1. 9/11/2000Information Organization and Retrieval Content Analysis and Statistical Properties of Text Ray Larson & Marti Hearst University of California, Berkeley School of Information Management and Systems SIMS 202: Information Organization and Retrieval

2. 9/11/2000Information Organization and Retrieval Today Overview of Content Analysis Text Representation Statistical Characteristics of Text Collections Zipf distribution Statistical dependence

3. 9/11/2000Information Organization and Retrieval Content Analysis Automated Transformation of raw text into a form that represent some aspect(s) of its meaning Including, but not limited to: –Automated Thesaurus Generation –Phrase Detection –Categorization –Clustering –Summarization

4. 9/11/2000Information Organization and Retrieval Techniques for Content Analysis Statistical –Single Document –Full Collection Linguistic –Syntactic –Semantic –Pragmatic Knowledge-Based (Artificial Intelligence) Hybrid (Combinations)

5. 9/11/2000Information Organization and Retrieval Text Processing Standard Steps: –Recognize document structure titles, sections, paragraphs, etc. –Break into tokens usually space and punctuation delineated special issues with Asian languages –Stemming/morphological analysis –Store in inverted index (to be discussed later)

6. Information need Index Pre-process Parse Collections Rank Query text input How is the query constructed? How is the text processed?

7. Information Organization and Retrieval Document Processing Steps

8. 9/11/2000Information Organization and Retrieval Stemming and Morphological Analysis Goal: “normalize” similar words Morphology (“form” of words) –Inflectional Morphology E.g,. inflect verb endings and noun number Never change grammatical class –dog, dogs –tengo, tienes, tiene, tenemos, tienen –Derivational Morphology Derive one word from another, Often change grammatical class –build, building; health, healthy

9. 9/11/2000Information Organization and Retrieval Automated Methods Powerful multilingual tools exist for morphological analysis –PCKimmo, Xerox Lexical technology –Require a grammar and dictionary –Use “two-level” automata Stemmers: –Very dumb rules work well (for English) –Porter Stemmer: Iteratively remove suffixes –Improvement: pass results through a lexicon

10. 9/11/2000Information Organization and Retrieval Errors Generated by Porter Stemmer (Krovetz 93)

11. 9/11/2000Information Organization and Retrieval Statistical Properties of Text Token occurrences in text are not uniformly distributed They are also not normally distributed They do exhibit a Zipf distribution

12. 9/11/2000Information Organization and Retrieval A More Standard Collection 8164 the 4771 of 4005 to 2834 a 2827 and 2802 in 1592 The 1370 for 1326 is 1324 s 1194 that 973 by 969 on 915 FT 883 Mr 860 was 855 be 849 Pounds 798 TEXT 798 PUB 798 PROFILE 798 PAGE 798 HEADLINE 798 DOCNO 1 ABC 1 ABFT 1 ABOUT 1 ACFT 1 ACI 1 ACQUI 1 ACQUISITIONS 1 ACSIS 1 ADFT 1 ADVISERS 1 AE Government documents, 157734 tokens, 32259 unique

13. 9/11/2000Information Organization and Retrieval Plotting Word Frequency by Rank Main idea: count –How many times tokens occur in the text Over all texts in the collection Now rank these according to how often they occur. This is called the rank.

14. 9/11/2000Information Organization and Retrieval Rank Freq Term 1 37 system 2 32 knowledg 3 24 base 4 20 problem 5 18 abstract 6 15 model 7 15 languag 8 15 implem 9 13 reason 10 13 inform 11 11 expert 12 11 analysi 13 10 rule 14 10 program 15 10 oper 16 10 evalu 17 10 comput 18 10 case 19 9 gener 20 9 form 150 2 enhanc 151 2 energi 152 2 emphasi 153 2 detect 154 2 desir 155 2 date 156 2 critic 157 2 content 158 2 consider 159 2 concern 160 2 compon 161 2 compar 162 2 commerci 163 2 clause 164 2 aspect 165 2 area 166 2 aim 167 2 affect Most and Least Frequent Terms

15. 9/11/2000Information Organization and Retrieval Rank Freq 1 37 system 2 32 knowledg 3 24 base 4 20 problem 5 18 abstract 6 15 model 7 15 languag 8 15 implem 9 13 reason 10 13 inform 11 11 expert 12 11 analysi 13 10 rule 14 10 program 15 10 oper 16 10 evalu 17 10 comput 18 10 case 19 9 gener 20 9 form The Corresponding Zipf Curve

16. 9/11/2000Information Organization and Retrieval Zoom in on the Knee of the Curve 43 6 approach 44 5 work 45 5 variabl 46 5 theori 47 5 specif 48 5 softwar 49 5 requir 50 5 potenti 51 5 method 52 5 mean 53 5 inher 54 5 data 55 5 commit 56 5 applic 57 4 tool 58 4 technolog 59 4 techniqu

17. 9/11/2000Information Organization and Retrieval Zipf Distribution The Important Points: –a few elements occur very frequently –a medium number of elements have medium frequency –many elements occur very infrequently

18. 9/11/2000Information Organization and Retrieval Zipf Distribution The product of the frequency of words (f) and their rank (r) is approximately constant –Rank = order of words’ frequency of occurrence Another way to state this is with an approximately correct rule of thumb: –Say the most common term occurs C times –The second most common occurs C/2 times –The third most common occurs C/3 times –…

19. Information Organization and Retrieval Zipf Distribution (linear and log scale)

20. 9/11/2000Information Organization and Retrieval What Kinds of Data Exhibit a Zipf Distribution? Words in a text collection –Virtually any language usage Library book checkout patterns Incoming Web Page Requests (Nielsen) Outgoing Web Page Requests (Cunha & Crovella) Document Size on Web (Cunha & Crovella)

21. 9/11/2000Information Organization and Retrieval Related Distributions/”Laws” Bradford’s Law of Scattering Lotka’s Law of Productivity De Solla Price’s Urn Model for “Cumulative Advantage Processes” ½ = 50%2/3 = 66%¾ = 75%Pick Replace +1

22. 9/11/2000Information Organization and Retrieval Very frequent word stems (Cha-Cha Web Index)

23. 9/11/2000Information Organization and Retrieval Words that occur few times (Cha-Cha Web Index)

24. 9/11/2000Information Organization and Retrieval Consequences of Zipf There are always a few very frequent tokens that are not good discriminators. –Called “stop words” in IR –Usually correspond to linguistic notion of “closed-class” words English examples: to, from, on, and, the,... Grammatical classes that don’t take on new members. There are always a large number of tokens that occur once and can mess up algorithms. Medium frequency words most descriptive

25. 9/11/2000Information Organization and Retrieval Word Frequency vs. Resolving Power (from van Rijsbergen 79) The most frequent words are not the most descriptive.

26. 9/11/2000Information Organization and Retrieval Statistical Independence vs. Statistical Dependence How likely is a red car to drive by given we’ve seen a black one? How likely is the word “ambulence” to appear, given that we’ve seen “car accident”? Color of cars driving by are independent (although more frequent colors are more likely) Words in text are not independent (although again more frequent words are more likely)

27. 9/11/2000Information Organization and Retrieval Statistical Independence Two events x and y are statistically independent if the product of their probability of their happening individually equals their probability of happening together.

28. 9/11/2000Information Organization and Retrieval Statistical Independence and Dependence What are examples of things that are statistically independent? What are examples of things that are statistically dependent?

29. 9/11/2000Information Organization and Retrieval Lexical Associations Subjects write first word that comes to mind –doctor/nurse; black/white (Palermo & Jenkins 64) Text Corpora yield similar associations One measure: Mutual Information (Church and Hanks 89) If word occurrences were independent, the numerator and denominator would be equal (if measured across a large collection)

30. 9/11/2000Information Organization and Retrieval Statistical Independence Compute for a window of words w1w11 w21 a b c d e f g h i j k l m n o p

31. 9/11/2000Information Organization and Retrieval Interesting Associations with “Doctor” (AP Corpus, N=15 million, Church & Hanks 89)

32. 9/11/2000Information Organization and Retrieval Un-Interesting Associations with “Doctor” ( AP Corpus, N=15 million, Church & Hanks 89) These associations were likely to happen because the non-doctor words shown here are very common and therefore likely to co-occur with any noun.

33. 9/11/2000Information Organization and Retrieval Document Vectors Documents are represented as “bags of words” Represented as vectors when used computationally –A vector is like an array of floating point –Has direction and magnitude –Each vector holds a place for every term in the collection –Therefore, most vectors are sparse

34. 9/11/2000Information Organization and Retrieval Document Vectors One location for each word. novagalaxy heath’wood filmroledietfur 10 5 3 5 10 10 8 7 9 10 5 10 10 9 10 5 7 9 6 10 2 8 7 5 1 3 ABCDEFGHIABCDEFGHI “Nova” occurs 10 times in text A “Galaxy” occurs 5 times in text A “Heat” occurs 3 times in text A (Blank means 0 occurrences.)

35. 9/11/2000Information Organization and Retrieval Document Vectors One location for each word. novagalaxy heath’wood filmroledietfur 10 5 3 5 10 10 8 7 9 10 5 10 10 9 10 5 7 9 6 10 2 8 7 5 1 3 ABCDEFGHIABCDEFGHI “Hollywood” occurs 7 times in text I “Film” occurs 5 times in text I “Diet” occurs 1 time in text I “Fur” occurs 3 times in text I

36. 9/11/2000Information Organization and Retrieval Document Vectors novagalaxy heath’wood filmroledietfur 10 5 3 5 10 10 8 7 9 10 5 10 10 9 10 5 7 9 6 10 2 8 7 5 1 3 ABCDEFGHIABCDEFGHI Document ids

37. 9/11/2000Information Organization and Retrieval We Can Plot the Vectors Star Diet Doc about astronomy Doc about movie stars Doc about mammal behavior

38. Information Organization and Retrieval Documents in 3D Space

39. 9/11/2000Information Organization and Retrieval Content Analysis Summary Content Analysis: transforming raw text into more computationally useful forms Words in text collections exhibit interesting statistical properties –Word frequencies have a Zipf distribution –Word co-occurrences exhibit dependencies Text documents are transformed to vectors –Pre-processing includes tokenization, stemming, collocations/phrases –Documents occupy multi-dimensional space.

40. Information need Index Pre-process Parse Collections Rank Query text input How is the index constructed?

41. 9/11/2000Information Organization and Retrieval Inverted Index This is the primary data structure for text indexes Main Idea: –Invert documents into a big index Basic steps: –Make a “dictionary” of all the tokens in the collection –For each token, list all the docs it occurs in. –Do a few things to reduce redundancy in the data structure

42. 9/11/2000Information Organization and Retrieval Inverted Indexes We have seen “Vector files” conceptually. An Inverted File is a vector file “inverted” so that rows become columns and columns become rows

43. 9/11/2000Information Organization and Retrieval How Are Inverted Files Created Documents are parsed to extract tokens. These are saved with the Document ID. Now is the time for all good men to come to the aid of their country Doc 1 It was a dark and stormy night in the country manor. The time was past midnight Doc 2

44. 9/11/2000Information Organization and Retrieval How Inverted Files are Created After all documents have been parsed the inverted file is sorted alphabetically.

45. 9/11/2000Information Organization and Retrieval How Inverted Files are Created Multiple term entries for a single document are merged. Within-document term frequency information is compiled.

46. 9/11/2000Information Organization and Retrieval How Inverted Files are Created Then the file can be split into –A Dictionary file and –A Postings file

47. 9/11/2000Information Organization and Retrieval How Inverted Files are Created Dictionary Postings

48. 9/11/2000Information Organization and Retrieval Inverted indexes Permit fast search for individual terms For each term, you get a list consisting of: –document ID –frequency of term in doc (optional) –position of term in doc (optional) These lists can be used to solve Boolean queries: country -> d1, d2 manor -> d2 country AND manor -> d2 Also used for statistical ranking algorithms

49. 9/11/2000Information Organization and Retrieval How Inverted Files are Used Dictionary Postings Query on “time” AND “dark” 2 docs with “time” in dictionary -> IDs 1 and 2 from posting file 1 doc with “dark” in dictionary -> ID 2 from posting file Therefore, only doc 2 satisfied the query.

50. 9/11/2000Information Organization and Retrieval Next Time Term weighting Statistical ranking