INFO 4300 / CS4300 Information Retrieval [0.5cm] slides ... · More Administrativa (Omitted last...
Transcript of INFO 4300 / CS4300 Information Retrieval [0.5cm] slides ... · More Administrativa (Omitted last...
INFO 4300 / CS4300Information Retrieval
slides adapted from Hinrich Schutze’s,linked from http://informationretrieval.org/
IR 2: The term vocabulary and postings lists
Paul Ginsparg
Cornell University, Ithaca, NY
1 Sep 2009
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Administrativa
Course Webpage:http://www.infosci.cornell.edu/Courses/info4300/2009fa/
Lectures: Tuesday and Thursday 11:40-12:55, Hollister B14
Instructor: Paul Ginsparg, [email protected], 255-7371,Cornell Information Science, 301 College Avenue
Instructor’s Assistant: Corinne Russell, [email protected],255-5925, Cornell Information Science, 301 College Avenue
Instructor’s Office Hours: Wed 1-2pm or e-mail instructor toschedule an appointment
Teaching Assistant TBD, @cornell.eduThe Teaching Assistant does not have scheduled office hoursbut is available to help you by email.
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More Administrativa
(Omitted last time, and no one asked until afterwards . . .)
During this course there will be four assignments which requireprogramming (due last year 21 Sep, 11 Oct, 9 Nov, 5 Dec), andtwo examinations: a mid-term and a final.
The course grade will be based on course assignments,participation in the discussions, and the examinations.
The weightings given to these components are expected to be asfollows (but may be changed):Assignments 40%, Participation 20%, Examinations 40%.
Course text at: http://informationretrieval.org/
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Major Steps
1. Collect documents
2. Tokenize text
3. linguistic preprocessing
4. Index documents
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Inverted index
For each term t, we store a list of all documents that contain t.
Brutus −→ 1 2 4 11 31 45 173 174
Caesar −→ 1 2 4 5 6 16 57 132 . . .
Calpurnia −→ 2 31 54 101
...
︸ ︷︷ ︸ ︸ ︷︷ ︸
dictionary postings
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Intersecting two postings lists
Brutus −→ 1 → 2 → 4 → 11 → 31 → 45 → 173 → 174
Calpurnia −→ 2 → 31 → 54 → 101
Intersection =⇒ 2 → 31
Linear in the length of the postings lists.
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Constructing the inverted index: Sort postingsterm docID
I 1did 1enact 1julius 1caesar 1I 1was 1killed 1i’ 1the 1capitol 1brutus 1killed 1me 1so 2let 2it 2be 2with 2caesar 2the 2noble 2brutus 2hath 2told 2you 2caesar 2was 2ambitious 2
=⇒
term docID
ambitious 2be 2brutus 1brutus 2capitol 1caesar 1caesar 2caesar 2did 1enact 1hath 1I 1I 1i’ 1it 2julius 1killed 1killed 1let 2me 1noble 2so 2the 1the 2told 2you 2was 1was 2with 2
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Westlaw: Example queries
Information need: Information on the legal theories involved inpreventing the disclosure of trade secrets by employees formerlyemployed by a competing company Query: “trade secret” /s
disclos! /s prevent /s employe! Information need: Requirements for
disabled people to be able to access a workplace Query: disab! /p
access! /s work-site work-place (employment /3 place) Information
need: Cases about a host’s responsibility for drunk guests Query:
host! /p (responsib! liab!) /p (intoxicat! drunk!) /p guest
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Terms and documents
Last lecture: Simple Boolean retrieval system
Our assumptions were:
We know what a document is.We know what a term is.
Both issues can be complex in reality.
We’ll look a little bit at what a document is.
But mostly at terms: How do we define and process thevocabulary of terms of a collection?
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Parsing a document
Before we can even start worrying about terms . . .
. . . need to deal with format and language of each document.
What format is it in? pdf, word, excel, html etc.
What language is it in?
What character set is in use?
Each of these is a classification problem, which we will studylater in this course
Alternative: use heuristics
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Format/Language: Complications
A single index usually contains terms of several languages.
Sometimes a document or its components contain multiplelanguages/formats.French email with Spanish pdf attachment
What is the document unit for indexing?
A file?
An email?
An email with 5 attachments?
A group of files (ppt or latex in HTML)?
Issues with books (again precision vs. recall)
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What is a document?
Take-away: potentially non-trivial; in many cases requiressome design decisions.
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Definitions
Word – A delimited string of characters as it appears in thetext.
Term – A “normalized” word (case, morphology, spelling etc);an equivalence class of words.
Token – An instance of a word or term occurring in adocument.
Type – The same as a term in most cases: an equivalenceclass of tokens.
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Type/token distinction: Example
In June, the dog likes to chase the cat in the barn.
How many word tokens? How many word types?
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Recall: Inverted index construction
Input:
Friends, Romans, countrymen. So let it be with Caesar . . .
Output:
friend roman countryman so . . .
Each token is a candidate for a postings entry.
What are valid tokens to emit?
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Why tokenization is difficult – even in English
Example: Mr. O’Neill thinks that the boys’ stories about Chile’s
capital aren’t amusing.
Tokenize this sentence
(neill , oneill , o’neill , o’ neill , o neill)(aren’t , arent , are n’t , aren t)
choices determine which Boolean queries will match
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One word or two? (or several)
Hewlett-Packard
State-of-the-art
co-education
the hold-him-back-and-drag-him-away maneuver
data base
San Francisco
Los Angeles-based company
cheap San Francisco-Los Angeles fares
York University vs. New York University
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Numbers
3/20/91
20/3/91
Mar 20, 1991
B-52
100.2.86.144
(800) 234-2333
800.234.2333
Older IR systems may not index numbers . . .
. . . but generally it’s a useful feature.
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Chinese: No whitespace莎拉波娃 � 在居住在美国� 南部的佛 � 里 � 。今年4月9日,莎拉波娃在美国第一大城市 � � 度 � 了18 � 生日。生日派� 上,莎拉波娃露出了甜美的微笑。23 / 42
Ambiguous segmentation in Chinese和尚The two characters can be treated as one word meaning ‘monk’ oras a sequence of two words meaning ‘and’ and ‘still’.
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Other cases of “no whitespace”
Compounds in Dutch and German
Computerlinguistik → Computer + Linguistik
Lebensversicherungsgesellschaftsangestellter
→ leben + versicherung + gesellschaft + angestellter
Inuit: tusaatsiarunnanngittualuujunga (I can’t hear very well.)
Swedish, Finnish, Greek, Urdu, many other languages
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Japanese � � � � � � � � � � � � � � � � � � � � � ! " � # $% & ' ( ( ) * + ) * , - � . � / 0 1 2 � 3 4 5 6 7 8 9 2 � �: � ; < = > ?@ / 4 A B � C D C E � F G � H I J K L � 6 M N?A B � C D C E 2 O P 3 Q R � 3 C % S 2 T 4 U V W H X Y % Z [\ ] � ^ _ _ ` a b / c d W H 2 $ 4 e f D g h 4 � i = j 4 kD l � m n 3 o _ p q _ 6 H r W s t u v w � C J x � � � y W > 4z _ { | } ~ / � � � 2 Z � � � q � / � � � � � V H I J4
different “alphabets”: Chinese characters, hiragana syllabary forinflectional endings and function words, katakana syllabary fortranscription of foreign words and other uses, and latin. No spaces(as in Chinese). End user can express query entirely in hiragana!
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Arabic script: Bidirectionality
�ل ا������132 ��� 1962ا���� ا��ا�� �� ��� �� . #"!" ! ا� ← → ← → ← START ‘Algeria achieved its independence in 1962 after 132 years of French occupation.’ Bidirectionality is not a problem if text is coded in Unicode.
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Accents and diacritics
Accents: resume vs. resume (simple omission of accent)
Umlauts: Universitat vs. Universitaet (substitution withspecial letter sequence “ae”)
Most important criterion: How are users likely to write theirqueries for these words?
Even in languages that standardly have accents, users oftendo not type them. (Polish?)
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Normalization
Need to “normalize” terms in indexed text as well as queryterms into the same form.
Example: We want to match U.S.A. and USA
We most commonly implicitly define equivalence classes ofterms.
Alternatively: do asymmetric expansion
window → window, windowswindows → Windows, windowsWindows (no expansion)
More powerful, but less efficient
Why don’t you want to put window, Window, windows, andWindows in the same equivalence class?
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Normalization: Other languages
Normalization and language detection interact.
PETER WILL NICHT MIT. → MIT = mit
He got his PhD from MIT. → MIT 6= mit
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Case folding
Reduce all letters to lower case
Possible exceptions: capitalized words in mid-sentence
MIT vs. mit
Fed vs. fed
It’s often best to lowercase everything since users will uselowercase regardless of correct capitalization.
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Stop words
stop words = extremely common words which would appearto be of little value in helping select documents matching auser need
Examples: a, an, and, are, as, at, be, by, for, from, has, he, in,
is, it, its, of, on, that, the, to, was, were, will, with
Stop word elimination used to be standard in older IR systems.
But you need stop words for phrase queries, e.g. “King ofDenmark”
Most web search engines index stop words.
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More equivalence classing
Soundex: (phonetic equivalence, Muller = Mueller)
Thesauri: (semantic equivalence, car = automobile)
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Lemmatization
Reduce inflectional/variant forms to base form
Example: am, are, is → be
Example: car, cars, car’s, cars’ → car
Example: the boy’s cars are different colors
→ the boy car be different color
Lemmatization implies doing “proper” reduction to dictionaryheadword form (the lemma).
Inflectional morphology (cutting → cut) vs. derivationalmorphology (destruction → destroy)
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Stemming
Definition of stemming: Crude heuristic process that chops offthe ends of words in the hope of achieving what “principled”lemmatization attempts to do with a lot of linguisticknowledge.
Language dependent
Often inflectional and derivational
Example for derivational: automate, automatic, automation
all reduce to automat
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Porter algorithm
Most common algorithm for stemming English
Results suggest that it is at least as good as other stemmingoptions
Conventions + 5 phases of reductions
Phases are applied sequentially
Each phase consists of a set of commands.
Sample command: Delete final ement if what remains is longerthan 1 characterreplacement → replaccement → cement
Sample convention: Of the rules in a compound command,select the one that applies to the longest suffix.
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Porter stemmer: A few rules
Rule Example
SSES → SS caresses → caressIES → I ponies → poniSS → SS caress → caressS → cats → cat
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Three stemmers: A comparison
Sample text: Such an analysis can reveal features that are not easilyvisible from the variations in the individual genes and canlead to a picture of expression that is more biologicallytransparent and accessible to interpretation
Porter stemmer: such an analysi can reveal featur that ar not easili visiblfrom the variat in the individu gene and can lead to a picturof express that is more biolog transpar and access to interpret
Lovins stemmer: such an analys can reve featur that ar not eas vis from thvari in th individu gen and can lead to a pictur of expres thatis mor biolog transpar and acces to interpres
Paice stemmer: such an analys can rev feat that are not easy vis from thevary in the individ gen and can lead to a pict of express thatis mor biolog transp and access to interpret
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Does stemming improve effectiveness?
In general, stemming increases effectiveness for some queries,and decreases effectiveness for others.
Porter Stemmer equivalence class oper contains all of operate
operating operates operation operative operatives operational.
Queries where stemming hurts: “operational AND research”,“operating AND system”, “operative AND dentistry”
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