Full-Text Indexing

1. Full-Text Indexing Session 10 INFM 718N Web-Enabled Databases

2. Agenda • How to do it • How it works • The “A” Team

3. (PC) Interface Design (IE, Firefox) Client-side Programming (JavaScript) Interaction Design Interchange Language (HTML, XML) Server-side Programming (PHP) Business rules (MySQL) (PC, Unix) • Relational normalization • Structured programming • Software patterns • Object-oriented design • Functional decomposition Client Hardware Web Browser Database Server Hardware

4. Full-Text Indexing in MySQL • Create a MyISAM table (not InnoDB!) • Include a CHAR, VARCHAR, or TEXT field • Text fields can hold a bit over 10,000 words • Create a FULLTEXT index • ALTER TABLE x ADD FULLTEXT INDEX y; • Issue a (ranked) query • SELECT y FROM x WHERE MATCH y AGAINST (‘cat’);

5. Other Types of Queries • Automatic (ranked) vocabulary expansion • SELECT y FROM x WHERE MATCH y AGAINST (‘cat’ WITH QUERY EXPANSION); • Boolean (unranked) search • SELECT y FROM x WHERE MATCH y AGAINST (‘+cat -dog’ IN BOOLEAN MODE);

6. Query Details • No more than 254 characters (~40 words) • Longer queries take more time • Multiple words are implicitly joined by “OR” • Boolean queries can use (unnested) operators • Words preceded by “+” must occur (AND) • Words preceded by “-” must not occur (AND NOT)

7. What’s a “Word?” • Delimited by “white space” or “-” • White-space includes space, tab, newline, … • Not case sensitive • Exact string match • No “stemming” (automatic truncation) • Boolean search has additional options • Truncation (e.g., time*) • Phrases (e.g., “cats and dogs”)

8. Unsearchable Words • Very common words • Those that appear in more than 50% of docs • Words of 3 or fewer characters • Rarely are topically specific • Other “stopwords” • able about above according accordingly across actually after afterwards again against ain't …

9. Human-Machine Synergy • Machines are good at: • Doing simple things accurately and quickly • Scaling to larger collections in sublinear time • People are better at: • Accurately recognizing what they are looking for • Evaluating intangibles such as “quality” • Both are pretty bad at: • Mapping consistently between words and concepts

10. Predict Nominate IR System Query Formulation Query Search Ranked List Selection Query Reformulation and Relevance Feedback Document Examination Document Source Reselection Delivery Supporting the Search Process Source Selection Choose

11. IR System Query Formulation Query Search Ranked List Selection Document Indexing Index Examination Document Acquisition Collection Delivery Supporting the Search Process Source Selection

12. Taylor’s Model of Question Formation Q1 Visceral Need Q2 Conscious Need Intermediated Search End-user Search Q3 Formalized Need Q4 Compromised Need (Query)

13. Search Goal • Choose the same documents a human would • Without human intervention (less work) • Faster than a human could (less time) • As accurately as possible (less accuracy) • Humans start with an information need • Machines start with a query • Humans match documents to information needs • Machines match document & query representations

14. Search Component Model Utility Human Judgment Information Need Document Query Formulation Query Document Processing Query Processing Representation Function Representation Function Query Representation Document Representation Comparison Function Retrieval Status Value

15. Relevance • Relevance relates a topic and a document • Duplicates are equally relevant, by definition • Constant over time and across users • Pertinence relates a task and a document • Accounts for quality, complexity, language, … • Utility relates a user and a document • Accounts for prior knowledge • We seek utility, but relevance is what we get!

16. Problems With Word Matching • Word matching suffers from two problems • Synonymy: paper vs. article • Homonymy: bank (river) vs. bank (financial) • Disambiguation in IR: seek to resolve homonymy • Index word senses rather than words • Synonymy usually addressed by • Thesaurus-based query expansion • Latent semantic indexing

17. “Bag of Terms” Representation • Bag = a “set” that can contain duplicates • “The quick brown fox jumped over the lazy dog’s back”  {back, brown, dog, fox, jump, lazy, over, quick, the, the} • Vector = values recorded in any consistent order • {back, brown, dog, fox, jump, lazy, over, quick, the, the}  [1 1 1 1 1 1 1 1 2]

18. Bag of Terms Example Document 1 Stopword List Term Document 1 Document 2 The quick brown fox jumped over the lazy dog’s back. for aid 0 1 is all 0 1 of back 1 0 the brown 1 0 to come 0 1 dog 1 0 fox 1 0 Document 2 good 0 1 jump 1 0 lazy 1 0 Now is the time for all good men to come to the aid of their party. men 0 1 now 0 1 over 1 0 party 0 1 quick 1 0 their 0 1 time 0 1

19. Boolean IR • Strong points • Accurate, if you know the right strategies • Efficient for the computer • Weaknesses • Often results in too many documents, or none • Users must learn Boolean logic • Sometimes finds relationships that don’t exist • Words can have many meanings • Choosing the right words is sometimes hard

20. Proximity Operators • More precise versions of AND • “NEAR n” allows at most n-1 intervening terms • “WITH” requires terms to be adjacent and in order • Easy to implement, but less efficient • Store a list of positions for each word in each doc • Stopwords become very important! • Perform normal Boolean computations • Treat WITH and NEAR like AND with an extra constraint

21. Proximity Operator Example Term Doc 1 Doc 2 • time AND come • Doc 2 • time (NEAR 2) come • Empty • quick (NEAR 2) fox • Doc 1 • quick WITH fox • Empty aid 0 1 (13) all 0 1 (6) back 1 (10) 0 brown 1 (3) 0 come 0 1 (9) dog 1 (9) 0 fox 1 (4) 0 good 0 1 (7) jump 1 (5) 0 lazy 1 (8) 0 men 0 1 (8) now 0 1 (1) over 1 (6) 0 party 0 1 (16) quick 1 (2) 0 their 0 1 (15) time 0 1 (4)

22. Advantages of Ranked Retrieval • Closer to the way people think • Some documents are better than others • Enriches browsing behavior • Decide how far down the list to go as you read it • Allows more flexible queries • Long and short queries can produce useful results

23. Ranked Retrieval Challenges • “Best first” is easy to say but hard to do! • The best we can hope for is to approximate it • Will the user understand the process? • It is hard to use a tool that you don’t understand • Efficiency becomes a concern • Only a problem for long queries, though

24. Similarity-Based Queries • Treat the query as if it were a document • Create a query bag-of-words • Find the similarity of each document • Using the coordination measure, for example • Rank order the documents by similarity • Most similar to the query first • Surprisingly, this works pretty well! • Especially for very short queries

25. Counting Terms • Terms tell us about documents • If “rabbit” appears a lot, it may be about rabbits • Documents tell us about terms • “the” is in every document -- not discriminating • Documents are most likely described well by rare terms that occur in them frequently • Higher “term frequency” is stronger evidence • Low “collection frequency” makes it stronger still

26. The Document Length Effect • Humans look for documents with useful parts • But probabilities are computed for the whole • Document lengths vary in many collections • So probability calculations could be inconsistent • Two strategies • Adjust probability estimates for document length • Divide the documents into equal “passages”

27. Incorporating Term Frequency • High term frequency is evidence of meaning • And high IDF is evidence of term importance • Recompute the bag-of-words • Compute TF * IDF for every element

28. TF*IDF Example 1 2 3 4 1 2 3 4 Unweighted query: contaminated retrieval Result: 2, 3, 1, 4 5 2 1.51 0.60 complicated 0.301 4 1 3 0.50 0.13 0.38 contaminated 0.125 5 4 3 0.63 0.50 0.38 fallout 0.125 Weighted query: contaminated(3) retrieval(1) Result: 1, 3, 2, 4 6 3 3 2 information 0.000 1 0.60 interesting 0.602 3 7 0.90 2.11 nuclear 0.301 IDF-weighted query: contaminated retrieval Result: 2, 3, 1, 4 6 1 4 0.75 0.13 0.50 retrieval 0.125 2 1.20 siberia 0.602

29. Document Length Normalization • Long documents have an unfair advantage • They use a lot of terms • So they get more matches than short documents • And they use the same words repeatedly • So they have much higher term frequencies • Normalization seeks to remove these effects • Related somehow to maximum term frequency • But also sensitive to the of number of terms

30. “Okapi” Term Weights TF component IDF component

31. MySQL Term Weights • local weight = (log(tf)+1)/sumtf * U/(1+0.0115*U) • global weight = log((N-nf)/nf) • query weight = local weight * global weight * qf tf      How many times the term appears in the rowsumtf The sum of "(log(tf)+1)" for all terms in the same rowU        How many unique terms are in the rowN        How many rows are in the tablenf       How many rows contain the termqf       How many times the term appears in the query

32. Summary • Goal: find documents most similar to the query • Compute normalized document term weights • Some combination of TF, DF, and Length • Optionally, get query term weights from the user • Estimate of term importance • Compute inner product of query and doc vectors • Multiply corresponding elements and then add

33. The Indexing Process Postings Term Inverted File Doc 3 Doc 1 Doc 2 Doc 4 Doc 5 Doc 6 Doc 7 Doc 8 aid 0 0 0 1 0 0 0 1 4, 8 AI A all 0 1 0 1 0 1 0 0 2, 4, 6 AL back 1 0 1 0 0 0 1 0 1, 3, 7 BA B brown 1 0 1 0 1 0 1 0 1, 3, 5, 7 BR come 0 1 0 1 0 1 0 1 2, 4, 6, 8 C dog 0 0 1 0 1 0 0 0 3, 5 D fox 0 0 1 0 1 0 1 0 3, 5, 7 F good 0 1 0 1 0 1 0 1 2, 4, 6, 8 G jump 0 0 1 0 0 0 0 0 3 J lazy 1 0 1 0 1 0 1 0 1, 3, 5, 7 L men 0 1 0 1 0 0 0 1 2, 4, 8 M now 0 1 0 0 0 1 0 1 2, 6, 8 N over 1 0 1 0 1 0 1 1 1, 3, 5, 7, 8 O party 0 0 0 0 0 1 0 1 6, 8 P quick 1 0 1 0 0 0 0 0 1, 3 Q their 1 0 0 0 1 0 1 0 1, 5, 7 TH T time 0 1 0 1 0 1 0 0 2, 4, 6 TI

34. The Finished Product Term Inverted File Postings aid 4, 8 AI A all 2, 4, 6 AL back 1, 3, 7 BA B brown 1, 3, 5, 7 BR come 2, 4, 6, 8 C dog 3, 5 D fox 3, 5, 7 F good 2, 4, 6, 8 G jump 3 J lazy 1, 3, 5, 7 L men 2, 4, 8 M now 2, 6, 8 N over 1, 3, 5, 7, 8 O party 6, 8 P quick 1, 3 Q their 1, 5, 7 TH T time 2, 4, 6 TI

35. How Big Is the Postings File? • Very compact for Boolean retrieval • About 10% of the size of the documents • If an aggressive stopword list is used! • Not much larger for ranked retrieval • Perhaps 20% • Enormous for proximity operators • Sometimes larger than the documents!

36. Building an Inverted Index • Simplest solution is a single sorted array • Fast lookup using binary search • But sorting large files on disk is very slow • And adding one document means starting over • Tree structures allow easy insertion • But the worst case lookup time is linear • Balanced trees provide the best of both • Fast lookup and easy insertion • But they require 45% more disk space

37. How Big is the Inverted Index? • Typically smaller than the postings file • Depends on number of terms, not documents • Eventually, most terms will already be indexed • But the postings file will continue to grow • Postings dominate asymptotic space complexity • Linear in the number of documents

38. Summary • Slow indexing yields fast query processing • Key fact: most terms don’t appear in most documents • We use extra disk space to save query time • Index space is in addition to document space • Time and space complexity must be balanced • Disk block reads are the critical resource • This makes index compression a big win