CSE 535 Information Retrieval

1. CSE 535Information Retrieval Chapter 2: Text Operations

2. Logical View 1. Description of content and semantics of document 2. Structure 3. Derivation of index terms: (a) removal of stopwords (b) stemming (c) identification of nouns

3. Assigned terms 1. Manual indexing (a) Professional indexer (b) Authors 2. Classification efforts (Library of Congress Classification, ACM) (a) Coarseness: large collections? (b) Categories are mutually exclusive 3. Subjectivity, inter-indexer consistency (:very low!) 4. Ontologies and classications: limited appeal for retrieval Extracted terms 1. Extraction from title, abstract or full-text 2. Methodology to extract must distinguish significant from non-significant terms 3. Controlled (limit to pre-defined dictionary vs. non-controlled vocabulary (all terms as they are) 4. Controlled ! less noise, better performance? Index Terms

4. Document Pre-Processing • Lexical analysis • digits, hypens, punctuation marks • e.g. I’ll --> I will • Stopword elimination • filter out words with low discriminatory value • stopword list • reduces size of index by 40% or more • might miss documents, e.g. “to be or not to be” • Stemming • connecting, connected, etc. • Selection of index terms • e.g. select nouns only • Thesaurus construction • permits query expansion

5. Lexical Analysis • Converting stream of characters into stream of words/tokens • more than just detecting spaces between words (see next slide) • Disregard numbers as index terms • too vague • perform date and number normalization • Hyphens • consistent policy on removal e.g. B-52 • Punctuation marks • typically removed • sometimes problematic, e.g. variable names myclass.height • Orthographic variations typically disregarded

6. Tokenization

7. Tokenization • Input: “Friends, Romans and Countrymen” • Output: Tokens • Friends • Romans • Countrymen • Each such token is now a candidate for an index entry, after further processing • Described below • But what are valid tokens to emit?

8. Parsing a document • What format is it in? • pdf/word/excel/html? • What language is it in? • What character set is in use? Each of these is a classification problem. But there are complications …

9. Format/language stripping • Documents being indexed can include docs from many different languages • A single index may have to contain terms of several languages. • Sometimes a document or its components can contain multiple languages/formats • French email with a Portuguese pdf attachment. • What is a unit document? • An email? • With attachments? • An email with a zip containing documents?

10. Tokenization • Issues in tokenization: • Finland’s capital  Finland? Finlands? Finland’s? • Hewlett-Packard  Hewlett and Packard as two tokens? • San Francisco: one token or two? How do you decide it is one token?

11. Language issues • Accents: résumé vs. resume. • L'ensemble one token or two? • L ? L’ ? Le ? • How are your users like to write their queries for these words?

12. Katakana Hiragana Kanji “Romaji” Tokenization: language issues • Chinese and Japanese have no spaces between words: • Not always guaranteed a unique tokenization • Further complicated in Japanese, with multiple alphabets intermingled • Dates/amounts in multiple formats フォーチュン500社は情報不足のため時間あた$500K(約6,000万円) End-user can express query entirely in (say) Hiragana!

13. Is this German “mit”? Normalization • “Right-to-left languages” like Hebrew and Arabic: can have “left-to-right” text interspersed. • Need to “normalize” indexed text as well as query terms into the same form • Character-level alphabet detection and conversion • Tokenization not separable from this. • Sometimes ambiguous: 7月30日 vs. 7/30 Morgen will ich in MIT …

14. Punctuation • Ne’er: use language-specific, handcrafted “locale” to normalize. • Which language? • Most common: detect/apply language at a pre-determined granularity: doc/paragraph. • State-of-the-art: break up hyphenated sequence. Phrase index? • U.S.A. vs. USA - use locale. • a.out

15. Numbers • 3/12/91 • Mar. 12, 1991 • 55 B.C. • B-52 • My PGP key is 324a3df234cb23e • 100.2.86.144 • Generally, don’t index as text. • Will often index “meta-data” separately • Creation date, format, etc.

16. Case folding • Reduce all letters to lower case • exception: upper case (in mid-sentence?) • e.g.,General Motors • Fed vs. fed • SAIL vs. sail

17. Thesauri and soundex • Handle synonyms and homonyms • Hand-constructed equivalence classes • e.g., car = automobile • your and you’re • Index such equivalences • When the document contains automobile, index it under car as well (usually, also vice-versa) • Or expand query? • When the query contains automobile, look under car as well

18. Assymetric query expansion

19. Lemmatization • Reduce inflectional/variant forms to base form • E.g., • am, are,is be • car, cars, car's, cars'car • the boy's cars are different colorsthe boy car be different color

20. Dictionary entries – first cut These may be grouped by language. More on this in ranking/query processing.

21. Perl program to Split Words # ! / usr/bin/perl while (<>) { $ line = $ ; chomp ( $ line ) ; Store line (with CR removed) if ( $ line = ˜ / [ aÅ|z ] / ) { # split on spaces @words = split / \ s + / , $line ; words is an associative array # separately print all words in line for each $term ( @words ) { # lowercase $term = ˜ tr / [A-Z ] / [ a-z ] / ; # remove punctuation $term = ˜ s / \ , | \ . | \ ; | \ : | \ ” | \ ’ | \ ‘ | \ ? | \ ! / / g ; print $term . ” \ n ” ; } } }

22. Building Lexical Analysers • Finite state machines • recognizer for regular expressions • Lex (http://www.combo.org/lex_yacc_page/lex.html) • popular Unix utility • program generator designed for lexical processing of character input streams • input: high-level, problem oriented specification for character string matching • output: program in a general purpose language which recognizes regular expressions +-------+ Source -> | Lex | -> yylex +-------+ +-------+ Input -> | yylex | -> Output +-------+

23. Writing lex source files • Format of a lex source file: • {definitions} • %% • {rules} • %% • {user subroutines} • invoking lex: • lex source cc lex.yy.c -ll • minimum lex program is %% (null program copies input to output) • use rules to do any special transformations • Program to convert British spelling to American • colour printf("color"); • mechanise printf("mechanize"); • petrol printf("gas");

24. Sample tokenizer using lex Sample input and output from above program here are some ids 1: <id> <id> <id> <id> now some numbers 123 4 2: <id> <id> <id> <number> <number> ids with numbers x123 78xyz 3: <id> <id> <id> <id> <number><id> garbage: @#$%^890abc 4: <id>\:\ \@#$%^\<number><id>

25. Stopword Elimination 1. Stopwords: (a) Too frequent, P(ki) > 0.8 (b) Irrelevant in linguistic terms i. Do not correpond to nouns, concepts etc ii. Could however be vital in natural language processing 2. Removal from collection on two grounds: (a) Retrieval efficiency i. High frequency: no distinction between documents ii. Useless for retrieval (b) Storage efficiency i. Zipf’s law ii. Heap’s law

26. OAC Stopword list (~ 275 words) • The Complete OAC-Search Stopword List • a did has nobody somewhere usually • about do have noone soon very • all does having nor still was • almost doing here not such way • along done how nothing than ways • already during however now that we • also each i of the well • although either if once their went • always enough ii one theirs were • am etc in one's then what • among even including only there when • an ever indeed or therefore whenever • and every instead other these where • any everyone into others they wherever • anyone everything is our thing whether • anything for it ours things which

27. Stopword list contd. anywhere from its out this while are further itself over those who as gave just own though whoever at get like perhaps through why away getting likely rather thus will because give may really to with between given maybe same together within beyond giving me shall too without both go might should toward would but going mine simply until yes by gone much since upon yet can good must so us you cannot got neither some use your come great never someone used yours could had no something uses yourself sometimes using

28. Implementing Stoplists • 2 ways to filter stoplist words from input token stream • examine tokenizer output and remove any stopwords • remove stopwords as part of lexical analysis (or tokenization) phase • Approach 1: filtering after tokenization • standard list search problem; every token must be looked up in stoplist • binary search trees, hashing • hashing is preferred method • insert stoplist into a hash table • each token hashed into table • if location is empty, not a stopword; otherwise string comparisons to see whether hashed value is really a stopword • Approach 2 • much more efficient

29. Stemming Algorithms • Designed to standardize morphological variations • stem is the portion of a word which is left after the removal of its affixes (prefixes and suffixes) • Benefits of stemming • supposedly, to increase recall; could lead to lower precision • controversy surrounding benefits • web search engines do NOT perform stemming • Reduces size of index (clear benefit) • 4 types of stemming algorithms • affix removal: most commonly used • table look-up: considerable storage space, all data not available • successor variety : (uses knowledge from structural linguistics, more complex) • n-grams; more a term clustering procedure that a stemming algorithm • Porter Stemming algorithm • http://snowball.sourceforge.net/porter/stemmer.html

30. Stemming - example • Word is “duplicatable” • duplicat step 4 • duplicate step 1b • duplic step 3

31. Stemming • Reduce terms to their “roots” before indexing • language dependent • e.g., automate(s), automatic, automation all reduced to automat. for exampl compres and compres are both accept as equival to compres. for example compressed and compression are both accepted as equivalent to compress.

32. Porter’s algorithm • Commonest algorithm for stemming English • Conventions + 5 phases of reductions • phases applied sequentially • each phase consists of a set of commands • sample convention: Of the rules in a compound command, select the one that applies to the longest suffix.

33. Typical rules in Porter • sses ss • ies i • ational ate • tional tion

34. Other stemmers • Other stemmers exist, e.g., Lovins stemmer http://www.comp.lancs.ac.uk/computing/research/stemming/general/lovins.htm • Single-pass, longest suffix removal (about 250 rules) • Motivated by Linguistics as well as IR • Full morphological analysis - modest benefits for retrieval

35. Stemming variants

36. Porter Stemmer : Representing Strings • A consonant in a word is a letter other than A, E, I, O or U, and other than Y preceded by a consonant. • (The fact that the term ‘consonant’ is defined to some extent in terms of itself does not make it ambiguous.) • So in TOY the consonants are T and Y, and in SYZYGY they are S, Z and G. • If a letter is not a consonant it is a vowel. A consonant will be denoted by c, a vowel by v. A list ccc... of length greater than 0 will be denoted by C, • and a list vvv... of length greater than 0 will be denoted by V. Any word, or part of a word, • therefore has one of the four forms: • CVCV ... C • CVCV ... V • VCVC ... C • VCVC ... V • These may all be represented by the single form • [C]VCVC ... [V] • where the square brackets denote arbitrary presence of their contents. Using (VC)m to denote VC repeated m times, • this may again be written as • [C](VC)m[V]. • m will be called the measure of any word or word part when represented in this form. The case m = 0 covers the null word. • Here are some examples: • m=0 TR, EE, TREE, Y, BY. • m=1 TROUBLE, OATS, TREES, IVY. • m=2 TROUBLES, PRIVATE, OATEN, ORRERY.

37. Porter Stemmer - Types of Rules • The rules for removing a suffix will be given in the form • (condition) S1 -> S2 • This means that if a word ends with the suffix S1, and the stem before S1 satisfies the given condition, S1 is replaced by S2. The condition is usually given in terms of m, e.g. • (m > 1) EMENT -> • Here S1 is ‘EMENT’ and S2 is null. This would map REPLACEMENT to REPLAC, since REPLAC is a word part for which m = 2. The ‘condition’ part may also contain the following: • *S - the stem ends with S (and similarly for the other letters). • *v* - the stem contains a vowel. • *d - the stem ends with a double consonant (e.g. -TT, -SS). • *o - the stem ends cvc, where the second c is not W, X or Y (e.g. -WIL, -HOP). • And the condition part may also contain expressions with and, or and not, so that • (m>1 and (*S or *T)) • tests for a stem with m>1 ending in S or T, while • (*d and not (*L or *S or *Z)) • tests for a stem ending with a double consonant other than L, S or Z. Elaborate conditions like this are required only rarely. In a set of rules written beneath each other, only one is obeyed, and this will be the one with the longest matching S1 for the given word. For example, with • SSES -> SS • IES -> I • SS -> SS • S -> • (here the conditions are all null) CARESSES maps to CARESS since SSES is the longest match for S1. • Equally CARESS maps to CARESS (S1=‘SS’) and CARES to CARE (S1=‘S’).

38. Porter Stemmer - Step 1a In the rules below, examples of their application, successful or otherwise, are given on the right in lower case. Only one rule from each step can fire. The algorithm now follows: Step 1a SSES -> SS caresses -> caress IES -> I ponies -> poni ties -> ti SS -> SS caress -> caress S -> cats -> cat

39. Porter Stemmer- Step 1b Step 1b (m>0) EED -> EE feed -> feed agreed -> agree (*v*) ED -> plastered -> plaster bled -> bled (*v*) ING -> motoring -> motor sing -> sing If the second or third of the rules in Step 1b is successful, the following is done: AT -> ATE conflat(ed) -> conflate BL -> BLE troubl(ed) -> trouble IZ -> IZE siz(ed) -> size (*d and not (*L or *S or *Z)) -> single letter hopp(ing) -> hop tann(ed) -> tan fall(ing) -> fall hiss(ing) -> hiss fizz(ed) -> fizz (m=1 and *o) -> E fail(ing) -> fail fil(ing) -> file The rule to map to a single letter causes the removal of one of the double letter pair. The -E is put back on -AT, -BL and -IZ, so that the suffixes -ATE, -BLE and -IZE can be recognised later. This E may be removed in step 4.

40. Porter Stemmer- Step 1c Step 1c (*v*) Y -> I happy -> happi sky -> sky Step 1 deals with plurals and past participles. The subsequent steps are much more straightforward.

41. Porter Stemmer - Step 2 (m>0) ATIONAL -> ATE relational -> relate (m>0) TIONAL -> TION conditional -> condition rational -> rational (m>0) ENCI -> ENCE valenci -> valence (m>0) ANCI -> ANCE hesitanci -> hesitance (m>0) IZER -> IZE digitizer -> digitize (m>0) ABLI -> ABLE conformabli -> conformable (m>0) ALLI -> AL radicalli -> radical (m>0) ENTLI -> ENT differentli -> different (m>0) ELI -> E vileli -> vile (m>0) OUSLI -> OUS analogousli -> analogous (m>0) IZATION -> IZE vietnamization -> vietnamize (m>0) ATION -> ATE predication -> predicate (m>0) ATOR -> ATE operator -> operate (m>0) ALISM -> AL feudalism -> feudal (m>0) IVENESS -> IVE decisiveness -> decisive (m>0) FULNESS -> FUL hopefulness -> hopeful (m>0) OUSNESS -> OUS callousness -> callous (m>0) ALITI -> AL formaliti -> formal (m>0) IVITI -> IVE sensitiviti -> sensitive (m>0) BILITI -> BLE sensibiliti -> sensible The test for the string S1 can be made fast by doing a program switch on the penultimate letter of the word being tested. This gives a fairly even breakdown of the possible values of the string S1. It will be seen in fact that the S1-strings in step 2 are presented here in the alphabetical order of their penultimate letter. Similar techniques may be applied in the other steps.

42. Porter Stemmer - Step 3 (m>0) ICATE -> IC triplicate -> triplic (m>0) ATIVE -> formative -> form (m>0) ALIZE -> AL formalize -> formal (m>0) ICITI -> IC electriciti -> electric (m>0) ICAL -> IC electrical -> electric (m>0) FUL -> hopeful -> hope (m>0) NESS -> goodness -> good

43. Porter Stemmer- Step 4 (m>1) AL -> revival -> reviv (m>1) ANCE -> allowance -> allow (m>1) ENCE -> inference -> infer (m>1) ER -> airliner -> airlin (m>1) IC -> gyroscopic -> gyroscop (m>1) ABLE -> adjustable -> adjust (m>1) IBLE -> defensible -> defens (m>1) ANT -> irritant -> irrit (m>1) EMENT -> replacement -> replac (m>1) MENT -> adjustment -> adjust (m>1) ENT -> dependent -> depend (m>1 and (*S or *T)) ION -> adoption -> adopt (m>1) OU -> homologou -> homolog (m>1) ISM -> communism -> commun (m>1) ATE -> activate -> activ (m>1) ITI -> angulariti -> angular (m>1) OUS -> homologous -> homolog (m>1) IVE -> effective -> effect (m>1) IZE -> bowdlerize -> bowdler The suffixes are now removed. All that remains is a little tidying up.

44. Porter Stemmer - Step 5 Step 5a (m>1) E -> probate -> probat rate -> rate (m=1 and not *o) E -> cease -> ceas Step 5b (m > 1 and *d and *L) -> single letter controll -> control roll -> roll

45. Thesauri • Thesaurus • precompiled list of words in a given domain • for each word above, a list of related words • computer-aided instruction computer applications educational computing • Applications of Thesaurus in IR • provides a standard vocabulary for indexing and searching • medical literature querying • proper query formulation • classified hierarchies that allow broadening/narrowing of current query • e.g. Yahoo • Components of a thesaurus • index terms • relationship among terms • layout design for term relationships