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Content Analysis & Stemming Yaşar Tonta Hacettepe Üniversitesi tonta@hacettepe. edu. tr Note: Slides are Content Analysis & Stemming Yaşar Tonta Hacettepe Üniversitesi [email protected] edu. tr Note: Slides are taken from Prof. Ray Larson’s web site (www. sims. berkeley. edu/~ray/ yunus. hacettepe. edu. tr/~tonta/ BBY 220 Bilgi Erişim İlkeleri BBY 220 - SLAYT 1

Content Analysis • Automated Transformation of raw text into a form that represent some Content Analysis • Automated Transformation of raw text into a form that represent some aspect(s) of its meaning • Including, but not limited to: – – – BBY 220 Automated Thesaurus Generation Phrase Detection Categorization Clustering Summarization - SLAYT 2

Techniques for Content Analysis • Statistical – Single Document – Full Collection • Linguistic Techniques for Content Analysis • Statistical – Single Document – Full Collection • Linguistic – Syntactic – Semantic – Pragmatic • Knowledge-Based (Artificial Intelligence) • Hybrid (Combinations) BBY 220 - SLAYT 3

Text Processing • Standard Steps: – Recognize document structure • titles, sections, paragraphs, etc. 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 BBY 220 - SLAYT 4

Document Processing Steps - SLAYT 5 Document Processing Steps - SLAYT 5

Stemming and Morphological Analysis words • Goal: “normalize” similar • Morphology (“form” of words) Stemming and Morphological Analysis words • Goal: “normalize” similar • 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 BBY 220 - SLAYT 6

Statistical Properties of Text • Token occurrences in text are not uniformly distributed • Statistical Properties of Text • Token occurrences in text are not uniformly distributed • They are also not normally distributed • They do exhibit a Zipf distribution BBY 220 - SLAYT 7

Plotting Word Frequency by Rank • Main idea: count – How many tokens occur Plotting Word Frequency by Rank • Main idea: count – How many tokens occur 1 time – How many tokens occur 2 times – How many tokens occur 3 times … • Now rank these according to how of they occur. This is called the rank. BBY 220 - SLAYT 8

Plotting Word Frequency by Rank • Say for a text with 100 tokens • Plotting Word Frequency by Rank • Say for a text with 100 tokens • Count – – – How many tokens occur 1 time (50) How many tokens occur 2 times (20) … How many tokens occur 7 times (10) … How many tokens occur 12 times (1) How many tokens occur 14 times (1) • So things that occur the most often share the highest rank (rank 1). • Things that occur the fewest times have the lowest rank (rank n). BBY 220 - SLAYT 9

Observation: MANY phenomena can be characterized this way. • • • BBY 220 Words Observation: MANY phenomena can be characterized this way. • • • BBY 220 Words in a text collection Library book checkout patterns Bradford’s and Lotka’s laws. Incoming Web Page Requests (Nielsen) Outgoing Web Page Requests (Cunha & Crovella) Document Size on Web (Cunha & Crovella) - SLAYT 10

Zipf Distribution (linear and log scale) - SLAYT 11 Zipf Distribution (linear and log scale) - SLAYT 11

Zipf Distribution • The product of the frequency of words (f) and their rank 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: – – BBY 220 Say the most common term occurs C times The second most common occurs C/2 times The third most common occurs C/3 times … - SLAYT 12

The Corresponding Zipf Curve Rank 1 2 3 4 5 6 7 8 9 The Corresponding Zipf Curve Rank 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Freq 37 32 24 20 18 15 15 15 13 13 11 11 10 10 10 9 9 BBY 220 system knowledg base problem abstract model languag implem reason inform expert analysi rule program oper evalu comput case gener form - SLAYT 13

Zoom in on the Knee of the Curve 43 44 45 46 47 48 Zoom in on the Knee of the Curve 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 6 5 5 5 5 4 4 4 BBY 220 approach work variabl theori specif softwar requir potenti method mean inher data commit applic tool technolog techniqu - SLAYT 14

Zipf Distribution • The Important Points: – a few elements occur very frequently – Zipf Distribution • The Important Points: – a few elements occur very frequently – a medium number of elements have medium frequency – many elements occur very infrequently BBY 220 - SLAYT 15

Most and Least Frequent Terms Rank 1 2 3 4 5 6 7 8 Most and Least Frequent Terms Rank 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 BBY 220 Freq 37 32 24 20 18 15 15 15 13 13 11 11 10 10 10 9 9 Term system knowledg base problem abstract model languag implem reason inform expert analysi rule program oper evalu comput case gener form 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 2 2 2 2 2 enhanc energi emphasi detect desir date critic content consider concern compon compar commerci clause aspect area aim affect - SLAYT 16

A Standard Collection Government documents, 157734 tokens, 32259 unique 8164 the 4771 of 4005 A Standard Collection Government documents, 157734 tokens, 32259 unique 8164 the 4771 of 4005 to 2834 a 2827 and 2802 in 1592 The 1370 for 1326 is 1324 s 1194 that 973 by BBY 220 969 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 ACQUISITIONS 1 ACSIS 1 ADFT 1 ADVISERS 1 AE - SLAYT 17

Housing Listing Frequency Data 6208 tokens, 1318 unique (very small collection) BBY 220 - Housing Listing Frequency Data 6208 tokens, 1318 unique (very small collection) BBY 220 - SLAYT 18

Very frequent word stems (Cha -Cha Web Index) BBY 220 - SLAYT 19 Very frequent word stems (Cha -Cha Web Index) BBY 220 - SLAYT 19

Words that occur few times (Cha-Cha Web Index) BBY 220 - SLAYT 20 Words that occur few times (Cha-Cha Web Index) BBY 220 - SLAYT 20

Word Frequency vs. Resolving Power (from van Rijsbergen 79) The most frequent words are Word Frequency vs. Resolving Power (from van Rijsbergen 79) The most frequent words are not the most descriptive. BBY 220 - SLAYT 21

Stemming and Morphological Analysis words • Goal: “normalize” similar • Morphology (“form” of words) Stemming and Morphological Analysis words • Goal: “normalize” similar • 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 BBY 220 - SLAYT 22

Simple “S” stemming • IF a word ends in “ies”, but not “eies” or Simple “S” stemming • IF a word ends in “ies”, but not “eies” or “aies” – THEN “ies” “y” • IF a word ends in “es”, but not “aes”, “ees”, or “oes” – THEN “es” “e” • IF a word ends in “s”, but not “us” or “ss” – THEN “s” NULL Harman, JASIS 1991 BBY 220 - SLAYT 23

Errors Generated by Porter Stemmer (Krovetz 93) BBY 220 - SLAYT 24 Errors Generated by Porter Stemmer (Krovetz 93) BBY 220 - SLAYT 24

Automated Methods • Stemmers: – Very dumb rules work well (for English) – Porter Automated Methods • Stemmers: – Very dumb rules work well (for English) – Porter Stemmer: Iteratively remove suffixes – Improvement: pass results through a lexicon • Powerful multilingual tools exist for morphological analysis – – BBY 220 PCKimmo, Xerox Lexical technology Require a grammar and dictionary Use “two-level” automata Wordnet “morpher” - SLAYT 25

Wordnet • Type “wn word” on irony. • Large exception dictionary: • Demo BBY Wordnet • Type “wn word” on irony. • Large exception dictionary: • Demo BBY 220 aardwolves aardwolf abaci abacuses abacus abbacies abbacy abhenries abhenry abilities ability abkhaz abnormalities abnormality aboideaus aboideaux aboideau aboiteaus aboiteaux aboiteau abos abo abscissae abscissas abscissa absurdities absurdity … - SLAYT 26

Using NLP • Strzalkowski (in Reader) Text NLP: BBY 220 TAGGER NLP repres PARSER Using NLP • Strzalkowski (in Reader) Text NLP: BBY 220 TAGGER NLP repres PARSER Dbase search TERMS - SLAYT 27

Using NLP INPUT SENTENCE The former Soviet President has been a local hero ever Using NLP INPUT SENTENCE The former Soviet President has been a local hero ever since a Russian tank invaded Wisconsin. TAGGED SENTENCE The/dt former/jj Soviet/jj President/nn has/vbz been/vbn a/dt local/jj hero/nn ever/rb since/in a/dt Russian/jj tank/nn invaded/vbd Wisconsin/np. /per BBY 220 - SLAYT 28

Using NLP TAGGED & STEMMED SENTENCE the/dt former/jj soviet/jj president/nn have/vbz be/vbn a/dt local/jj Using NLP TAGGED & STEMMED SENTENCE the/dt former/jj soviet/jj president/nn have/vbz be/vbn a/dt local/jj hero/nn ever/rb since/in a/dt russian/jj tank/nn invade/vbd wisconsin/np. /per BBY 220 - SLAYT 29

Using NLP PARSED SENTENCE [assert [[perf [have]][[verb[BE]] [subject [np[n PRESIDENT][t_pos THE] [adj[FORMER]][adj[SOVIET]]]] [adv EVER] Using NLP PARSED SENTENCE [assert [[perf [have]][[verb[BE]] [subject [np[n PRESIDENT][t_pos THE] [adj[FORMER]][adj[SOVIET]]]] [adv EVER] [sub_ord[SINCE [[verb[INVADE]] [subject [np [n TANK][t_pos A] [adj [RUSSIAN]]]] [object [np [name [WISCONSIN]]]]] BBY 220 - SLAYT 30

Using NLP EXTRACTED TERMS & WEIGHTS President 2. 623519 soviet President+soviet 11. 556747 president+former Using NLP EXTRACTED TERMS & WEIGHTS President 2. 623519 soviet President+soviet 11. 556747 president+former Hero 7. 896426 hero+local Invade 8. 435012 tank Tank+invade 17. 402237 tank+russian Russian 7. 383342 wisconsin BBY 220 5. 416102 14. 594883 14. 314775 6. 848128 16. 030809 7. 785689 - SLAYT 31

Other Considerations • Church (SIGIR 1995) looked at correlations between forms of words in Other Considerations • Church (SIGIR 1995) looked at correlations between forms of words in texts BBY 220 - SLAYT 32

Assumptions in IR • Statistical independence of terms • Dependence approximations BBY 220 - Assumptions in IR • Statistical independence of terms • Dependence approximations BBY 220 - SLAYT 33

Statistical Independence Two events x and y are statistically independent if the product of 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. BBY 220 - SLAYT 34

Statistical Independence and Dependence • What are examples of things that are statistically independent? Statistical Independence and Dependence • What are examples of things that are statistically independent? • What are examples of things that are statistically dependent? BBY 220 - SLAYT 35

Statistical Independence vs. Statistical Dependence • How likely is a red car to drive 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) BBY 220 - SLAYT 36

Lexical Associations • Subjects write first word that comes to mind – doctor/nurse; black/white 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) BBY 220 - SLAYT 37

Interesting Associations with “Doctor” (AP Corpus, N=15 million, Church & Hanks 89) BBY 220 Interesting Associations with “Doctor” (AP Corpus, N=15 million, Church & Hanks 89) BBY 220 - SLAYT 38

Un-Interesting Associations with “Doctor” (AP Corpus, N=15 million, Church & Hanks 89) These associations 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. BBY 220 - SLAYT 39