| 1 Gertjan van Noord2014 Zoekmachines Lecture 2: vocabulary, posting lists.

| 1

Gertjan van Noord 2014

Zoekmachines

Lecture 2: vocabulary, posting lists

Agenda for today

• Questions Chapter 1• Chapter 2: Term vocabulary & posting lists• Chapter 2: Posting lists with positions• Homework/lab assignment

Chapter 2 Overview

Preprocessing of documents• choose the unit of indexing (granularity)• tokenization (removing punctuation, splitting

in words)• stop list?• normalization: case folding, stemming versus

lemmatizing, ...• extensions to postings lists

Tokens, types and terms

token each separate word in the texttype same words belong to one type

(index) term finally included in the indexindex term is an equivalence class

of tokens and/or types

Tokens, types and terms

The Lord of the Rings

• Number of tokens?• 5• Number of types?• 4• Number of terms?• 4? 2? 1?

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Equivalence classes

• Casefolding• Diacritics• Stemming/lemmatisation• Decompounding• Synonym lists• Variant spellings

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Equivalence classes

• Implicit: mapping rules• Relational: query expansion• Relational: double indexing• Mapping should be done:

– Indexing– Querying

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Words and word forms

• Inflection (D: verbuiging/vervoeging)- changing a word to express person, case,

aspect, ...- for determiners, nouns, pronouns, adjectives:

declination (D: verbuiging)- for verbs: conjugation (D: vervoeging)

• Derivation (D: afleiding)- formation of a new word from another word

(e.g. by adding an affix (prefix or suffix) or changing the grammatical category)

Inflection examples

DeterminersE: the D: de, het G: der, des, dem, den, die, das

AdjectivesE: young D: jonge, jonge G: junger, junge, junges,

jungenNouns

E: man, men D: man, mannen G: mann, mannes, Verbs

E write / writes / wrote / writtenD schrijf/ schrijft /schrijven / schreef/ schreven /

geschrevenG schreibe/ schreibst / schreibt / schreiben / schrieben /

geschrieben

Derivation examples

to browse -> a browserred -> to redden, reddish Google -> to google

arm(s) -> to arm, to disarm -> disarmament, disarming

Stemming and lemmatizing

verb forms inform, informs, informed, informingderivations information, informative, informal??stem inform lemma inform, information, informative,

informal

verb forms sing: sings, sang, sung, singingderivations singer, singers, song, songs stem sing, sang, sung, song, lemma sing, singer, song

Discussion

Why is stemming used when lemmatizing is much more precise?

Lemmatizing is a more complex processit needs - a vocabulary (problem: new words)- morphologic analysis (knowledge of inflection rules)- syntactic analysis, parsing (noun or verb?)

Compound splitting

Marketingjargon -> marketing AND jargon

• Increased retrieval• Decreased precision• Must be applied to both query and index!• But what to do with the query marketing

jargon ?• And with spreekwoord appel boom ?

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Chapter 2 Overview

Preprocessing of documents• choose the unit of indexing (granularity)• tokenization (removing punctuation, splitting

in words)• stop list?• normalization: case folding, stemming versus

lemmatizing, ...• extensions to postings lists

Efficient merging of postings

For X AND Y, we have to intersect 2 listsMost documents will contain only one of the two

terms

Recall basic intersection algorithm

Skip pointers

Skip pointers

• Makes intersection of 2 lists more efficient• think of millions of list items

• How many skip pointers and where?• Trade-off:

• More pointers, often useful but small skips.

• Less pointers …• Heuristic: distance √n, evenly distributed

Skip pointers: useful?

Yes, certainly in the pastWith very fast CPUs less important

Especially in a rather static indexIf a list keeps changing less effective

Extensions of the simple term indexTo support phrase queries• “information retrieval”• “retrieval of information”

Different approaches• biword indexes• phrase indexes• positional indexes• combinations

Biword and phrase indexes

• Holding terms together in the index• Simple biword index:

• retrieval of, of information • Sophisticated: POS tagger selects nouns

• N x* N retrieval of this information• Phrase index: includes variable lengths of word

sequences • terms of 1 and 2 words both included

Positional index

Add in the postings lists for each doc the list of positions of the termfor phrase queriesfor proximity search

Example[information, 4] : [1:<4,22, 35>, 2:<5,17, 30>, …][retrieval, 2] : [1:<5,20>, 2:<18,31>]

Combination schemes

Often queried combinations: phrase indexnames of persons and organizationesp. combinations of common terms (!)find out from query log

For other phrases a positional indexWilliams e.a.: next word index added

H.E. Williams, J.Zobel, and D.Bahle (2004) Fast Phrase Querying With Combined Indexes (ACM Dig Library):

Phrase querying with a combination of three approaches

(next word index, phrase index and inverted file)... is more than 60% faster on average than using an

inverted index alone ... requires structures that total only 20% of the size

of the collection.

A nextword index (Williams e.a.)

docfreq,(<doc,freq,[pos, pos,..]>,<doc, freq, [..]

No of matching docs

Doc ID

No of occurrences in doc

position