11

Modern Information Modern Information RetrievalRetrieval

Chapter 3. EvaluationChapter 3. Evaluation

22

Why System Evaluation?Why System Evaluation?

• There are many retrieval models/ There are many retrieval models/ algorithms/ systems, which one is the best?algorithms/ systems, which one is the best?

• What is the best component for:What is the best component for:– Ranking function (dot-product, cosine, …)Ranking function (dot-product, cosine, …)– Term selection (stopword removal, stemming…)Term selection (stopword removal, stemming…)– Term weighting (TF, TF-IDF,…)Term weighting (TF, TF-IDF,…)

• How far down the ranked list will a user How far down the ranked list will a user need to look to find some/all relevant need to look to find some/all relevant documents?documents?

33

Difficulties in Evaluating IR Difficulties in Evaluating IR SystemsSystems

• Effectiveness is related to the Effectiveness is related to the relevancyrelevancy of of retrieved items.retrieved items.

• Relevancy is not typically binary but continuous.Relevancy is not typically binary but continuous.• Even if relevancy is binary, it can be a difficult Even if relevancy is binary, it can be a difficult

judgment to make.judgment to make.• Relevancy, from a human standpoint, is:Relevancy, from a human standpoint, is:

– Subjective: Depends upon a specific user’s judgment.Subjective: Depends upon a specific user’s judgment.– Situational: Relates to user’s current needs.Situational: Relates to user’s current needs.– Cognitive: Depends on human perception and Cognitive: Depends on human perception and

behavior.behavior.– Dynamic: Changes over time.Dynamic: Changes over time.

44

Human Labeled CorporaHuman Labeled Corpora (Gold Standard) (Gold Standard)• Start with a corpus of documents.Start with a corpus of documents.• Collect a set of queries for this corpus.Collect a set of queries for this corpus.• Have one or more human experts Have one or more human experts

exhaustively label the relevant exhaustively label the relevant documents for each query.documents for each query.

• Typically assumes binary relevance Typically assumes binary relevance judgments.judgments.

• Requires considerable human effort for Requires considerable human effort for large document/query corpora.large document/query corpora.

55

documents relevant of number Total

retrieved documents relevant of Number recall

retrieved documents of number Total

retrieved documents relevant of Number precision

Relevant documents

Retrieved documents

Entire document collection

retrieved & relevant

not retrieved but relevant

retrieved & irrelevant

Not retrieved & irrelevant

retrieved not retrieved

rele

vant

irre

leva

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Precision and RecallPrecision and Recall

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Precision and RecallPrecision and Recall• PrecisionPrecision

– The ability to retrieveThe ability to retrieve top-ranked top-ranked documents that are mostly relevant.documents that are mostly relevant.

• RecallRecall– The ability of the search to find The ability of the search to find allall of of

the relevant items in the corpus.the relevant items in the corpus.

77

Determining Recall is Determining Recall is DifficultDifficult

• Total number of relevant items is Total number of relevant items is sometimes not available:sometimes not available:– Sample across the database and Sample across the database and

perform relevance judgment on these perform relevance judgment on these items.items.

– Apply different retrieval algorithms to Apply different retrieval algorithms to the same database for the same query. the same database for the same query. The aggregate of relevant items is taken The aggregate of relevant items is taken as the total relevant set.as the total relevant set.

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Trade-off between Recall Trade-off between Recall and Precisionand Precision

10

1

Recall

Pre

cisi

on

The idealReturns relevant documents butmisses many useful ones too

Returns most relevantdocuments but includes lots of junk

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Computing Recall/Precision Computing Recall/Precision PointsPoints• For a given query, produce the ranked list of For a given query, produce the ranked list of

retrievals.retrievals.

• Adjusting a threshold on this ranked list produces Adjusting a threshold on this ranked list produces different sets of retrieved documents, and different sets of retrieved documents, and therefore different recall/precision measures.therefore different recall/precision measures.

• Mark each document in the ranked list that is Mark each document in the ranked list that is relevant according to the gold standard.relevant according to the gold standard.

• Compute a recall/precision pair for each position Compute a recall/precision pair for each position in the ranked list that contains a relevant in the ranked list that contains a relevant document.document.

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R=3/6=0.5; P=3/4=0.75

Computing Recall/Precision Computing Recall/Precision Points: An ExamplePoints: An Example

n doc # relevant

1 588 x2 589 x3 5764 590 x5 9866 592 x7 9848 9889 57810 98511 10312 59113 772 x14 990

Let total # of relevant docs = 6Check each new recall point:

R=1/6=0.167; P=1/1=1

R=2/6=0.333; P=2/2=1

R=5/6=0.833; p=5/13=0.38

R=4/6=0.667; P=4/6=0.667

Missing one relevant document.

Never reach 100% recall

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Interpolating a Interpolating a Recall/Precision CurveRecall/Precision Curve

• Interpolate a precision value for each Interpolate a precision value for each standard recall levelstandard recall level::– rrjj {0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, {0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8,

0.9, 1.0}0.9, 1.0}

– rr00 = 0.0, r = 0.0, r11 = 0.1, …, r = 0.1, …, r1010=1.0=1.0

• The interpolated precision at the The interpolated precision at the jj-th -th standard recall level is the maximum standard recall level is the maximum known precision at any recall level known precision at any recall level between the between the jj-th and (-th and (j j + 1)-th level:+ 1)-th level:)(max)(

1

rPrPjj rrr

j

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Interpolating a Interpolating a Recall/Precision Curve: An Recall/Precision Curve: An ExampleExample

0.4 0.8

1.0

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Prec

isio

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Average Recall/Precision Average Recall/Precision CurveCurve• Typically average performance over a Typically average performance over a

large large setset of queries. of queries.

• Compute average precision at each Compute average precision at each standard recall level across all queries.standard recall level across all queries.

• Plot average precision/recall curves to Plot average precision/recall curves to evaluate overall system performance evaluate overall system performance on a document/query corpus.on a document/query corpus.

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Compare Two or More Compare Two or More SystemsSystems• The curve closest to the upper right-The curve closest to the upper right-

hand corner of the graph indicates hand corner of the graph indicates the best performancethe best performance

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Recall

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R- PrecisionR- Precision• Precision at the R-th position in the Precision at the R-th position in the

ranking of results for a query that ranking of results for a query that has R relevant documents.has R relevant documents.

n doc # relevant

1 588 x2 589 x3 5764 590 x5 9866 592 x7 9848 9889 57810 98511 10312 59113 772 x14 990

R = # of relevant docs = 6

R-Precision = 4/6 = 0.67

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F-MeasureF-Measure

• One measure of performance that takes One measure of performance that takes into account both recall and precision.into account both recall and precision.

• Harmonic mean of recall and precision:Harmonic mean of recall and precision:

• Compared to arithmetic mean, both need Compared to arithmetic mean, both need to be high for harmonic mean to be high.to be high for harmonic mean to be high.

PRRP

PRF 11

22

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E Measure (parameterized F E Measure (parameterized F Measure)Measure)

• A variant of F measure that allows A variant of F measure that allows weighting emphasis on precision over weighting emphasis on precision over recall:recall:

• Value of Value of controls trade-off: controls trade-off:– = 1: Equally weight precision and recall (E=F).= 1: Equally weight precision and recall (E=F).– > 1: Weight recall more.> 1: Weight recall more.– < 1: Weight precision more.< 1: Weight precision more.

PRRP

PRE

1

2

2

2

2

)1()1(

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Fallout RateFallout Rate

• Problems with both precision and Problems with both precision and recall:recall:– Number of irrelevant documents in the Number of irrelevant documents in the

collection is not taken into account.collection is not taken into account.– Recall is undefined when there is no Recall is undefined when there is no

relevant document in the collection.relevant document in the collection.– Precision is undefined when no Precision is undefined when no

document is retrieved.document is retrieved.

collection the in items tnonrelevan of no. totalretrieved items tnonrelevan of no.

Fallout

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Subjective Relevance Subjective Relevance MeasureMeasure• Novelty RatioNovelty Ratio: The proportion of items retrieved : The proportion of items retrieved

and judged relevant by the user and of which and judged relevant by the user and of which they were previously unaware.they were previously unaware.– Ability to findAbility to find new new information on a topic. information on a topic.

• Coverage RatioCoverage Ratio: The proportion of relevant : The proportion of relevant items retrieved out of the total relevant items retrieved out of the total relevant documents documents knownknown to a user prior to the search. to a user prior to the search.– Relevant when the user wants to locate documents Relevant when the user wants to locate documents

which they have seen before (e.g., the budget report which they have seen before (e.g., the budget report for Year 2000).for Year 2000).

2020

Other Factors to ConsiderOther Factors to Consider• User effortUser effort: Work required from the user : Work required from the user

in formulating queries, conducting the in formulating queries, conducting the search, and screening the output.search, and screening the output.

• Response timeResponse time: Time interval between : Time interval between receipt of a user query and the receipt of a user query and the presentation of system responses.presentation of system responses.

• Form of presentationForm of presentation: Influence of search : Influence of search output format on the user’s ability to output format on the user’s ability to utilize the retrieved materials.utilize the retrieved materials.

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Experimental Setup for Experimental Setup for BenchmarkingBenchmarking• AAnalyticalnalytical performance evaluation is difficult performance evaluation is difficult

for document retrieval systems because many for document retrieval systems because many characteristics such as relevance, distribution of characteristics such as relevance, distribution of words, etc., are difficult to describe with words, etc., are difficult to describe with mathematical precision.mathematical precision.

• Performance is measured by Performance is measured by benchmarkingbenchmarking. . That is, the retrieval effectiveness of a system is That is, the retrieval effectiveness of a system is evaluated on a evaluated on a given set of documentsgiven set of documents, , queriesqueries, , and and relevance judgmentsrelevance judgments..

• Performance data is valid only for the Performance data is valid only for the environment under which the system is environment under which the system is evaluated. evaluated.

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BenchmarksBenchmarks• A benchmark collection contains:A benchmark collection contains:

– A set of standard documents and queries/topics.A set of standard documents and queries/topics.– A list of relevant documents for each query.A list of relevant documents for each query.

• Standard collections for traditional IR:Standard collections for traditional IR:– Smart collection: ftp://ftp.cs.cornell.edu/pub/smartSmart collection: ftp://ftp.cs.cornell.edu/pub/smart– TREC: http://trec.nist.gov/TREC: http://trec.nist.gov/

Standard document collection

Standard queries

Algorithm under test Evaluation

Standard result

Retrieved result

Precision and recall

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Benchmarking Benchmarking The The ProblemsProblems

• Performance data is valid only for a Performance data is valid only for a particular benchmark.particular benchmark.

• Building a benchmark corpus is a Building a benchmark corpus is a difficult task.difficult task.

• Benchmark web corpora are just Benchmark web corpora are just starting to be developed.starting to be developed.

• Benchmark foreign-language corpora Benchmark foreign-language corpora are just starting to be developed.are just starting to be developed.

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CollectionCollection Number Of Number Of Number Of Number Of Raw Size Raw Size Name Name Documents Documents Queries Queries (Mbytes) (Mbytes) CACM CACM 3,204 3,204 64 64 1.5 1.5 CISICISI 1,460 1,460 112 112 1.3 1.3 CRANCRAN 1,400 1,400 225 225 1.6 1.6 MED MED 1,033 1,033 30 30 1.1 1.1 TIME TIME 425 425 83 83 1.5 1.5

• Different researchers used different test Different researchers used different test collections and evaluation techniques.collections and evaluation techniques.

Early Test CollectionsEarly Test Collections

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The TREC Benchmark The TREC Benchmark • TREC: Text REtrieval Conference (http://trec.nist.gov/) Originated from the TIPSTER program sponsored by Defense Advanced Research Projects Agency (DARPA).

• Became an annual conference in 1992, co-sponsored by the National Institute of Standards and Technology (NIST) and DARPA.

• Participants are given parts of a standard set of documents and TOPICS (from which queries have to be derived) in different stages for training and testing.

• Participants submit the P/R values for the final document and query corpus and present their results at the conference.

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The TREC Objectives The TREC Objectives • Provide a common ground for comparing different IR techniques.

– Same set of documents and queries, and same evaluation method.• Sharing of resources and experiences in developing the benchmark.

– With major sponsorship from government to develop large benchmark collections.

• Encourage participation from industry and academia.• Development of new evaluation techniques, particularly for new applications.

– Retrieval, routing/filtering, non-English collection, web-based collection, question answering.

2727

TREC AdvantagesTREC Advantages

• Large scale (compared to a few MB in the SMART Large scale (compared to a few MB in the SMART Collection).Collection).

• Relevance judgments provided.Relevance judgments provided.• Under continuous development with support from the Under continuous development with support from the

U.S. Government.U.S. Government.• Wide participation:Wide participation:

– TREC 1: 28 papers 360 pages.TREC 1: 28 papers 360 pages.– TREC 4: 37 papers 560 pages.TREC 4: 37 papers 560 pages.– TREC 7: 61 papers 600 pages. TREC 7: 61 papers 600 pages. – TREC 8: 74 papers.TREC 8: 74 papers.– TREC 2004 had 103 participants from over 20 countries TREC 2004 had 103 participants from over 20 countries

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TREC ImpactsTREC ImpactsCornell University TREC Systems

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TREC-7

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TREC TasksTREC Tasks• Ad hocAd hoc: New questions are being asked on : New questions are being asked on

a static set of data. a static set of data. • Routing: Routing: Same questions are being asked, Same questions are being asked,

but new information is being searched. but new information is being searched. (news clipping, library profiling).(news clipping, library profiling).

• New tasks added after TREC 5 - New tasks added after TREC 5 - Interactive, multilingual, natural Interactive, multilingual, natural language, multiple database merging, language, multiple database merging, filtering, very large corpus (20 GB, 7.5 filtering, very large corpus (20 GB, 7.5 million documents), question answering.million documents), question answering.

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TREC TracksTREC TracksRetrieval in a domain

Answers, not docs

Web searching, size

Beyond text

Beyond just English

Human-in-the-loop

Streamed text

Static text Ad Hoc, Robust

Interactive, HARD

X{X,Y,Z} ChineseSpanish

VideoSpeechOCR

TerabyteWebVLC

NoveltyQ&A

FilteringRouting

Genome

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TREC-6 Cross Language TREC-6 Cross Language TaskTask• Task: starting in one language, Task: starting in one language,

search for documents written in a search for documents written in a variety of languagesvariety of languages•Document collectionDocument collection

– SDA: French (257 MB), German (331 MB)SDA: French (257 MB), German (331 MB)– Neue Zürcher Zeitung (198 MB, in German)Neue Zürcher Zeitung (198 MB, in German)– AP (759MB, in English)AP (759MB, in English)

•25 topics:25 topics:– Mostly built in English and translated at NIST into Mostly built in English and translated at NIST into

French and German; also translated elsewhere into French and German; also translated elsewhere into Spanish and DutchSpanish and Dutch

•Relevance judgments made at NIST by two tri-Relevance judgments made at NIST by two tri-lingual surrogate users (who built the topics)lingual surrogate users (who built the topics)

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TREC-7 Cross Language TREC-7 Cross Language TaskTask

•Document collection:Document collection:– SDA: French (257 MB), German (331 MB), Italian SDA: French (257 MB), German (331 MB), Italian

(194MB)(194MB)– Neue Zürcher Zeitung (198 MB, in German)Neue Zürcher Zeitung (198 MB, in German)– AP (759MB, in English)AP (759MB, in English)

•28 topics (7 each from 4 sites)28 topics (7 each from 4 sites)

•Relevance judgments made Relevance judgments made independently at each site by native independently at each site by native speakersspeakers

English: NI ST German: University BonnFrench: EPFL Lausanne I talian: CNR Pisa

3333

CLIR Evaluation IssuesCLIR Evaluation Issues

• How to ensure that the “translated” topics How to ensure that the “translated” topics represent how an information request would represent how an information request would be made in a given languagebe made in a given language

• How to ensure that there is enough How to ensure that there is enough common understanding of the topic so that common understanding of the topic so that the relevance judgments are consistentthe relevance judgments are consistent

• How to ensure that the sampling techniques How to ensure that the sampling techniques for the relevance judging are complete for the relevance judging are complete enoughenough

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CLEF 2000CLEF 2000

• TREC European CLIR task moved to the TREC European CLIR task moved to the new Cross Language Evaluation Forum new Cross Language Evaluation Forum (CLEF) in 2000; number of participants (CLEF) in 2000; number of participants grew from 12 to 20!!grew from 12 to 20!!

• Document collection stayed the same but Document collection stayed the same but the topics were created in 8 languagesthe topics were created in 8 languages

• The “seriousness” of the experiments The “seriousness” of the experiments also took a big jump also took a big jump

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CLEF 2004CLEF 2004

• Document collection expanded to include Document collection expanded to include major newspapers in 10 languages!!major newspapers in 10 languages!!

• This means that the CLEF test collection is This means that the CLEF test collection is now a cooperative project across at least 10 now a cooperative project across at least 10 groups in Europegroups in Europe

• Topics translated into 14 languagesTopics translated into 14 languages

• Fifty-five participating groupsFifty-five participating groups

• Six tasks, including question answering and Six tasks, including question answering and image retrieval image retrieval

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TREC TracksTREC TracksRetrieval in a domain

Answers, not docs

Web searching, size

Beyond text

Beyond just English

Human-in-the-loop

Streamed text

Static text Ad Hoc, Robust

Interactive, HARD

X{X,Y,Z} ChineseSpanish

VideoSpeechOCR

TerabyteWebVLC

NoveltyQ&A

FilteringRouting

Genome

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Spoken Document RetrievalSpoken Document Retrieval

• Documents: TREC-7 had 100 hours of Documents: TREC-7 had 100 hours of news broadcasts; 550 hours/21,500 news broadcasts; 550 hours/21,500 stories in TREC-8. stories in TREC-8.

• Topics: similar to “standard” TREC Topics: similar to “standard” TREC topics, 23 in TREC-7 and 50 in TREC-8topics, 23 in TREC-7 and 50 in TREC-8

• ““Documents” were available as several Documents” were available as several baseline recognizer outputs (at different baseline recognizer outputs (at different error rates), along with transcriptserror rates), along with transcripts

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Video RetrievalVideo Retrieval

• Video retrieval is not just speech retrieval, Video retrieval is not just speech retrieval, even though that is a major component of even though that is a major component of many current systemsmany current systems

• TREC 2001 had a video retrieval track with TREC 2001 had a video retrieval track with 11 hours of video, 2 tasks (shot boundary 11 hours of video, 2 tasks (shot boundary and search), and 12 participantsand search), and 12 participants

• TRECvid 2004 had 70 hours, 4 tasks TRECvid 2004 had 70 hours, 4 tasks (feature extraction and story segmentation (feature extraction and story segmentation added) and 33 participantsadded) and 33 participants

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TREC TracksTREC TracksRetrieval in a domain

Answers, not docs

Web searching, size

Beyond text

Beyond just English

Human-in-the-loop

Streamed text

Static text Ad Hoc, Robust

Interactive, HARD

X{X,Y,Z} ChineseSpanish

VideoSpeechOCR

TerabyteWebVLC

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FilteringRouting

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TREC-8 Factoid QATREC-8 Factoid QA

• Document collection was “regular” TREC-8 Document collection was “regular” TREC-8

• Topics are now questions (198 of them)Topics are now questions (198 of them)– What many calories in a Big Mac?What many calories in a Big Mac?– Where is the Taj Mahal?Where is the Taj Mahal?

• Task is to retrieve answer strings of 50 or Task is to retrieve answer strings of 50 or 250 bytes, not a document list250 bytes, not a document list

• Humans determine correct answers from Humans determine correct answers from what is submittedwhat is submitted

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Moving beyond factoid QAMoving beyond factoid QA

• Use of questions from MSN, AskJeeves logs in Use of questions from MSN, AskJeeves logs in 2000 and 20012000 and 2001

• Addition of questions with no answers in 2001 Addition of questions with no answers in 2001 and reduction to 50-byte answersand reduction to 50-byte answers

• Requirement of exact answers in 2002Requirement of exact answers in 2002• Addition of “definition”/who is questions in 2003Addition of “definition”/who is questions in 2003• Expansion of these questions to include exact Expansion of these questions to include exact

and list slots in 2004and list slots in 2004• Addition of events, pilot of relationship Addition of events, pilot of relationship

questions planned for 2005questions planned for 2005

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Cross-language QACross-language QA

• CLEF started Cross-language QA in 2004CLEF started Cross-language QA in 2004

• Lots of interest, lots of unique issuesLots of interest, lots of unique issues– Do cultural aspects make this more difficult Do cultural aspects make this more difficult

than cross-language document retrieval??than cross-language document retrieval??– What language should the answers be in??What language should the answers be in??– Are there specific types of questions that Are there specific types of questions that

should be tested??should be tested??– Would it be interesting to test question Would it be interesting to test question

answering in a specific domain??answering in a specific domain??

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For more informationFor more information

trec.nist.govtrec.nist.gov for more on TREC for more on TREC

www.clef-campaign.org www.clef-campaign.org for more on for more on CLEFCLEF

research.nii.ac.jp/ntcirresearch.nii.ac.jp/ntcir for more on for more on NTCIR (evaluation in Japanese, NTCIR (evaluation in Japanese, Chinese, and Korean)Chinese, and Korean)

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Characteristics of the TREC Characteristics of the TREC Collection Collection

• Both long and short documents (from a few Both long and short documents (from a few hundred to over one thousand unique terms in hundred to over one thousand unique terms in a document).a document).

• Test documents consist of: Test documents consist of: WSJWSJ Wall Street Journal articles (1986-1992) Wall Street Journal articles (1986-1992) 550 M 550 M AP AP Associate Press Newswire (1989) Associate Press Newswire (1989) 514 M514 M ZIFFZIFF Computer Select Disks (Ziff-Davis Publishing) Computer Select Disks (Ziff-Davis Publishing) 493 M 493 M FR FR Federal Register Federal Register 469 M 469 M DOE DOE Abstracts from Department of Energy reports Abstracts from Department of Energy reports 190 M 190 M

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More Details on Document More Details on Document CollectionsCollections• Volume 1 (Mar 1994) - Volume 1 (Mar 1994) - Wall Street JournalWall Street Journal (1987, 1988, 1989), (1987, 1988, 1989),

Federal Register Federal Register (1989), (1989), Associated PressAssociated Press (1989), (1989), Department of Department of Energy abstractsEnergy abstracts, and , and Information from the Computer SelectInformation from the Computer Select disks disks (1989, 1990)(1989, 1990)

• Volume 2 (Mar 1994) - Volume 2 (Mar 1994) - Wall Street JournalWall Street Journal (1990, 1991, 1992), the (1990, 1991, 1992), the Federal Register (1988), Associated Press (1988) and Information Federal Register (1988), Associated Press (1988) and Information from the Computer Select disks (1989, 1990)from the Computer Select disks (1989, 1990)

• Volume 3 (Mar 1994) - Volume 3 (Mar 1994) - San Jose Mercury NewsSan Jose Mercury News (1991), the Associated (1991), the Associated Press (1990), Press (1990), U.S. PatentsU.S. Patents (1983-1991), and Information from the (1983-1991), and Information from the Computer Select disks (1991, 1992)Computer Select disks (1991, 1992)

• Volume 4 (May 1996) - Volume 4 (May 1996) - Financial TimesFinancial Times Limited (1991, 1992, 1993, Limited (1991, 1992, 1993, 1994), the 1994), the Congressional Record of the 103rd CongressCongressional Record of the 103rd Congress (1993), and (1993), and the Federal Register (1994). the Federal Register (1994).

• Volume 5 (Apr 1997) - Volume 5 (Apr 1997) - Foreign Broadcast Information ServiceForeign Broadcast Information Service (1996) (1996) and the and the Los Angeles TimesLos Angeles Times (1989, 1990). (1989, 1990).

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TREC Disk 4,5TREC Disk 4,5TREC Disk 4 Congressional Record of the 103rd Congress

approx. 30,000 documents approx. 235 MBFederal Register (1994) approx. 55,000 documents approx. 395 MBFinancial Times (1992-1994) approx. 210,000 documents approx. 565 MB

TREC Disk 5 Data provided from the Foreign Broadcast Information Service approx. 130,000 documents approx. 470 MBLos Angeles Times (randomly selected articles from 1989 & 1990) approx. 130,000 document approx. 475 MB

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Sample Document (with Sample Document (with SGML)SGML)<<DOC>DOC> <DOCNO><DOCNO> WSJ870324-0001 WSJ870324-0001 </DOCNO></DOCNO> <HL><HL> John Blair Is Near Accord To Sell Unit, Sources Say John Blair Is Near Accord To Sell Unit, Sources Say </HL></HL> <DD><DD> 03/24/87 03/24/87</DD></DD> <SO><SO> WALL STREET JOURNAL (J) WALL STREET JOURNAL (J) </SO></SO><IN><IN> REL TENDER OFFERS, MERGERS, ACQUISITIONS (TNM) REL TENDER OFFERS, MERGERS, ACQUISITIONS (TNM)

MARKETING, ADVERTISING (MKT) TELECOMMUNICATIONS, MARKETING, ADVERTISING (MKT) TELECOMMUNICATIONS, BROADCASTING, TELEPHONE, TELEGRAPH (TEL) BROADCASTING, TELEPHONE, TELEGRAPH (TEL) </IN></IN>

<DATELINE><DATELINE> NEW YORK NEW YORK </DATELINE></DATELINE> <TEXT><TEXT> John Blair &amp; Co. is close to an agreement to sell its TV John Blair &amp; Co. is close to an agreement to sell its TV

station advertising representation operation and program station advertising representation operation and program production unit to an investor group led by James H. Rosenfield, production unit to an investor group led by James H. Rosenfield, a former CBS Inc. executive, industry sources said. Industry a former CBS Inc. executive, industry sources said. Industry sources put the value of the proposed acquisition at more than sources put the value of the proposed acquisition at more than $100 million. ... $100 million. ...

</TEXT> </TEXT> </DOC> </DOC>

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Sample Query (with SGML)Sample Query (with SGML)

<<top> top> <head><head> Tipster Topic Description Tipster Topic Description <num><num> Number: 066 Number: 066 <dom><dom> Domain: Science and Technology Domain: Science and Technology <title><title> Topic: Natural Language Processing Topic: Natural Language Processing <desc><desc> Description: Document will identify a type of natural Description: Document will identify a type of natural

language processing technology which is being developed or language processing technology which is being developed or marketed in the U.S. marketed in the U.S.

<narr><narr> Narrative: A relevant document will identify a company or Narrative: A relevant document will identify a company or institution developing or marketing a natural language institution developing or marketing a natural language processing technology, identify the technology, and identify processing technology, identify the technology, and identify one of more features of the company's product.one of more features of the company's product.

<con><con> Concept(s): 1. natural language processing ;2. translation, Concept(s): 1. natural language processing ;2. translation, language, dictionarylanguage, dictionary

<fac><fac> Factor(s): Factor(s): <nat><nat> Nationality: U.S Nationality: U.S.</nat>.</nat></fac></fac> <def><def> Definitions(s): Definitions(s): </top></top>

4949

TREC PropertiesTREC Properties• Both documents and queries contain Both documents and queries contain

many different kinds of information many different kinds of information (fields).(fields).

• Generation of the formal queries Generation of the formal queries (Boolean, Vector Space, etc.) is the (Boolean, Vector Space, etc.) is the responsibility of the system.responsibility of the system.– A system may be very good at querying A system may be very good at querying

and ranking, but if it generates poor and ranking, but if it generates poor queries from the topic, its final P/R queries from the topic, its final P/R would be poor.would be poor.

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Two more TREC Document Two more TREC Document ExamplesExamples

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Another Example of TREC Another Example of TREC Topic/QueryTopic/Query

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Evaluation Evaluation • Summary table statisticsSummary table statistics: Number of topics, : Number of topics,

number of documents retrieved, number of number of documents retrieved, number of relevant documents.relevant documents.

• Recall-precision averageRecall-precision average: Average precision at : Average precision at 11 recall levels (0 to 1 at 0.1 increments).11 recall levels (0 to 1 at 0.1 increments).

• Document level averageDocument level average: Average precision : Average precision when 5, 10, .., 100, … 1000 documents are when 5, 10, .., 100, … 1000 documents are retrieved.retrieved.

• Average precision histogramAverage precision histogram: Difference of the : Difference of the R-precision for each topic and the average R-R-precision for each topic and the average R-precision of all systems for that topic.precision of all systems for that topic.

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Cystic Fibrosis (CF) Cystic Fibrosis (CF) CollectionCollection• 1,239 abstracts of medical journal articles 1,239 abstracts of medical journal articles

on CF.on CF.• 100 information requests (queries) in the 100 information requests (queries) in the

form of complete English questions.form of complete English questions.• Relevant documents determined and Relevant documents determined and

rated by 4 separate medical experts on 0-rated by 4 separate medical experts on 0-2 scale:2 scale:– 0: Not relevant.0: Not relevant.– 1: Marginally relevant.1: Marginally relevant.– 2: Highly relevant.2: Highly relevant.

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CF Document FieldsCF Document Fields

• MEDLINE access numberMEDLINE access number

• AuthorAuthor

• TitleTitle

• SourceSource

• Major subjectsMajor subjects

• Minor subjectsMinor subjects

• Abstract (or extract)Abstract (or extract)

• References to other documentsReferences to other documents

• Citations to this documentCitations to this document

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Sample CF DocumentSample CF DocumentAN 74154352AU Burnell-R-H. Robertson-E-F.TI Cystic fibrosis in a patient with Kartagener syndrome.SO Am-J-Dis-Child. 1974 May. 127(5). P 746-7.MJ CYSTIC-FIBROSIS: co. KARTAGENER-TRIAD: co.MN CASE-REPORT. CHLORIDES: an. HUMAN. INFANT. LUNG: ra. MALE. SITUS-INVERSUS: co, ra. SODIUM: an. SWEAT: an.AB A patient exhibited the features of both Kartagener syndrome and cystic fibrosis. At most, to the authors' knowledge, this represents the third such report of the combination. Cystic fibrosis should be excluded before a diagnosis of Kartagener syndrome is made.RF 001 KARTAGENER M BEITR KLIN TUBERK 83 489 933 002 SCHWARZ V ARCH DIS CHILD 43 695 968 003 MACE JW CLIN PEDIATR 10 285 971 …CT 1 BOCHKOVA DN GENETIKA (SOVIET GENETICS) 11 154 975 2 WOOD RE AM REV RESPIR DIS 113 833 976 3 MOSSBERG B MT SINAI J MED 44 837 977 …

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Sample CF QueriesSample CF QueriesQN 00002QU Can one distinguish between the effects of mucus hypersecretion and infection on the submucosal glands of the respiratory tract in CF?NR 00007RD 169 1000 434 1001 454 0100 498 1000 499 1000 592 0002 875 1011

QN 00004QU What is the lipid composition of CF respiratory secretions?NR 00009RD 503 0001 538 0100 539 0100 540 0100 553 0001 604 2222 669 1010 711 2122 876 2222

NR: Number of Relevant documentsRD: Relevant Documents

Ratings code: Four 0-2 ratings, one from each expert

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Preprocessing for VSR Preprocessing for VSR ExperimentsExperiments• Separate file for each document with Separate file for each document with

just:just:– AuthorAuthor– TitleTitle– Major and Minor TopicsMajor and Minor Topics– Abstract (Extract)Abstract (Extract)

• Relevance judgment made binary by Relevance judgment made binary by assuming that assuming that allall documents rated 1 or 2 documents rated 1 or 2 by by anyany expert were relevant. expert were relevant.