Information Retrieval Performance, Lecture Slides - Computer Science, Slides of Artificial Intelligence

Prof.Paul McNamee, Information Retrieval,Computer Science, Artificial Intelligence, Johns Hopkins University, Computer Science, Prof.Paul McNamee, Information Retrieval Permormance, Query Refinement

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2010/2011

Uploaded on 11/09/2011

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Week 4
Evaluation of IR Performance
(IIR Chapter 8)
Evaluation Metrics
Test Collections
Query Refinement
(IIR Chapter 9)
Query Expansion
Relevance Feedback
Relevance
In what ways can a document be relevant
to a query?
Answer question precisely (or partially)
Suggest a source for more information
Give background information
Remind the user of other knowledge
Degree of Relevance
Binary: relevant or not?
On a scale: 0-100
Intrinsic or Subjective
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Week 4

 Evaluation of IR Performance

  • (IIR Chapter 8)
  • Evaluation Metrics
  • Test Collections

 Query Refinement

  • (IIR Chapter 9)
  • Query Expansion
  • Relevance Feedback

Relevance

 In what ways can a document be relevant

to a query?

  • Answer question precisely (or partially)
  • Suggest a source for more information
  • Give background information
  • Remind the user of other knowledge

 Degree of Relevance

  • Binary: relevant or not?
  • On a scale: 0-

 Intrinsic or Subjective

What to Evaluate?

 What can be measured that reflects

users’ ability to use system? (Cleverdon 66)

  • Coverage of Information
  • Form of Presentation
  • Effort required/Ease of Use
  • Time and Space Efficiency
  • Recall
    • proportion of relevant material actually retrieved
  • Precision
    • proportion of retrieved material actually relevant

Problems with Precision/Recall

 Can’t know true recall value

  • except in small collections

 Precision/Recall measure different

aspects of search quality

  • A combined measure sometimes is more

appropriate

 Focused somewhat on set evaluation vs.

ranked lists

Precision/Recall CurvesThere is a tradeoff between precision and recallSo measure precision at different levels of recall

  • Interpolate
  • Average over multiple queries precision recall x x x x x x x x x x x Which of these three curves is better?

Existing Test Collections

 Historical, Small

– CACM (3204), CISI (1460), CRAN (1397)
– INSPEC (12684), MED (1033), REUTERS (21578)

 More recent

  • TREC: 1.5M newsprint; 10/18/100GB Web; 1 TB
  • CLEF: 1M+ newspaper articles from 1994 – 1995, 13 langs
  • NTCIR: Asian languages (JP, KR, ZH)
  • FIRE: Hindi, Marathi, Bengali (about 100k docs each)
  • Reuters RV1 (880,000)

Text REtrieval Conference (TREC)

 Annual bake-off for text retrieval systems

 Sponsored by

 Roughly 2.5 gigabytes of text, newswire

  • 50 “topics” (queries)
  • Return top 1000 documents per topic (~80 groups)
  • Results judged by retired intelligence analysts
    • Documents are relevant or not

 Numerous tracks

  • Cross-Language
  • Spoken Documents
  • Question Answering

 http://trec.nist.gov/

Sample TREC Topic

Number: 285 Topic: World submarine forces <desc> Description: Determine the number of submarines, both nuclear-powered and conventional, presently in the inventories of all the countries in the world. <narr> Narrative: We are looking for a count of operable submarines in any country that currently has a navy with submarines. To be relevant a document should give a specific number of submarines, but not necessarily its entire fleet of submarines (although, that is our ultimate goal). A report of a French submarine suffering a mishap in the North sea would not be relevant. However, a report of a new submarine being built in Shanghai that contains other valuable information, such as “this is the third reported unit constructed at this base” would be relevant. Any information that would be considered useful as an intelligence tool in determining a country’s submarine order of battle would be relevant. </top> SGML Markup Short Phrase Sentence Paragraph</p> <h2>How Test Runs are Evaluated</h2> <p> <strong>First ranked doc is relevant, which is 10% of the total relevant. Therefore Precision at the 10% Recall level is 100%</strong>  <strong>Next Relevant gives us 66% Precision at 20% recall level</strong>  <strong>Etc….</strong></p> <h3>1. d 123 *</h3> <h3>2. d 84</h3> <h3>3. d 56 *</h3> <h3>4. d 6</h3> <h3>5. d 8</h3> <h3>6. d 9 *</h3> <h3>7. d 511</h3> <h3>8. d 129</h3> <h3>9. d 187</h3> <h3>10. d 25 *</h3> <h3>11. d 38</h3> <h3>12. d 48</h3> <h3>13. d 250</h3> <h3>14. d 113</h3> <h3>15. d 3 *</h3> <p>Rq={d 3 ,d 5 ,d 9 ,d 25 ,d 39 ,d 44 ,d 56 ,d 71 ,d 89 ,d 123 } : 10 Relevant Example from Chapter 3 in MIR</p> <h2>Interpolation</h2> <h3> So, at recall levels 0%, 10%, 20%, and</h3> <h3>30% the interpolated precision is 33.3%</h3> <h3> At recall levels 40%, 50%, and 60%</h3> <h3>interpolated precision is 25%</h3> <h3> And at recall levels 70%, 80%, 90% and</h3> <h3>100%, interpolated precision is 20%</h3> <h3> Giving graph…</h3> <p><strong>X</strong></p> <h2>Interpolation</h2> <h5>P R E C I S I O N</h5> <h5>RECALL</h5> <p><strong>X X</strong></p> <h2>Computing Average Precision</h2> <p> <strong>We sum over the 5 seen relevant documents</strong>  <strong>(1 + 0.66 + 0.5 + 0.4 + 0.3)</strong>  <strong>We must divide by the number of relevant (10 docs)</strong></p> <ul> <li><strong>5 relevant document’s weren’t</strong> <strong>observed</strong>  <strong>Average Precision is 0.</strong>  <strong><em>Mean</em></strong> <strong>average precision is an average over multiple queries</strong></li> </ul> <h2>1. d 123 *</h2> <h2>2. d 84</h2> <h2>3. d 56 *</h2> <h2>4. d 6</h2> <h2>5. d 8</h2> <h2>6. d 9 *</h2> <h2>7. d 511</h2> <h2>8. d 129</h2> <h2>9. d 187</h2> <h2>10. d 25 *</h2> <h2>11. d 38</h2> <h2>12. d 48</h2> <h2>13. d 250</h2> <h2>14. d 113</h2> <h2>15. d 3 *</h2> <p>Rq={d 3 ,d 5 ,d 9 ,d 25 ,d 39 ,d 44 ,d 56 ,d 71 ,d 89 ,d 123 } : 10 Relevant</p> <h2>Using TREC_EVAL</h2> <h4> Developed from SMART evaluation programs</h4> <h4>for use in TREC</h4> <ul> <li><strong>trec_eval [-q] [-a] [-d] trec_qrel_file top_ranked_file</strong></li> </ul> <h4> Uses:</h4> <ul> <li><strong>List of top-ranked documents</strong> <ul> <li><strong>QID iter docno rank sim runid</strong></li> <li><strong>030 Q0 ZF08-175-870 0 4238 prise</strong></li> </ul></li> <li><strong>QRELS file for collection</strong> <ul> <li><strong>QID docno rel</strong></li> <li><strong>251 0 FT911-1003 1</strong></li> <li><strong>251 0 FT911-101 1</strong></li> <li><strong>251 0 FT911-1300 0</strong></li> </ul></li> </ul> <h2>Query Refinement</h2> <h3> Global Methods</h3> <ul> <li><strong>Context independent thesaurus expansion</strong></li> </ul> <h3> Local Methods</h3> <ul> <li><strong>Manual Feedback</strong></li> <li><strong>Automated Relevance Feedback</strong></li> </ul> <h2>Query Modification</h2> <h3> Problem: how to reformulate the query?</h3> <ul> <li><strong>Thesaurus expansion:</strong> <ul> <li><strong>Suggest terms similar to query terms</strong></li> <li><strong>Global method: see car, add ‘automobile’</strong></li> </ul></li> <li><strong>Relevance feedback:</strong> <ul> <li><strong>Suggest terms (and documents) similar to</strong> <strong>retrieved documents that have been judged (by a</strong> <strong>user) to be relevant (or use top k documents)</strong></li> <li><strong>Local method: based on retrieved document set</strong></li> </ul></li> <li><strong>Term re-weighting</strong></li> </ul> <h2>Thesauri</h2> <h3> Some electronic thesauri exist</h3> <ul> <li><strong>E.g., Roget’s</strong></li> <li><strong>Domain specific thesauri (e.g., chemistry)</strong> <ul> <li><strong>might map NaCl, salt, sodium chloride</strong></li> </ul></li> </ul> <h3> Other approach is to induce one from a</h3> <h3>collection of text, statistically</h3> <ul> <li><strong>Can capture synonymy</strong></li> <li><strong>But, might also get other word relations</strong></li> </ul> <h3> My opinion</h3> <ul> <li><strong>Thesaurus-based query expansion is best</strong></li> </ul> <h4>with controlled vocabulary search</h4> <h2>Automagically building thesauri</h2> <h3> Church (“One Term or Two”, SIGIR 1995)</h3> <h3>looked at correlations between forms of</h3> <h3>words in texts</h3> <h3> If hostage and hostages both occur in a</h3> <h3>document</h3> <ul> <li><strong>Worth more than single occurrence, but less</strong></li> </ul> <h4>than two. Possibly 1.</h4> <h3> Church proposes novel term weighting</h3> <h3>scheme</h3> <p><strong>hostages! hostages hostage 619 479 ! hostage 648 78,</strong></p> <h2>Lexical Associations</h2> <p> <strong>Subjects write first word that comes to mind</strong></p> <ul> <li><strong>doctor/nurse; black/white (Palermo & Jenkins 64)</strong></li> <li><strong>Text corpora yield similar associations</strong>  <strong>One measure: Mutual Information</strong></li> <li><strong>See Church and Hanks (Comp. Ling. ’90)</strong>  <strong>If word occurrences are independent, the numerator and denominator become equal</strong></li> <li><strong>when measured across a large collection</strong></li> </ul> <h2>Associations with “Doctor”</h2> <h4>(AP Corpus, N=15 million, Church & Hanks)</h4> <p><strong>MI(x,y) f(x,y) f(x) x f(y) y</strong> 11.3 12 111 honorary 621 doctor 11.3 8 1105 doctors 44 dentists 10.7 30 1105 doctors 241 nurses 9.4 8 1105 doctors 154 treating 9.0 6 275 examined 621 doctor 8.9 11 11.05 doctors 317 treat 8.7 25 621 doctor 1407 bills 0.96 6 621 doctor 73785 with 0.95 41 284690 a 1105 doctors 0.93 12 84716 is 1105 doctors</p> <h2>Term Similarity Calculations</h2> <p> <strong>Multiple metrics possible</strong></p> <ul> <li><strong>See Chung & Lee, JASIST</strong> <strong>52(4), 2001 for comparison</strong>  <strong>Contingency table Present Absent Present a b a+b Absent c d c+d a+c b+d N snow plow</strong></li> </ul> <h2>Relevance Feedback</h2> <h3> Main Idea:</h3> <ul> 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h6 y9 ff3 fs2 fc0 sc0 ls8 ws0\"\u003e\u003cspan class=\"ff4 ls0\"\u003e \u003c/span\u003e\u003cspan class=\"ff2 fs3 ws1\"\u003eQuery Refinement \u003c/span\u003e\u003c/div\u003e\u003cdiv class=\"t m0 x5 h7 ya ff5 fs0 fc0 sc0 ls1\"\u003e•\u003cspan class=\"ff4 ls2\"\u003e \u003cspan class=\"ff2 ls8 ws1\"\u003e(IIR Chapter 9) \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv class=\"t m0 x6 h8 yb ff5 fs4 fc0 sc0 ls3\"\u003e–\u003cspan class=\"ff4 ls4\"\u003e \u003cspan class=\"ff2 ls8 ws1\"\u003eQuery Expansion \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv class=\"t m0 x6 h8 yc ff5 fs4 fc0 sc0 ls3\"\u003e–\u003cspan class=\"ff4 ls4\"\u003e \u003cspan class=\"ff2 ls9 ws1\"\u003eRelevance Feedback \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"c x3 yd w3 h4\"\u003e\u003cdiv class=\"t m0 x4 h5 y4 ff2 fs1 fc0 sc0 lsa ws1\"\u003eRelevance \u003c/div\u003e\u003cdiv class=\"t m0 x4 h6 y5 ff3 fs2 fc0 sc0 ls8 ws0\"\u003e\u003cspan class=\"ff4 ls0\"\u003e \u003c/span\u003e\u003cspan class=\"ff2 fs3 ws1\"\u003eIn what ways can a document be relevant \u003c/span\u003e\u003c/div\u003e\u003cdiv class=\"t m0 x7 h6 ye ff2 fs3 fc0 sc0 ls8 ws1\"\u003eto a query? \u003c/div\u003e\u003cdiv class=\"t m0 x6 h8 yf ff5 fs4 fc0 sc0 ls3\"\u003e–\u003cspan class=\"ff4 ls4\"\u003e \u003cspan class=\"ff2 ls8 ws1\"\u003eAnswer question precisely (or partially) \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv class=\"t m0 x6 h8 y10 ff5 fs4 fc0 sc0 ls3\"\u003e–\u003cspan class=\"ff4 ls4\"\u003e \u003cspan class=\"ff2 ls8 ws1\"\u003eSuggest a source for more information \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv class=\"t m0 x6 h8 y11 ff5 fs4 fc0 sc0 ls3\"\u003e–\u003cspan class=\"ff4 ls4\"\u003e \u003cspan class=\"ff2 ls8 ws1\"\u003eGive background information \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv class=\"t m0 x6 h8 y12 ff5 fs4 fc0 sc0 ls3\"\u003e–\u003cspan class=\"ff4 ls4\"\u003e \u003cspan class=\"ff2 ls8 ws1\"\u003eRemind the user of other knowledge \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv class=\"t m0 x4 h6 y13 ff3 fs2 fc0 sc0 ls8 ws0\"\u003e\u003cspan class=\"ff4 ls0\"\u003e \u003cspan class=\"ff2 fs3 ls5 ws1\"\u003eDegree of Relevance \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv class=\"t m0 x6 h8 y14 ff5 fs4 fc0 sc0 ls3\"\u003e–\u003cspan class=\"ff4 ls4\"\u003e \u003cspan class=\"ff2 ls8 ws1\"\u003eBinary: relevant or not? \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv class=\"t m0 x6 h8 y15 ff5 fs4 fc0 sc0 ls3\"\u003e–\u003cspan class=\"ff4 ls4\"\u003e \u003cspan class=\"ff2 ls8 ws1\"\u003eOn a scale: 0-100 \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv class=\"t m0 x4 h6 y16 ff3 fs2 fc0 sc0 ls8 ws0\"\u003e\u003cspan class=\"ff4 ls0\"\u003e \u003c/span\u003e\u003cspan class=\"ff2 fs3 ws1\"\u003eIntrinsic or Subjective 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\u003c/ul\u003e \u003ch3\u003e Query Refinement\u003c/h3\u003e \u003cul\u003e \u003cli\u003e\u003cstrong\u003e(IIR Chapter 9)\u003c/strong\u003e\u003c/li\u003e \u003cli\u003e\u003cstrong\u003eQuery Expansion\u003c/strong\u003e\u003c/li\u003e \u003cli\u003e\u003cstrong\u003eRelevance Feedback\u003c/strong\u003e\u003c/li\u003e \u003c/ul\u003e \u003ch2\u003eRelevance\u003c/h2\u003e \u003ch3\u003e In what ways can a document be relevant\u003c/h3\u003e \u003ch3\u003eto a query?\u003c/h3\u003e \u003cul\u003e \u003cli\u003e\u003cstrong\u003eAnswer question precisely (or partially)\u003c/strong\u003e\u003c/li\u003e \u003cli\u003e\u003cstrong\u003eSuggest a source for more information\u003c/strong\u003e\u003c/li\u003e \u003cli\u003e\u003cstrong\u003eGive background information\u003c/strong\u003e\u003c/li\u003e \u003cli\u003e\u003cstrong\u003eRemind the user of other knowledge\u003c/strong\u003e\u003c/li\u003e \u003c/ul\u003e \u003ch3\u003e Degree of Relevance\u003c/h3\u003e \u003cul\u003e \u003cli\u003e\u003cstrong\u003eBinary: relevant or not?\u003c/strong\u003e\u003c/li\u003e \u003cli\u003e\u003cstrong\u003eOn a scale: 0-\u003c/strong\u003e\u003c/li\u003e \u003c/ul\u003e \u003ch3\u003e Intrinsic or Subjective\u003c/h3\u003e \u003ch2\u003eWhat to Evaluate?\u003c/h2\u003e \u003ch3\u003e What can be measured that reflects\u003c/h3\u003e \u003ch3\u003eusers’ ability to use system? (Cleverdon 66)\u003c/h3\u003e \u003cul\u003e \u003cli\u003e\u003cstrong\u003eCoverage of Information\u003c/strong\u003e\u003c/li\u003e \u003cli\u003e\u003cstrong\u003eForm of Presentation\u003c/strong\u003e\u003c/li\u003e \u003cli\u003e\u003cstrong\u003eEffort required/Ease of Use\u003c/strong\u003e\u003c/li\u003e \u003cli\u003e\u003cstrong\u003eTime and Space Efficiency\u003c/strong\u003e\u003c/li\u003e \u003cli\u003e\u003cstrong\u003eRecall\u003c/strong\u003e \u003cul\u003e \u003cli\u003e\u003cstrong\u003eproportion of relevant material actually retrieved\u003c/strong\u003e\u003c/li\u003e \u003c/ul\u003e\u003c/li\u003e \u003cli\u003e\u003cstrong\u003ePrecision\u003c/strong\u003e \u003cul\u003e \u003cli\u003e\u003cstrong\u003eproportion of retrieved material actually relevant\u003c/strong\u003e\u003c/li\u003e \u003c/ul\u003e\u003c/li\u003e \u003c/ul\u003e \u003ch2\u003eProblems with Precision/Recall\u003c/h2\u003e \u003ch3\u003e Can’t know true recall value\u003c/h3\u003e \u003cul\u003e \u003cli\u003e\u003cstrong\u003eexcept in small collections\u003c/strong\u003e\u003c/li\u003e \u003c/ul\u003e \u003ch3\u003e Precision/Recall measure different\u003c/h3\u003e \u003ch3\u003easpects of search quality\u003c/h3\u003e \u003cul\u003e \u003cli\u003e\u003cstrong\u003eA combined measure sometimes is more\u003c/strong\u003e\u003c/li\u003e \u003c/ul\u003e \u003ch4\u003eappropriate\u003c/h4\u003e \u003ch3\u003e Focused somewhat on set evaluation vs.\u003c/h3\u003e \u003ch3\u003eranked lists\u003c/h3\u003e \u003cp\u003e\u003cstrong\u003ePrecision/Recall Curves\u003c/strong\u003e  \u003cstrong\u003eThere is a tradeoff between precision and recall\u003c/strong\u003e  \u003cstrong\u003eSo measure precision at different levels of recall\u003c/strong\u003e\u003c/p\u003e \u003cul\u003e \u003cli\u003e\u003cstrong\u003eInterpolate\u003c/strong\u003e\u003c/li\u003e \u003cli\u003e\u003cstrong\u003eAverage over multiple queries\u003c/strong\u003e precision recall x x x x x x x x x x x Which of these three curves is better?\u003c/li\u003e \u003c/ul\u003e \u003ch2\u003eExisting Test Collections\u003c/h2\u003e \u003ch4\u003e Historical, Small\u003c/h4\u003e \u003ch5\u003e– CACM (3204), CISI (1460), CRAN (1397)\u003c/h5\u003e \u003ch5\u003e– INSPEC (12684), MED (1033), REUTERS (21578)\u003c/h5\u003e \u003ch4\u003e More recent\u003c/h4\u003e \u003cul\u003e \u003cli\u003e\u003cstrong\u003eTREC: 1.5M newsprint; 10/18/100GB Web; 1 TB\u003c/strong\u003e\u003c/li\u003e \u003cli\u003e\u003cstrong\u003eCLEF: 1M+ newspaper articles from 1994 – 1995, 13 langs\u003c/strong\u003e\u003c/li\u003e \u003cli\u003e\u003cstrong\u003eNTCIR: Asian languages (JP, KR, ZH)\u003c/strong\u003e\u003c/li\u003e \u003cli\u003e\u003cstrong\u003eFIRE: Hindi, Marathi, Bengali (about 100k docs each)\u003c/strong\u003e\u003c/li\u003e \u003cli\u003e\u003cstrong\u003eReuters RV1 (880,000)\u003c/strong\u003e\u003c/li\u003e \u003c/ul\u003e \u003ch2\u003eText REtrieval Conference (TREC)\u003c/h2\u003e \u003ch4\u003e Annual bake-off for text retrieval systems\u003c/h4\u003e \u003ch4\u003e Sponsored by\u003c/h4\u003e \u003ch4\u003e Roughly 2.5 gigabytes of text, newswire\u003c/h4\u003e \u003cul\u003e \u003cli\u003e\u003cstrong\u003e50 “topics” (queries)\u003c/strong\u003e\u003c/li\u003e \u003cli\u003e\u003cstrong\u003eReturn top 1000 documents per topic (~80 groups)\u003c/strong\u003e\u003c/li\u003e \u003cli\u003e\u003cstrong\u003eResults judged by retired intelligence analysts\u003c/strong\u003e \u003cul\u003e \u003cli\u003e\u003cstrong\u003eDocuments are relevant or not\u003c/strong\u003e\u003c/li\u003e \u003c/ul\u003e\u003c/li\u003e \u003c/ul\u003e \u003ch4\u003e Numerous tracks\u003c/h4\u003e \u003cul\u003e \u003cli\u003e\u003cstrong\u003eCross-Language\u003c/strong\u003e\u003c/li\u003e \u003cli\u003e\u003cstrong\u003eSpoken Documents\u003c/strong\u003e\u003c/li\u003e \u003cli\u003e\u003cstrong\u003eQuestion Answering\u003c/strong\u003e\u003c/li\u003e \u003c/ul\u003e \u003ch4\u003e http://trec.nist.gov/\u003c/h4\u003e \u003ch2\u003eSample TREC Topic\u003c/h2\u003e \u003cp\u003e\u003ctop\u003e \u003cnum\u003e Number: 285 \u003ctitle\u003e Topic: World submarine forces \u003cdesc\u003e Description: Determine the number of submarines, both nuclear-powered and conventional, presently in the inventories of all the countries in the world. \u003cnarr\u003e Narrative: We are looking for a count of operable submarines in any country that currently has a navy with submarines. To be relevant a document should give a specific number of submarines, but not necessarily its entire fleet of submarines (although, that is our ultimate goal). A report of a French submarine suffering a mishap in the North sea would not be relevant. However, a report of a new submarine being built in Shanghai that contains other valuable information, such as “this is the third reported unit constructed at this base” would be relevant. Any information that would be considered useful as an intelligence tool in determining a country’s submarine order of battle would be relevant. \u003c/top\u003e SGML Markup Short Phrase Sentence Paragraph\u003c/p\u003e \u003ch2\u003eHow Test Runs are Evaluated\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eFirst ranked doc is relevant, which is 10% of the total relevant. Therefore Precision at the 10% Recall level is 100%\u003c/strong\u003e  \u003cstrong\u003eNext Relevant gives us 66% Precision at 20% recall level\u003c/strong\u003e  \u003cstrong\u003eEtc….\u003c/strong\u003e\u003c/p\u003e \u003ch3\u003e1. d 123 *\u003c/h3\u003e \u003ch3\u003e2. d 84\u003c/h3\u003e \u003ch3\u003e3. d 56 *\u003c/h3\u003e \u003ch3\u003e4. d 6\u003c/h3\u003e \u003ch3\u003e5. d 8\u003c/h3\u003e \u003ch3\u003e6. d 9 *\u003c/h3\u003e \u003ch3\u003e7. d 511\u003c/h3\u003e \u003ch3\u003e8. d 129\u003c/h3\u003e \u003ch3\u003e9. d 187\u003c/h3\u003e \u003ch3\u003e10. d 25 *\u003c/h3\u003e \u003ch3\u003e11. d 38\u003c/h3\u003e \u003ch3\u003e12. d 48\u003c/h3\u003e \u003ch3\u003e13. d 250\u003c/h3\u003e \u003ch3\u003e14. d 113\u003c/h3\u003e \u003ch3\u003e15. d 3 *\u003c/h3\u003e \u003cp\u003eRq={d 3 ,d 5 ,d 9 ,d 25 ,d 39 ,d 44 ,d 56 ,d 71 ,d 89 ,d 123 } : 10 Relevant Example from Chapter 3 in MIR\u003c/p\u003e \u003ch2\u003eInterpolation\u003c/h2\u003e \u003ch3\u003e So, at recall levels 0%, 10%, 20%, and\u003c/h3\u003e \u003ch3\u003e30% the interpolated precision is 33.3%\u003c/h3\u003e \u003ch3\u003e At recall levels 40%, 50%, and 60%\u003c/h3\u003e \u003ch3\u003einterpolated precision is 25%\u003c/h3\u003e \u003ch3\u003e And at recall levels 70%, 80%, 90% and\u003c/h3\u003e \u003ch3\u003e100%, interpolated precision is 20%\u003c/h3\u003e \u003ch3\u003e Giving graph…\u003c/h3\u003e \u003cp\u003e\u003cstrong\u003eX\u003c/strong\u003e\u003c/p\u003e \u003ch2\u003eInterpolation\u003c/h2\u003e \u003ch5\u003eP R E C I S I O N\u003c/h5\u003e \u003ch5\u003eRECALL\u003c/h5\u003e \u003cp\u003e\u003cstrong\u003eX X\u003c/strong\u003e\u003c/p\u003e \u003ch2\u003eComputing Average Precision\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eWe sum over the 5 seen relevant documents\u003c/strong\u003e  \u003cstrong\u003e(1 + 0.66 + 0.5 + 0.4 + 0.3)\u003c/strong\u003e  \u003cstrong\u003eWe must divide by the number of relevant (10 docs)\u003c/strong\u003e\u003c/p\u003e \u003cul\u003e \u003cli\u003e\u003cstrong\u003e5 relevant document’s weren’t\u003c/strong\u003e \u003cstrong\u003eobserved\u003c/strong\u003e  \u003cstrong\u003eAverage Precision is 0.\u003c/strong\u003e  \u003cstrong\u003e\u003cem\u003eMean\u003c/em\u003e\u003c/strong\u003e \u003cstrong\u003eaverage precision is an average over multiple queries\u003c/strong\u003e\u003c/li\u003e \u003c/ul\u003e \u003ch2\u003e1. d 123 *\u003c/h2\u003e \u003ch2\u003e2. d 84\u003c/h2\u003e \u003ch2\u003e3. d 56 *\u003c/h2\u003e \u003ch2\u003e4. d 6\u003c/h2\u003e \u003ch2\u003e5. d 8\u003c/h2\u003e \u003ch2\u003e6. d 9 *\u003c/h2\u003e \u003ch2\u003e7. d 511\u003c/h2\u003e \u003ch2\u003e8. d 129\u003c/h2\u003e \u003ch2\u003e9. d 187\u003c/h2\u003e \u003ch2\u003e10. d 25 *\u003c/h2\u003e \u003ch2\u003e11. d 38\u003c/h2\u003e \u003ch2\u003e12. d 48\u003c/h2\u003e \u003ch2\u003e13. d 250\u003c/h2\u003e \u003ch2\u003e14. d 113\u003c/h2\u003e \u003ch2\u003e15. d 3 *\u003c/h2\u003e \u003cp\u003eRq={d 3 ,d 5 ,d 9 ,d 25 ,d 39 ,d 44 ,d 56 ,d 71 ,d 89 ,d 123 } : 10 Relevant\u003c/p\u003e \u003ch2\u003eUsing TREC_EVAL\u003c/h2\u003e \u003ch4\u003e Developed from SMART evaluation programs\u003c/h4\u003e \u003ch4\u003efor use in TREC\u003c/h4\u003e \u003cul\u003e \u003cli\u003e\u003cstrong\u003etrec_eval [-q] [-a] [-d] trec_qrel_file top_ranked_file\u003c/strong\u003e\u003c/li\u003e \u003c/ul\u003e \u003ch4\u003e Uses:\u003c/h4\u003e \u003cul\u003e \u003cli\u003e\u003cstrong\u003eList of top-ranked documents\u003c/strong\u003e \u003cul\u003e \u003cli\u003e\u003cstrong\u003eQID iter docno rank sim runid\u003c/strong\u003e\u003c/li\u003e \u003cli\u003e\u003cstrong\u003e030 Q0 ZF08-175-870 0 4238 prise\u003c/strong\u003e\u003c/li\u003e \u003c/ul\u003e\u003c/li\u003e \u003cli\u003e\u003cstrong\u003eQRELS file for collection\u003c/strong\u003e \u003cul\u003e \u003cli\u003e\u003cstrong\u003eQID docno rel\u003c/strong\u003e\u003c/li\u003e \u003cli\u003e\u003cstrong\u003e251 0 FT911-1003 1\u003c/strong\u003e\u003c/li\u003e \u003cli\u003e\u003cstrong\u003e251 0 FT911-101 1\u003c/strong\u003e\u003c/li\u003e \u003cli\u003e\u003cstrong\u003e251 0 FT911-1300 0\u003c/strong\u003e\u003c/li\u003e \u003c/ul\u003e\u003c/li\u003e \u003c/ul\u003e \u003ch2\u003eQuery Refinement\u003c/h2\u003e \u003ch3\u003e Global Methods\u003c/h3\u003e \u003cul\u003e \u003cli\u003e\u003cstrong\u003eContext independent thesaurus expansion\u003c/strong\u003e\u003c/li\u003e \u003c/ul\u003e \u003ch3\u003e Local Methods\u003c/h3\u003e \u003cul\u003e \u003cli\u003e\u003cstrong\u003eManual Feedback\u003c/strong\u003e\u003c/li\u003e \u003cli\u003e\u003cstrong\u003eAutomated Relevance Feedback\u003c/strong\u003e\u003c/li\u003e \u003c/ul\u003e \u003ch2\u003eQuery Modification\u003c/h2\u003e \u003ch3\u003e Problem: how to reformulate the query?\u003c/h3\u003e \u003cul\u003e \u003cli\u003e\u003cstrong\u003eThesaurus expansion:\u003c/strong\u003e \u003cul\u003e \u003cli\u003e\u003cstrong\u003eSuggest terms similar to query terms\u003c/strong\u003e\u003c/li\u003e \u003cli\u003e\u003cstrong\u003eGlobal method: see car, add ‘automobile’\u003c/strong\u003e\u003c/li\u003e \u003c/ul\u003e\u003c/li\u003e \u003cli\u003e\u003cstrong\u003eRelevance feedback:\u003c/strong\u003e \u003cul\u003e \u003cli\u003e\u003cstrong\u003eSuggest terms (and documents) similar to\u003c/strong\u003e \u003cstrong\u003eretrieved documents that have been judged (by a\u003c/strong\u003e \u003cstrong\u003euser) to be relevant (or use top k documents)\u003c/strong\u003e\u003c/li\u003e \u003cli\u003e\u003cstrong\u003eLocal method: based on retrieved document set\u003c/strong\u003e\u003c/li\u003e \u003c/ul\u003e\u003c/li\u003e \u003cli\u003e\u003cstrong\u003eTerm re-weighting\u003c/strong\u003e\u003c/li\u003e \u003c/ul\u003e \u003ch2\u003eThesauri\u003c/h2\u003e \u003ch3\u003e Some electronic thesauri exist\u003c/h3\u003e \u003cul\u003e \u003cli\u003e\u003cstrong\u003eE.g., Roget’s\u003c/strong\u003e\u003c/li\u003e \u003cli\u003e\u003cstrong\u003eDomain specific thesauri (e.g., chemistry)\u003c/strong\u003e \u003cul\u003e \u003cli\u003e\u003cstrong\u003emight map NaCl, salt, sodium chloride\u003c/strong\u003e\u003c/li\u003e \u003c/ul\u003e\u003c/li\u003e \u003c/ul\u003e \u003ch3\u003e Other approach is to induce one from a\u003c/h3\u003e \u003ch3\u003ecollection of text, statistically\u003c/h3\u003e \u003cul\u003e \u003cli\u003e\u003cstrong\u003eCan capture synonymy\u003c/strong\u003e\u003c/li\u003e \u003cli\u003e\u003cstrong\u003eBut, might also get other word relations\u003c/strong\u003e\u003c/li\u003e \u003c/ul\u003e \u003ch3\u003e My opinion\u003c/h3\u003e \u003cul\u003e \u003cli\u003e\u003cstrong\u003eThesaurus-based query expansion is best\u003c/strong\u003e\u003c/li\u003e \u003c/ul\u003e \u003ch4\u003ewith controlled vocabulary search\u003c/h4\u003e \u003ch2\u003eAutomagically building thesauri\u003c/h2\u003e \u003ch3\u003e Church (“One Term or Two”, SIGIR 1995)\u003c/h3\u003e \u003ch3\u003elooked at correlations between forms of\u003c/h3\u003e \u003ch3\u003ewords in texts\u003c/h3\u003e \u003ch3\u003e If hostage and hostages both occur in a\u003c/h3\u003e \u003ch3\u003edocument\u003c/h3\u003e \u003cul\u003e \u003cli\u003e\u003cstrong\u003eWorth more than single occurrence, but less\u003c/strong\u003e\u003c/li\u003e \u003c/ul\u003e \u003ch4\u003ethan two. Possibly 1.\u003c/h4\u003e \u003ch3\u003e Church proposes novel term weighting\u003c/h3\u003e \u003ch3\u003escheme\u003c/h3\u003e \u003cp\u003e\u003cstrong\u003ehostages! hostages hostage 619 479 ! hostage 648 78,\u003c/strong\u003e\u003c/p\u003e \u003ch2\u003eLexical Associations\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eSubjects write first word that comes to mind\u003c/strong\u003e\u003c/p\u003e \u003cul\u003e \u003cli\u003e\u003cstrong\u003edoctor/nurse; black/white (Palermo \u0026amp; Jenkins 64)\u003c/strong\u003e\u003c/li\u003e \u003cli\u003e\u003cstrong\u003eText corpora yield similar associations\u003c/strong\u003e  \u003cstrong\u003eOne measure: Mutual Information\u003c/strong\u003e\u003c/li\u003e \u003cli\u003e\u003cstrong\u003eSee Church and Hanks (Comp. Ling. ’90)\u003c/strong\u003e  \u003cstrong\u003eIf word occurrences are independent, the numerator and denominator become equal\u003c/strong\u003e\u003c/li\u003e \u003cli\u003e\u003cstrong\u003ewhen measured across a large collection\u003c/strong\u003e\u003c/li\u003e \u003c/ul\u003e \u003ch2\u003eAssociations with “Doctor”\u003c/h2\u003e \u003ch4\u003e(AP Corpus, N=15 million, Church \u0026amp; Hanks)\u003c/h4\u003e \u003cp\u003e\u003cstrong\u003eMI(x,y) f(x,y) f(x) x f(y) y\u003c/strong\u003e 11.3 12 111 honorary 621 doctor 11.3 8 1105 doctors 44 dentists 10.7 30 1105 doctors 241 nurses 9.4 8 1105 doctors 154 treating 9.0 6 275 examined 621 doctor 8.9 11 11.05 doctors 317 treat 8.7 25 621 doctor 1407 bills 0.96 6 621 doctor 73785 with 0.95 41 284690 a 1105 doctors 0.93 12 84716 is 1105 doctors\u003c/p\u003e \u003ch2\u003eTerm Similarity Calculations\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eMultiple metrics possible\u003c/strong\u003e\u003c/p\u003e \u003cul\u003e \u003cli\u003e\u003cstrong\u003eSee Chung \u0026amp; Lee, JASIST\u003c/strong\u003e \u003cstrong\u003e52(4), 2001 for comparison\u003c/strong\u003e  \u003cstrong\u003eContingency table Present Absent Present a b a+b Absent c d c+d a+c b+d N snow plow\u003c/strong\u003e\u003c/li\u003e \u003c/ul\u003e \u003ch2\u003eRelevance Feedback\u003c/h2\u003e \u003ch3\u003e Main Idea:\u003c/h3\u003e \u003cul\u003e \u003cli\u003e\u003cstrong\u003eModify existing query based on relevance\u003c/strong\u003e\u003c/li\u003e \u003c/ul\u003e \u003ch4\u003ejudgments\u003c/h4\u003e \u003cul\u003e \u003cli\u003e\u003cstrong\u003eExtract terms from relevant documents and add\u003c/strong\u003e \u003cstrong\u003ethem to the query\u003c/strong\u003e\u003c/li\u003e \u003cli\u003e\u003cstrong\u003eand/or re-weight the terms already in the query\u003c/strong\u003e\u003c/li\u003e \u003cli\u003e\u003cstrong\u003eManually\u003c/strong\u003e\u003c/li\u003e \u003cli\u003e\u003cstrong\u003eUsers select relevant documents\u003c/strong\u003e\u003c/li\u003e \u003cli\u003e\u003cstrong\u003eUsers/system select terms from an automatically- generated list\u003c/strong\u003e\u003c/li\u003e \u003cli\u003e\u003cstrong\u003eAutomated (blind/pseudo) rel. feedback\u003c/strong\u003e\u003c/li\u003e \u003cli\u003e\u003cstrong\u003eAssume top\u003c/strong\u003e \u003cstrong\u003e\u003cem\u003ek\u003c/em\u003e\u003c/strong\u003e \u003cstrong\u003edocs are relevant (e.g., 5 to 20)\u003c/strong\u003e\u003c/li\u003e \u003c/ul\u003e ","relatedDocs":[{"id":38661,"lang":"en","slug":"information-retrieval-5-exercises-computer-science","title":"Information Retrieval 5, Exercises - Computer Science","isStore":false,"isPrime":false,"isTop":false,"averageVotes":"4.5","reviewsCount":2},{"id":38687,"lang":"en","slug":"information-retrieval-tolerant-retrieval-lecture-slides-computer-science","title":"Information Retrieval Tolerant Retrieval, Lecture Slides - Computer Science","isStore":false,"isPrime":false,"isTop":false,"averageVotes":"0.0","reviewsCount":0},{"id":38658,"lang":"en","slug":"information-retrieval-3-exercises-computer-science","title":"Information Retrieval 3, Exercises - Computer Science","isStore":false,"isPrime":false,"isTop":false,"averageVotes":"0.0","reviewsCount":0},{"id":38655,"lang":"en","slug":"information-retrieval-2-exercises-computer-science","title":"Information Retrieval 2, Exercises - Computer Science","isStore":false,"isPrime":false,"isTop":false,"averageVotes":"0.0","reviewsCount":0},{"id":38676,"lang":"en","slug":"information-retrieval-overview-lecture-slides-computer-science","title":"Information Retrieval Overview, Lecture Slides - Computer Science","isStore":false,"isPrime":false,"isTop":false,"averageVotes":"0.0","reviewsCount":0},{"id":38660,"lang":"en","slug":"information-retrieval-4-exercises-computer-science","title":"Information Retrieval 4, Exercises - Computer Science","isStore":false,"isPrime":false,"isTop":false,"averageVotes":"0.0","reviewsCount":0},{"id":38663,"lang":"en","slug":"information-retrieval-6-exercises-computer-science","title":"Information Retrieval 6, Exercises - Computer Science","isStore":false,"isPrime":false,"isTop":false,"averageVotes":"0.0","reviewsCount":0},{"id":38667,"lang":"en","slug":"information-retrieval-and-nlp-lecture-slides-computer-science","title":"Information Retrieval and NLP, Lecture Slides - Computer Science","isStore":false,"isPrime":false,"isTop":false,"averageVotes":"0.0","reviewsCount":0},{"id":38681,"lang":"en","slug":"information-retrieval-7-exercises-computer-science","title":"Information Retrieval 7, Exercises - Computer Science","isStore":false,"isPrime":false,"isTop":false,"averageVotes":"0.0","reviewsCount":0},{"id":38653,"lang":"en","slug":"information-retrieval-and-question-answering-lecture-slides-computer-science","title":"Information Retrieval and Question Answering, Lecture Slides - Computer Science","isStore":false,"isPrime":false,"isTop":false,"averageVotes":"0.0","reviewsCount":0},{"id":38670,"lang":"en","slug":"information-retrieval-distributed-retrieval-lecture-slides-computer-science","title":"Distributed Info Retrieval: Algorithms for Partitioning, Files, and Queries","isStore":false,"isPrime":false,"isTop":false,"averageVotes":"0.0","reviewsCount":0},{"id":38678,"lang":"en","slug":"information-retrieval-search-and-multilinguality-lecture-slides-computer-science","title":"Cross-Language Information Retrieval: N-grams and Query Expansion","isStore":false,"isPrime":false,"isTop":false,"averageVotes":"0.0","reviewsCount":0}],"suggestedDocs":[]},"__N_SSP":true},"page":"/docs","query":{"slug":"information-retrieval-performance-lecture-slides-computer-science","id":"38683"},"buildId":"88ab84EjVlt2ImlLvPoah","assetPrefix":"https://assets.docsity.com/mf/docs/4.10.13","isFallback":false,"isExperimentalCompile":false,"gssp":true,"appGip":true,"locale":"en","locales":["default","it","en","es","pt","fr","pl","ru","sr","de"],"defaultLocale":"default","scriptLoader":[]}</script></body></html>