Egelbukh

http://www.cic.ipn.mx/%7Egelbukh

http://www.cic.ipn.mx/%7Esidorov




Indexing


Methods

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

• Automatic


LOC subject headings

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

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_a.pdf

http://www.loc.gov/catdir/cpso/lcco/lcco_b.pdf

http://www.loc.gov/catdir/cpso/lcco/lcco_c.pdf

http://www.loc.gov/catdir/cpso/lcco/lcco_d.pdf

http://www.loc.gov/catdir/cpso/lcco/lcco_ef.pdf

http://www.loc.gov/catdir/cpso/lcco/lcco_ef.pdf

http://www.loc.gov/catdir/cpso/lcco/lcco_g.pdf

http://www.loc.gov/catdir/cpso/lcco/lcco_h.pdf

http://www.loc.gov/catdir/cpso/lcco/lcco_j.pdf

http://www.loc.gov/catdir/cpso/lcco/lcco_k.pdf

http://www.loc.gov/catdir/cpso/lcco/lcco_l.pdf

http://www.loc.gov/catdir/cpso/lcco/lcco_m.pdf

http://www.loc.gov/catdir/cpso/lcco/lcco_n.pdf

http://www.loc.gov/catdir/cpso/lcco/lcco_p.pdf

http://www.loc.gov/catdir/cpso/lcco/lcco_q.pdf

http://www.loc.gov/catdir/cpso/lcco/lcco_r.pdf

http://www.loc.gov/catdir/cpso/lcco/lcco_s.pdf


MedicineCLASS R - MEDICINESubclass RR5-920 Medicine (General)R5-130.5 General worksR131-687 History of medicine. Medical expeditionsR690-697 Medicine as a profession. PhysiciansR702-703 Medicine and the humanities. Medicine and disease in

relation to history, literature, etc.R711-713.97 DirectoriesR722-722.32 Missionary medicine. Medical missionariesR723-726 Medical philosophy. Medical ethicsR726.5-726.8 Medicine and disease in relation to psychology.

Terminal care. DyingR727-727.5 Medical personnel and the public. Physician and the

publicR728-733 Practice of medicine. Medical practice economicsR735-854 Medical education. Medical schools. ResearchR855-855.5 Medical technologyR856-857 Biomedical engineering. Electronics. InstrumentationR858-859.7 Computer applications to medicine. Medical informaticsR864 Medical recordsR895-920 Medical physics. Medical radiology. Nuclear medicine


Finding the most frequent terms in a document

• Typically stop words: the, and, in

• Not content-bearing

• Terms vs. words

• Luhn’s method


Luhn’s method

WORDS

FREQUENCY

E


Computing term salience

• Term frequency (IDF)

• Document frequency (DF)

• Inverse document frequency (IDF)

N

wDFwIDF

)(log)(


Applications of TFIDF

• Cosine similarity

• Indexing

• Clustering


Variants of TF*IDF

• E.g., Okapi (Robertson)

• TF/(k+TF)

• k is from 1 to 2


Vector-based matching

• The cosine measure

sim (D,C) =

(dk . ck . idf(k))

(dk)2 . (ck)2

k

k

k


IDF: Inverse document frequency

N: number of documentsdk: number of documents containing term kfik: absolute frequency of term k in document iwik: weight of term k in document i

idfk = log2(N/dk) + 1 = log2N - log2dk + 1

TF * IDF is used for automated indexing and for topicdiscrimination:


Asian and European news

622.941 deng306.835 china196.725 beijing153.608 chinese152.113 xiaoping124.591 jiang108.777 communist102.894 body 85.173 party 71.898 died 68.820 leader 43.402 state 38.166 people

97.487 nato92.151 albright74.652 belgrade46.657 enlargement34.778 alliance34.778 french33.803 opposition32.571 russia14.095 government 9.389 told 9.154 would 8.459 their 6.059 which


Other topics

120.385 shuttle 99.487 space 90.128 telescope 70.224 hubble 59.992 rocket 50.160 astronauts 49.722 discovery 47.782 canaveral 47.782 cape 40.889 mission 35.778 florida 27.063 center

74.652 compuserve65.321 massey55.989 salizzoni29.996 bob27.994 online27.198 executive15.890 interim15.271 chief11.647 service11.174 second 6.781 world 6.315 president


Software

• KEA: http://www.nzdl.org/Kea/• Example:

– Paper: “Protocols for secure, atomic transaction execution in electronic commerce”

– Author: anonymity, atomicity, auction, electronic commerce, privacy, real-time, security, transaction

– Kea: atomicity, auction, customer, electronic commerce, intruder, merchant, protocol, security, third party, transaction

(c) 2003, the university of michigan1 information retrieval handout #2 january 14, 2005

Documents

precision p

number of crashes

vehicles crashworthiness

describecompare vehicles

passenger vehicles

average precisionaverage

number of fatalities

harmonic mean of p