information retrieval --> dictionary.ppt

Introduction to Information Retrieval
Introduction to
Information Retrieval
Document ingestion
2 The term vocabulary and postings lists

Recall the basic indexing pipeline
Tokenizer
Token stream Friends Romans Countrymen
Linguistic modules
Modified tokens friend roman countryman
Indexer
Inverted index
friend
roman
countryman
2 4
2
13 16
1
Documents to
be indexed
Friends, Romans, countrymen.

Parsing a document
 What format is it in?
 pdf/word/excel/html?
 What language is it in?
 What character set is in use?
 (CP1252, UTF-8, …)
Each of these is a classification problem, which
we will study later in the course.
But these tasks are often done heuristically …
Sec. 2.1

Complications: Format/language
 Documents being indexed can include docs from
many different languages
 A single index may contain terms from many languages.
 Sometimes a document or its components can
contain multiple languages/formats
 French email with a German pdf attachment.
 French email quote clauses from an English-language
contract
 There are commercial and open source libraries that
can handle a lot of this stuff
Sec. 2.1

Complications: What is a document?
We return from our query “documents” but there are
often interesting questions of grain size:
What is a unit document?
 A file?
 An email? (Perhaps one of many in a single mbox file)
 What about an email with 5 attachments?
 A group of files (e.g., PPT or LaTeX split over HTML pages)
Sec. 2.1

Introduction to
Tokens

Tokenization
 Input: “Friends, Romans and Countrymen”
 Output: Tokens
 Friends
 Romans
 Countrymen
 A token is an instance of a sequence of characters
 Each such token is now a candidate for an index
entry, after further processing
 Described below
 But what are valid tokens to emit(produce)?
Sec. 2.2.1

Tokenization
 Issues in tokenization:
 Finland’s capital 
Finland AND s? Finlands? Finland’s?
 Hewlett-Packard  Hewlett and Packard as two
tokens? (hyphenation)
 state-of-the-art: break up hyphenated sequence.
 co-education
 lowercase, lower-case, lower case ?
 It can be effective to get the user to put in possible hyphens
 San Francisco: one token or two?
 How do you decide it is one token?
 Splitting tokens on spaces can cause bad retrieval results
Sec. 2.2.1

Numbers
 3/20/91 Mar. 12, 1991 20/3/91
 55 B.C.
 B-52
 My PGP key is 324a3df234cb23e
 (800) 234-2333
 Often have embedded spaces
 Older IR systems may not index numbers
 But often very useful: think about things like looking up error
codes/stacktraces on the web
 (One answer is using n-grams: IIR ch. 3)
 Will often index “meta-data” separately
 Creation date, format, etc.
Sec. 2.2.1

Tokenization: language issues
 French
 L'ensemble  one token or two?
 L ? L’ ? Le ?
 Want l’ensemble to match with un ensemble
 Until at least 2003, it didn’t on Google
 Internationalization!
 German noun compounds are not segmented
 Lebensversicherungsgesellschaftsangestellter
 ‘life insurance company employee’
 German retrieval systems benefit greatly from a compound splitter
module
 Can give a 15% performance boost for German
Sec. 2.2.1

 Chinese and Japanese have no spaces between
words:
 莎拉波娃现在居住在美国东南部的佛罗里达。
 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)
Katakana Hiragana Kanji Romaji
End-user can express query entirely in hiragana!
Sec. 2.2.1

◮ Figure 2.4 Ambiguities in Chinese word segmentation.
The two characters can be treated as one word
meaning ‘monk’ or as a sequence of two words
meaning ‘and’ and ‘still’.
12

 Arabic (or Hebrew) is basically written right to left,
but with certain items like numbers written left to
right
 Words are separated, but letter forms within a word
form complex ligatures
 ← → ← → ← start
 ‘Algeria achieved its independence in 1962 after 132
years of French occupation.’
 With Unicode, the surface presentation is complex, but the
stored form is straightforward
Sec. 2.2.1
‫سنة‬ ‫في‬ ‫الجزائر‬ ‫استقلت‬
1962
‫بعد‬
132
‫الفرنسي‬ ‫االحتالل‬ ‫من‬ ‫عام‬
.

Introduction to
Terms
The things indexed in an IR system

Stop words
 With a stop list, you exclude from the dictionary
entirely the commonest words. Intuition:
 They have little semantic content: the, a, and, to, be
 There are a lot of them: ~30% of postings for top 30 words
 But the trend is away from doing this:
 Good compression techniques (IIR 5) means the space for including
stop words in a system is very small
 Good query optimization techniques (IIR 7) mean you pay little at
query time for including stop words.
 You need them for:
 Phrase queries: “King of Denmark”
 Various song titles, etc.: “Let it be”, “To be or not to be”
 “Relational” queries: “flights to London”
Sec. 2.2.2

Normalization to terms
 We may need to “normalize” words in indexed text
as well as query words into the same form
 We want to match U.S.A. and USA
 Result is terms: a term is a (normalized) word type,
which is an entry in our IR system dictionary
 We most commonly implicitly define equivalence
classes of terms by, e.g.,
 deleting periods to form a term
 U.S.A., USA  USA
 deleting hyphens to form a term
 anti-discriminatory, antidiscriminatory  antidiscriminatory
Sec. 2.2.3

Normalization: other languages
 Accents: e.g., French résumé vs. resume.
 Umlauts: e.g., German: Tuebingen vs. Tübingen
 Should be equivalent
 Most important criterion:
 How are your users like to write their queries for these
words?
 Even in languages that standardly have accents, users
often may not type them
 Often best to normalize to a de-accented term
 Tuebingen, Tübingen, Tubingen  Tubingen
Sec. 2.2.3

Normalization: other languages
 Normalization of things like date forms
 7月30日 vs. 7/30
 Japanese use of kana vs. Chinese characters
 Tokenization and normalization may depend on the
language and so is intertwined with language
detection
 Crucial: Need to “normalize” indexed text as well as
query terms identically
Morgen will ich in MIT …
Is this
German “mit”?
Sec. 2.2.3

Case folding
 Reduce all letters to lower case
 exception: upper case in mid-sentence?
 e.g., General Motors
 Fed vs. fed
 SAIL vs. sail
 Often best to lower case everything, since users will use
lowercase regardless of ‘correct’ capitalization…
 Longstanding Google example: [fixed in 2011…]
 Query C.A.T.
 #1 result is for “cats” (well, Lolcats) not Caterpillar Inc.
Sec. 2.2.3

Normalization to terms
 An alternative to equivalence classing is to do
asymmetric expansion
 An example of where this may be useful
 Enter: window Search: window, windows
 Enter: windows Search: Windows, windows, window
 Enter: Windows Search: Windows
 Potentially more powerful, but less efficient
Sec. 2.2.3

Thesauri and soundex
 Do we handle synonyms and homonyms?
 E.g., by hand-constructed equivalence classes
 car = automobile color = colour
 We can rewrite to form equivalence-class terms
 When the document contains automobile, index it under car-
automobile (and vice-versa)
 Or we can expand a query
 When the query contains automobile, look under car as well
 What about spelling mistakes?
 One approach is Soundex, which forms equivalence classes
of words based on phonetic heuristics
http://en.wikipedia.org/wiki/Soundex
 More in IIR 3 and IIR 9

Introduction to
Stemming and Lemmatization

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
 Lemmatization implies doing “proper” reduction to
dictionary headword form.
Sec. 2.2.4

lemmatization
 in linguistics is the process of grouping together the different
inflected forms of a word so they can be analyzed as a single
item.
 In computational linguistics, lemmatization is the algorithmic
process of determining the lemma for a given word. Since the
process may involve complex tasks such as understanding
context and determining the part of speech of a word in a
sentence (requiring, for example, knowledge of
the grammar of a language) it can be a hard task to
implement a lemmatizes for a new language.
 Unlike stemming, lemmatization can in principle select the
appropriate lemma depending on the context. (wikipedia)
24

Stemming
 Reduce terms to their “roots” before indexing
 “Stemming” suggests crude affix chopping
 language dependent
 e.g., automate(s), automatic, automation all reduced to
automat.
for example compressed
and compression are both
accepted as equivalent to
compress.
for exampl compress and
compress ar both accept
as equival to compress
Sec. 2.2.4

Lemmatisation vr stemming
 Lemmatisation is closely related to stemming. The
difference is that:
 a stemmer operates on a single
word without knowledge of the context, and
therefore cannot discriminate between words which
have different meanings depending on part of
speech.
 However, stemmers are typically easier to implement
and run faster, and the reduced accuracy may not
matter for some applications.
 Commonly listed English parts of speech
are noun, verb, adjective, adverb, pronoun, preposition, conjunction, interjection, and
sometimes article or determiner. 26

Porter’s algorithm
 Commonest algorithm for stemming English
 Results suggest it’s at least as good as other stemming
options
 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.
http://9ol.es/porter_js_demo.html
Sec. 2.2.4

Typical rules in Porter
 sses  ss
 ies  i
 ational  ate
 tional  tion
 Weight of word sensitive rules
 (m>1) EMENT →
 replacement → replac
 cement → cement
Sec. 2.2.4

Other stemmers
 Other stemmers exist:
 Lovins stemmer
 http://www.comp.lancs.ac.uk/computing/research/stemming/general/lovins.htm
 Single-pass, longest suffix removal (about 250 rules)
 Paice/Husk stemmer
 Snowball (http://lucene.apache.org/core/ )
 Full morphological analysis (lemmatization)
 At most modest benefits for retrieval
Sec. 2.2.4

Language-specificity
 The above methods embody transformations that
are
 Language-specific, and often
 Application-specific
 These are “plug-in” addenda to the indexing process
 Both open source and commercial plug-ins are
available for handling these
Sec. 2.2.4

Does stemming help?
 English: very mixed results. Helps recall for some
queries but harms precision on others
 E.g., operative (dentistry) ⇒ oper
 Definitely useful for Spanish, German, Finnish, …
 30% performance gains for Finnish!
Sec. 2.2.4

Introduction to
Faster postings merges:
Skip pointers/Skip lists

Recall basic merge
 Walk through the two postings simultaneously, in
time linear in the total number of postings entries
128
31
2 4 8 41 48 64
1 2 3 8 11 17 21
Brutus
Caesar
2 8
If the list lengths are m and n, the merge takes O(m+n)
operations.
Can we do better?
Yes (if the index isn’t changing too fast).
Sec. 2.3

Augment postings with skip pointers
(at indexing time)
 Why?
 To skip postings that will not figure in the search
results.
 How?
 Where do we place skip pointers?
128
2 4 8 41 48 64
31
1 2 3 8 11 17 21
31
11
41 128
Sec. 2.3

Query processing with skip pointers
128
2 4 8 41 48 64
31
1 2 3 8 11 17 21
31
11
41 128
Suppose we’ve stepped through the lists until we process 8
on each list. We match it and advance.
We then have 41 and 11 on the lower. 11 is smaller.
But the skip successor of 11 on the lower list is 31, so
we can skip ahead past the intervening postings.
Sec. 2.3

Where do we place skips?
 Tradeoff:
 More skips  shorter skip spans  more likely to skip.
But lots of comparisons to skip pointers.
 Fewer skips  few pointer comparison, but then long skip
spans  few successful skips.
Sec. 2.3

Placing skips
 Simple heuristic: for postings of length L, use L
evenly-spaced skip pointers [Moffat and Zobel 1996]
 This ignores the distribution of query terms.
 Easy if the index is relatively static; harder if L keeps
changing because of updates.
 This definitely used to help; with modern hardware it
may not unless you’re memory-based [Bahle et al. 2002]
 The I/O cost of loading a bigger postings list can outweigh
the gains from quicker in memory merging!
Sec. 2.3

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