l10evaluation

Evaluation of IR Systems !

Adapted from Lectures by

Prabhakar Raghavan (Yahoo and Stanford) and Christopher Manning (Stanford)

Prasad 1 L10Evaluation

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This lecture

n  Results summaries: n  Making our good results usable to a user

n  How do we know if our results are any good? n  Evaluating a search engine

n  Benchmarks n  Precision and recall

Prasad L10Evaluation

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Result Summaries

n  Having ranked the documents matching a query, we wish to present a results list.

n  Most commonly, a list of the document titles plus a short summary, aka “10 blue links”.

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Summaries

n  The title is typically automatically extracted from document metadata. What about the summaries? n  This description is crucial. n  User can identify good/relevant hits based on description.

n  Two basic kinds: n  A static summary of a document is always the same,

regardless of the query that hit the doc. n  A dynamic summary is a query-dependent attempt to

explain why the document was retrieved for the query at hand.


Static summaries

n  In typical systems, the static summary is a subset of the document. n  Simplest heuristic: the first 50 (or so – this can be

varied) words of the document n  Summary cached at indexing time

n  More sophisticated: extract from each document a set of “key” sentences

n  Simple NLP heuristics to score each sentence n  Summary is made up of top-scoring sentences.

n  Most sophisticated: NLP used to synthesize a summary

n  Seldom used in IR (cf. text summarization work)

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Dynamic summaries

n  Present one or more “windows” within the document that contain several of the query terms n  “KWIC” snippets: Keyword in Context presentation n  Generated in conjunction with scoring

n  If query found as a phrase, all or some occurrences of the phrase in the doc

n  If not, document windows that contain multiple query terms

n  The summary itself gives the entire content of the window – all terms, not only the query terms.

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Generating dynamic summaries

n  If we have only a positional index, we cannot (easily) reconstruct context window surrounding hits.

n  If we cache the documents at index time, then we can find windows in it, cueing from hits found in the positional index. n  E.g., positional index says “the query is a phrase in position

4378” so we go to this position in the cached document and stream out the content

n  Most often, cache only a fixed-size prefix of the doc. n  Note: Cached copy can be outdated


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Dynamic summaries

n  Producing good dynamic summaries is a tricky optimization problem n  The real estate for the summary is normally small

and fixed n  Want snippets to be long enough to be useful n  Want linguistically well-formed snippets n  Want snippets maximally informative about doc

n  But users really like snippets, even if they complicate IR system design


Alternative results presentations?

n  An active area of HCI research n  An alternative: http://www.searchme.com / copies the

idea of Apple’s Cover Flow for search results


Evaluating search engines


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Measures for a search engine

n  How fast does it index n  Number of documents/hour n  (Average document size)

n  How fast does it search n  Latency as a function of index size

n  Expressiveness of query language n  Ability to express complex information needs n  Speed on complex queries

n  Uncluttered UI n  Is it free? Prasad L10Evaluation

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Measures for a search engine

n  All of the preceding criteria are measurable: we can quantify speed/size; we can make expressiveness precise

n  The key measure: user happiness n  What is this?

n  Speed of response/size of index are factors n  But blindingly fast, useless answers won’t make a user

happy

n  Need a way of quantifying user happiness


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Data Retrieval vs Information Retrieval

n  DR Performance Evaluation (after establishing correctness) n  Response time n  Index space

n  IR Performance Evaluation n  How relevant is the answer set? (required to

establish functional correctness, e.g., through benchmarks)


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Measuring user happiness

n  Issue: who is the user we are trying to make happy? n  Depends on the setting/context

n  Web engine: user finds what they want and return to the engine n  Can measure rate of return users

n  eCommerce site: user finds what they want and make a purchase n  Is it the end-user, or the eCommerce site, whose

happiness we measure? n  Measure time to purchase, or fraction of searchers

who become buyers? Prasad L10Evaluation

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Measuring user happiness

n  Enterprise (company/govt/academic): Care about “user productivity”

n  How much time do my users save when looking for information?

n  Many other criteria having to do with breadth of access, secure access, etc.


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Happiness: elusive to measure

n  Most common proxy: relevance of search results

n  But how do you measure relevance?

n  We will detail a methodology here, then examine its issues

n  Relevant measurement requires 3 elements: 1.  A benchmark document collection 2.  A benchmark suite of queries 3.  A usually binary assessment of either Relevant or

Nonrelevant for each query and each document n  Some work on more-than-binary, but not the standard


Evaluating an IR system

n  Note: the information need is translated into a query

n  Relevance is assessed relative to the information need, not the query n  E.g., Information need: I'm looking for information

on whether drinking red wine is more effective at reducing heart attack risks than white wine.

n  Query: wine red white heart attack effective n  You evaluate whether the doc addresses the

information need, not whether it has these words


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Difficulties with gauging Relevancy

n  Relevancy, from a human standpoint, is: n  Subjective: Depends upon a specific

user’s judgment. n  Situational: Relates to user’s current

needs. n  Cognitive: Depends on human

perception and behavior. n  Dynamic: Changes over time.


Standard relevance benchmarks

n  TREC - National Institute of Standards and Technology (NIST) has run a large IR test bed for many years

n  Reuters and other benchmark doc collections used

n  “Retrieval tasks” specified n  sometimes as queries

n  Human experts mark, for each query and for each doc, Relevant or Nonrelevant n  or at least for subset of docs that some system

returned for that query Prasad 19

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Unranked retrieval evaluation: Precision and Recall

n  Precision: fraction of retrieved docs that are relevant = P(relevant|retrieved)

n  Recall: fraction of relevant docs that are retrieved = P(retrieved|relevant)

n  Precision P = tp/(tp + fp) n  Recall R = tp/(tp + fn)

Relevant Nonrelevant

Retrieved tp fp

Not Retrieved fn tn


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Precision and Recall


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Precision and Recall in Practice

n  Precision n  The ability to retrieve top-ranked documents that

are mostly relevant. n  The fraction of the retrieved documents that are relevant.

n  Recall n  The ability of the search to find all of the relevant

items in the corpus. n  The fraction of the relevant documents that are retrieved.


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Should we instead use the accuracy measure for evaluation?

n  Given a query, an engine classifies each doc as “Relevant” or “Nonrelevant”

n  The accuracy of an engine: the fraction of these classifications that are correct n  Accuracy is a commonly used evaluation

measure in machine learning classification work

n  Why is this not a very useful evaluation measure in IR?


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Why not just use accuracy?

n  How to build a 99.9999% accurate search engine on a low budget….

n  People doing information retrieval want to find something and have a certain tolerance for junk.

Search for:

0 matching results found.


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Precision/Recall

n  You can get high recall (but low precision) by retrieving all docs for all queries! n  Recall is a non-decreasing function of the number of

docs retrieved

n  In a good system, precision decreases as either the number of docs retrieved or recall increases n  This is not a theorem, but a result with strong

empirical confirmation


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Trade-offs

1 0

1

Recall

Prec

isio

n

The ideal Returns relevant documents but misses many useful ones too

Returns most relevant documents but includes lot of junk


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Difficulties in using precision/recall

n  Should average over large document collection/query ensembles

n  Need human relevance assessments n  People aren’t reliable assessors

n  Assessments have to be binary n  Nuanced assessments?

n  Heavily skewed by collection/authorship n  Results may not translate from one domain to

another


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A combined measure: F

n  Combined measure that assesses precision/recall tradeoff is F measure (harmonic mean):

n  Harmonic mean is a conservative average

n  See CJ van Rijsbergen, Information Retrieval

RPPR

RP

F+

=+

=2

112


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Aka E Measure (parameterized F Measure)

n  Variants of F measure that allow weighting emphasis on precision over recall:

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

PRRPPRE

1

2

2

2

2

)1()1(+

+=

+

+=

β

βββ


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F1 and other averages

Combined Measures

0

20

40

60

80

100

0 20 40 60 80 100

Precision (Recall fixed at 70%)

Minimum

Maximum

Arithmetic

Geometric

Harmonic


Recall vs Precision and F1

0

0.2

0.4

0.6

0.8

1

1.2

0 0.2 0.4 0.6 0.8 1 1.2

Recall

Prec

isio

n an

d F1

Breakeven Point

n  Breakeven point is the point where precision equals recall.

n  Alternative single measure of IR effectiveness.

n  How do you compute it?

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Evaluating ranked results

n  Evaluation of ranked results:

n  The system can return any number of results

n  By taking various numbers of the top returned documents (levels of recall), the evaluator can produce a precision-recall curve


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

Computing Recall/Precision Points: An Example

n doc # relevant1 588 x2 589 x3 5764 590 x5 9866 592 x7 9848 9889 57810 98511 10312 59113 772 x14 990

Let total # of relevant docs = 6 Check 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

L10Evaluation

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A precision-recall curve

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.2 0.4 0.6 0.8 1.0Recall

Precision

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Interpolating a Recall/Precision Curve

n  Interpolate a precision value for each standard recall level: n  rj ∈{0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0} n  r0 = 0.0, r1 = 0.1, …, r10=1.0

n  The interpolated precision at the j-th standard recall level is the maximum known precision at any recall level above the j-th level:

)(max)( rPrPrrj

j ≤=

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Average Recall/Precision Curve

n  Typically average performance over a large set of queries.

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

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

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Evaluation Metrics (cont’d)

n  Graphs are good, but people want summary measures! n  Precision at fixed retrieval level

n  Precision-at-k: Precision of top k results n  Perhaps appropriate for most of web search: all people

want are good matches on the first one or two results pages n  But: averages badly and has an arbitrary parameter of k

n  11-point interpolated average precision n  The standard measure in the early TREC competitions: you

take the precision at 11 levels of recall varying from 0 to 1 by tenths of the documents, using interpolation (the value for 0 is always interpolated!), and average them

n  Evaluates performance at all recall levels


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Typical (good) 11 point precisions

n  SabIR/Cornell 8A1 11pt precision from TREC 8 (1999)

0

0.2

0.4

0.6

0.8

1

0 0.2 0.4 0.6 0.8 1

Recall

Precision

Prasad

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11 point precisions

0

20

40

60

80

100

120

0 20 40 60 80 100 120

Recall

Precision


Receiver Operating Characteristics (ROC) Curve

n  True positive rate =

tp/(tp+fn) = recall = sensitivity

n  False positive rate = fp/(tn+fp). Related to precision.

n  fpr=0 <-> p=1

n  Why is the blue line “worthless”?

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Mean average precision (MAP)

n  MAP for a query n  Average of the precision value for each (of

the k top) relevant document retrieved n  This approach weights early appearance of a relevant

document over later appearance

n  MAP for query collection is the arithmetic average of MAP for each query n  Macro-averaging: each query counts

equally


Average Precision

Mean Average Precision (MAP) n  Mean Average Precision (MAP)

n  summarize rankings from multiple queries by averaging average precision

n  most commonly used measure in research papers

n  assumes user is interested in finding many relevant documents for each query

n  requires many relevance judgments in text collection

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Summarize a Ranking: MAP

n  Given that n docs are retrieved n  Compute the precision (at rank) where each

(new) relevant document is retrieved => p(1),…,p(k), if we have k rel. docs

n  E.g., if the first rel. doc is at the 2nd rank, then p(1)=1/2. n  If a relevant document never gets retrieved, we

assume the precision corresponding to that rel. doc to be zero

n  Compute the average over all the relevant documents n  Average precision = (p(1)+…p(k))/k

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(cont’d)

n  This gives us (non-interpolated) average precision, which captures both precision and recall and is sensitive to the rank of each relevant document

n  Mean Average Precisions (MAP) n  MAP = arithmetic mean average

precision over a set of topics n  gMAP = geometric mean average

precision over a set of topics (more affected by difficult topics)

Discounted Cumulative Gain

n  Popular measure for evaluating web search and related tasks

n  Two assumptions: n  Highly relevant documents are more useful

than marginally relevant document n  the lower the ranked position of a relevant

document, the less useful it is for the user, since it is less likely to be examined

Discounted Cumulative Gain

n  Uses graded relevance as a measure of usefulness, or gain, from examining a document

n  Gain is accumulated starting at the top of the ranking and may be reduced, or discounted, at lower ranks

n  Typical discount is 1/log (rank) n  With base 2, the discount at rank 4 is 1/2,

and at rank 8 it is 1/3

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Summarize a Ranking: DCG

n  What if relevance judgments are in a scale of [1,r]? r>2

n  Cumulative Gain (CG) at rank n n  Let the ratings of the n documents be r1, r2, …

rn (in ranked order) n  CG = r1+r2+…rn

n  Discounted Cumulative Gain (DCG) at rank n n  DCG = r1 + r2/log22 + r3/log23 + … rn/log2n

n  We may use any base for the logarithm, e.g., base=b

Discounted Cumulative Gain n  DCG is the total gain accumulated at a particular

rank p:

n  Alternative formulation:

n  used by some web search companies n  emphasis on retrieving highly relevant documents

DCG Example

n  10 ranked documents judged on 0-3 relevance scale: 3, 2, 3, 0, 0, 1, 2, 2, 3, 0

n  discounted gain: 3, 2/1, 3/1.59, 0, 0, 1/2.59, 2/2.81, 2/3, 3/3.17, 0 = 3, 2, 1.89, 0, 0, 0.39, 0.71, 0.67, 0.95, 0

n  DCG: 3, 5, 6.89, 6.89, 6.89, 7.28, 7.99, 8.66, 9.61, 9.61

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Summarize a Ranking: NDCG

n  Normalized Cumulative Gain (NDCG) at rank n n  Normalize DCG at rank n by the DCG value at

rank n of the ideal ranking n  The ideal ranking would first return the

documents with the highest relevance level, then the next highest relevance level, etc

n  Compute the precision (at rank) where each (new) relevant document is retrieved => p(1),…,p(k), if we have k rel. docs

n  NDCG is now quite popular in evaluating Web search

NDCG - Example

i Ground Truth Ranking Func2on1 Ranking Func2on2

Document Order ri

Document Order ri

Document Order ri

1 d4 2 d3 2 d3 2

2 d3 2 d4 2 d2 1

3 d2 1 d2 1 d4 2

4 d1 0 d1 0 d1 0

NDCGGT=1.00 NDCGRF1=1.00 NDCGRF2=0.9203

6309.44log

03log

12log

22222

=⎟⎟⎠

⎞⎜⎜⎝

⎛+++=GTDCG

6309.44log

03log

12log

22222

1 =⎟⎟⎠

⎞⎜⎜⎝

⎛+++=RFDCG

2619.44log

03log

22log

12222

2 =⎟⎟⎠

⎞⎜⎜⎝

⎛+++=RFDCG

6309.4== GTDCGMaxDCG

4 documents: d1, d2, d3, d4

n  Graded ranking/ordering:

n  DCG = 4 + 2/log(2) + 0/log(3) + 1/log(4) n  = 6.5

n  IDCG = 4 + 2/log(2) + 1/log(3) + 0/log(4) n  = 6.63

n  NDCG = DCG/IDCG = 6.5/6.63 = .98

NDCG - Example

1 0 2 4

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R- Precision

n  Precision at the R-th position in the ranking of results for a query that has R relevant documents.

n doc # relevant1 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

L10Evaluation

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Variance

n  For a test collection, it is usual that a system does crummily on some information needs (e.g., MAP = 0.1) and excellently on others (e.g., MAP = 0.7)

n  Indeed, it is usually the case that the variance in performance of the same system across queries is much greater than the variance of different systems on the same query. n  That is, there are easy information needs and hard

ones!


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Test Collections

Prasad

Creating Test Collections

for IR Evaluation


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From document collections to test collections

n  Still need n  Test queries n  Relevance assessments

n  Test queries n  Must be germane to docs available n  Best designed by domain experts n  Random query terms generally not a good idea

n  Relevance assessments n  Human judges, time-consuming n  Are human panels perfect?


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Can we avoid human judgment?

n  Not really n  Makes experimental work hard

n  Especially on a large scale n  In some very specific settings, can use proxies n  But once we have test collections, we can reuse

them (so long as we don’t overtrain too badly)

n  Example below, approximate vector space retrieval


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Approximate vector retrieval

n  Let G(q) be the “ground truth” of the actual k closest docs on query q

n  Let A(q) be the k docs returned by approximate algorithm A on query q

n  For performance we would measure A(q) ∩ G(q) n  Is this the right measure?


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Alternative proposal

n  Focus instead on how A(q) compares to G(q). n  Goodness can be measured here in cosine

proximity to q: we sum up q•d over d∈ A(q). n  Compare this to the sum of q•d over d∈ G(q).

n  Yields a measure of the relative “goodness” of A vis-à-vis G.

n  Thus A may be 90% “as good as” the ground-truth G, without finding 90% of the docs in G.

n  For scored retrieval, this may be acceptable:

n  Most web engines don’t always return the same answers for a given query.

Prasad

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Kappa measure for inter-judge (dis)agreement

n  Kappa measure n  Agreement measure among judges n  Designed for categorical judgments n  Corrects for chance agreement

n  Kappa = [ P(A) – P(E) ] / [ 1 – P(E) ] n  P(A) – proportion of time judges agree n  P(E) – what agreement would be by chance n  Kappa = 0 for chance agreement, 1 for total agreement.


Kappa Measure: Example

Number of docs Judge 1 Judge 2

300 Relevant Relevant

70 Nonrelevant Nonrelevant

20 Relevant Nonrelevant

10 Nonrelevant Relevant

P(A)? P(E)?

Kappa Example

n  P(A) = 370/400 = 0.925 n  P(nonrelevant) = (10+20+70+70)/800 = 0.2125 n  P(relevant) = (10+20+300+300)/800 = 0.7878 n  P(E) = 0.2125^2 + 0.7878^2 = 0.665 n  Kappa = (0.925 – 0.665)/(1-0.665) = 0.776

n  Kappa > 0.8 : good agreement n  0.67< Kappa <0.8 : “tentative conclusions” (Carletta ’96) n  Depends on purpose of study

n  For >2 judges: average pairwise kappas 65

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Other Evaluation Measures

Adapted from Slides Attributed to Prof. Dik Lee (Univ. of Science and Tech, Hong Kong)


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Fallout Rate

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

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

document in the collection. n  Precision is undefined when no document is

retrieved.

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


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Subjective Relevance Measure

n  Novelty Ratio: The proportion of items retrieved and judged relevant by the user and of which they were previously unaware. n  Ability to find new information on a topic.

n  Coverage Ratio: The proportion of relevant items retrieved out of the total relevant documents known to a user prior to the search. n  Relevant when the user wants to locate documents which

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


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Other Factors to Consider

n  User effort: Work required from the user in formulating queries, conducting the search, and screening the output.

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

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

n  Collection coverage: Extent to which any/all relevant items are included in the document corpus.


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SKIP DETAILS


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n  Previous experiments were based on the SMART collection which is fairly small. (ftp://ftp.cs.cornell.edu/pub/smart)

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

n  Different researchers used different test collections and evaluation

techniques.

Early Test Collections


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Critique of pure relevance

n  Relevance vs Marginal Relevance n  A document can be redundant even if it is highly

relevant n  Duplicates n  The same information from different sources n  Marginal relevance is a better measure of utility for

the user. n  Using facts/entities as evaluation units more

directly measures true relevance. n  But harder to create evaluation set Prasad L10Evaluation

Evaluation at large search engines

n  Search engines have test collections of queries and hand-ranked results

n  Recall is difficult to measure on the web n  Search engines often use precision at top k, e.g., k = 10 n  . . . or measures that reward you more for getting rank 1

right than for getting rank 10 right. n  NDCG (Normalized Cumulative Discounted Gain)

n  Search engines also use non-relevance-based measures. n  Clickthrough on first result

n  Not very reliable if you look at a single clickthrough … but pretty reliable in the aggregate.

n  Studies of user behavior in the lab n  A/B testing 73 L10Evaluation

A/B testing n  Purpose: Test a single innovation n  Prerequisite: You have a large search engine up and

running. n  Have most users use old system n  Divert a small proportion of traffic (e.g., 1%) to the new

system that includes the innovation n  Evaluate with an “automatic” measure like clickthrough

on first result n  Now we can directly see if the innovation does improve

user happiness. n  Probably the evaluation methodology that large search

engines trust most n  In principle less powerful than doing a multivariate

regression analysis, but easier to understand 74

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TREC Benchmarks


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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 image cannot be displayed. Your computer may not have enough memory to open the image, or the image may have been corrupted. Restart your computer, and then open the file again. If the red x still appears, you may have to delete the image and then insert it again.

•  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.


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TREC Advantages

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

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

U.S. Government. n  Wide participation:

n  TREC 1: 28 papers 360 pages. n  TREC 4: 37 papers 560 pages. n  TREC 7: 61 papers 600 pages. n  TREC 8: 74 papers.


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TREC Tasks n  Ad hoc: New questions are being asked on a

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

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

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


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TREC

n  TREC Ad Hoc task from first 8 TRECs is standard IR task n  50 detailed information needs a year n  Human evaluation of pooled results returned n  More recently other related things: Web track, HARD

n  A TREC query (TREC 5) <top> <num> Number: 225 <desc> Description: What is the main function of the Federal Emergency

Management Agency (FEMA) and the funding level provided to meet emergencies? Also, what resources are available to FEMA such as people, equipment, facilities?

</top> Prasad L10Evaluation

Standard relevance benchmarks: Others

n  GOV2 n  Another TREC/NIST collection n  25 million web pages n  Largest collection that is easily available n  But still 3 orders of magnitude smaller than what

Google/Yahoo/MSN index n  NTCIR

n  East Asian language and cross-language information retrieval

n  Cross Language Evaluation Forum (CLEF) n  This evaluation series has concentrated on European

languages and cross-language information retrieval.

81 L10Evaluation Prasad

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Characteristics of the TREC Collection

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

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


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More Details on Document Collections

n  Volume 1 (Mar 1994) - Wall Street Journal (1987, 1988, 1989), Federal Register (1989), Associated Press (1989), Department of Energy abstracts, and Information from the Computer Select disks (1989, 1990)

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

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

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

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


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TREC Disk 4,5 TREC 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 SGML) <DOC> <DOCNO> WSJ870324-0001 </DOCNO> <HL> John Blair Is Near Accord To Sell Unit, Sources Say </HL> <DD> 03/24/87</DD> <SO> WALL STREET JOURNAL (J) </SO> <IN> REL TENDER OFFERS, MERGERS, ACQUISITIONS (TNM) MARKETING,

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

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

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

</TEXT> </DOC>


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Sample Query (with SGML) <top> <head> Tipster Topic Description <num> Number: 066 <dom> Domain: Science and Technology <title> Topic: Natural Language Processing <desc> Description: Document will identify a type of natural language processing

technology which is being developed or marketed in the U.S. <narr> Narrative: A relevant document will identify a company or institution

developing or marketing a natural language processing technology, identify the technology, and identify one of more features of the company's product.

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

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


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TREC Properties

n  Both documents and queries contain many different kinds of information (fields).

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

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


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Two more TREC Document Examples


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Another Example of TREC Topic/Query


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Evaluation

n  Summary table statistics: Number of topics, number of documents retrieved, number of relevant documents.

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

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

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


91 Prasad L10Evaluation

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Cystic Fibrosis (CF) Collection

n  1,239 abstracts of medical journal articles on CF. n  100 information requests (queries) in the form of

complete English questions. n  Relevant documents determined and rated by 4

separate medical experts on 0-2 scale: n  0: Not relevant. n  1: Marginally relevant. n  2: Highly relevant.


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CF Document Fields

n  MEDLINE access number n  Author n  Title n  Source n  Major subjects n  Minor subjects n  Abstract (or extract) n  References to other documents n  Citations to this document


l10evaluation

Documents

wall street

computer select

discounted

highest relevance

measuring

standard recall

language information

relevance