it is about telorant retrieval in information retrieval.ppt

Introduction to Information Retrieval
Introduction to
Information Retrieval
CS276: Information Retrieval and Web Search
Pandu Nayak and Prabhakar Raghavan
Lecture 3: Dictionaries and tolerant retrieval

Recap of the previous lecture
 The type/token distinction
 Terms are normalized types put in the dictionary
 Tokenization problems:
 Hyphens, apostrophes, compounds, CJK
 Term equivalence classing:
 Numbers, case folding, stemming, lemmatization
 Skip pointers
 Encoding a tree-like structure in a postings list
 Biword indexes for phrases
 Positional indexes for phrases/proximity queries
Ch. 2
2

This lecture
 Dictionary data structures
 “Tolerant” retrieval
 Wild-card queries
 Spelling correction
 Soundex
Ch. 3
3

Dictionary data structures for inverted
indexes
 The dictionary data structure stores the term
vocabulary, document frequency, pointers to each
postings list … in what data structure?
Sec. 3.1
4

A naïve dictionary
 An array of struct:
char[20] int Postings *
20 bytes 4/8 bytes 4/8 bytes
 How do we store a dictionary in memory efficiently?
 How do we quickly look up elements at query time?
Sec. 3.1
5

Dictionary data structures
 Two main choices:
 Hashtables
 Trees
 Some IR systems use hashtables, some trees
Sec. 3.1
6

Hashtables
 Each vocabulary term is hashed to an integer
 (We assume you’ve seen hashtables before)
 Pros:
 Lookup is faster than for a tree: O(1)
 Cons:
 No easy way to find minor variants:
 judgment/judgement
 No prefix search [tolerant retrieval]
 If vocabulary keeps growing, need to occasionally do the
expensive operation of rehashing everything
Sec. 3.1
7

Root
a-m n-z
a-hu hy-m n-sh si-z
a
r
d
v
a
r
k
h
u
y
g
e
n
s
s
i
c
k
l
e
z
y
g
o
t
Tree: binary tree
Sec. 3.1
8

Tree: B-tree
 Definition: Every internal nodel has a number of children
in the interval [a,b] where a, b are appropriate natural
numbers, e.g., [2,4].
a-hu
hy-m
n-z
Sec. 3.1
9

Trees
 Simplest: binary tree
 More usual: B-trees
 Trees require a standard ordering of characters and hence
strings … but we typically have one
 Pros:
 Solves the prefix problem (terms starting with hyp)
 Cons:
 Slower: O(log M) [and this requires balanced tree]
 Rebalancing binary trees is expensive
 But B-trees mitigate the rebalancing problem
Sec. 3.1
10

WILD-CARD QUERIES
11

Wild-card queries: *
 mon*: find all docs containing any word beginning
with “mon”.
 Easy with binary tree (or B-tree) lexicon: retrieve all
words in range: mon ≤ w < moo
 *mon: find words ending in “mon”: harder
 Maintain an additional B-tree for terms backwards.
Can retrieve all words in range: nom ≤ w < non.
Exercise: from this, how can we enumerate all terms
meeting the wild-card query pro*cent ?
Sec. 3.2
12

Query processing
 At this point, we have an enumeration of all terms in
the dictionary that match the wild-card query.
 We still have to look up the postings for each
enumerated term.
 E.g., consider the query:
se*ate AND fil*er
This may result in the execution of many Boolean
AND queries.
Sec. 3.2
13

B-trees handle *’s at the end of a
query term
 How can we handle *’s in the middle of query term?
 co*tion
 We could look up co* AND *tion in a B-tree and
intersect the two term sets
 Expensive
 The solution: transform wild-card queries so that the
*’s occur at the end
 This gives rise to the Permuterm Index.
Sec. 3.2
14

Permuterm index
 For term hello, index under:
 hello$, ello$h, llo$he, lo$hel, o$hell, $hello
where $ is a special symbol.
 Queries:
 X lookup on X$ X* lookup on $X*
 *X lookup on X$* *X* lookup on X*
 X*Y lookup on Y$X* X*Y*Z ??? Exercise!
Query = hel*o
X=hel, Y=o
Lookup o$hel*
Sec. 3.2.1
15

Permuterm query processing
 Rotate query wild-card to the right
 Now use B-tree lookup as before.
 Permuterm problem: ≈ quadruples lexicon size
Empirical observation for English.
Sec. 3.2.1
16

Bigram (k-gram) indexes
 Enumerate all k-grams (sequence of k chars)
occurring in any term
 e.g., from text “April is the cruelest month” we get
the 2-grams (bigrams)
 $ is a special word boundary symbol
 Maintain a second inverted index from bigrams to
dictionary terms that match each bigram.
$a,ap,pr,ri,il,l$,$i,is,s$,$t,th,he,e$,$c,cr,ru,
ue,el,le,es,st,t$, $m,mo,on,nt,h$
Sec. 3.2.2
17

Bigram index example
 The k-gram index finds terms based on a query
consisting of k-grams (here k=2).
mo
on
among
$m mace
along
amortize
madden
among
Sec. 3.2.2
18

Processing wild-cards
 Query mon* can now be run as
 $m AND mo AND on
 Gets terms that match AND version of our wildcard
query.
 But we’d enumerate moon.
 Must post-filter these terms against query.
 Surviving enumerated terms are then looked up in
the term-document inverted index.
 Fast, space efficient (compared to permuterm).
Sec. 3.2.2
19

Processing wild-card queries
 As before, we must execute a Boolean query for each
enumerated, filtered term.
 Wild-cards can result in expensive query execution (very
large disjunctions…)
 pyth* AND prog*
 If you encourage “laziness” people will respond!
 Which web search engines allow wildcard queries?
Search
Type your search terms, use ‘*’ if you need to.
E.g., Alex* will match Alexander.
Sec. 3.2.2
20

SPELLING CORRECTION
21

Spell correction
 Two principal uses
 Correcting document(s) being indexed
 Correcting user queries to retrieve “right” answers
 Two main flavors:
 Isolated word
 Check each word on its own for misspelling
 Will not catch typos resulting in correctly spelled words
 e.g., from  form
 Context-sensitive
 Look at surrounding words,
 e.g., I flew form Heathrow to Narita.
Sec. 3.3
22

Document correction
 Especially needed for OCR’ed documents
 Correction algorithms are tuned for this: rn/m
 Can use domain-specific knowledge
 E.g., OCR can confuse O and D more often than it would confuse O
and I (adjacent on the QWERTY keyboard, so more likely
interchanged in typing).
 But also: web pages and even printed material have
typos
 Goal: the dictionary contains fewer misspellings
 But often we don’t change the documents and
instead fix the query-document mapping
Sec. 3.3
23

Query mis-spellings
 Our principal focus here
 E.g., the query Alanis Morisett
 We can either
 Retrieve documents indexed by the correct spelling, OR
 Return several suggested alternative queries with the
correct spelling
 Did you mean … ?
Sec. 3.3
24

Isolated word correction
 Fundamental premise – there is a lexicon from which
the correct spellings come
 Two basic choices for this
 A standard lexicon such as
 Webster’s English Dictionary
 An “industry-specific” lexicon – hand-maintained
 The lexicon of the indexed corpus
 E.g., all words on the web
 All names, acronyms etc.
 (Including the mis-spellings)
Sec. 3.3.2
25

Isolated word correction
 Given a lexicon and a character sequence Q, return
the words in the lexicon closest to Q
 What’s “closest”?
 We’ll study several alternatives
 Edit distance (Levenshtein distance)
 Weighted edit distance
 n-gram overlap
Sec. 3.3.2
26

Edit distance
 Given two strings S1 and S2, the minimum number of
operations to convert one to the other
 Operations are typically character-level
 Insert, Delete, Replace, (Transposition)
 E.g., the edit distance from dof to dog is 1
 From cat to act is 2 (Just 1 with transpose.)
 from cat to dog is 3.
 Generally found by dynamic programming.
 See http://www.merriampark.com/ld.htm for a nice
example plus an applet.
Sec. 3.3.3
27

Weighted edit distance
 As above, but the weight of an operation depends on
the character(s) involved
 Meant to capture OCR or keyboard errors
Example: m more likely to be mis-typed as n than as q
 Therefore, replacing m by n is a smaller edit distance than
by q
 This may be formulated as a probability model
 Requires weight matrix as input
 Modify dynamic programming to handle weights
Sec. 3.3.3
28

Using edit distances
 Given query, first enumerate all character sequences
within a preset (weighted) edit distance (e.g., 2)
 Intersect this set with list of “correct” words
 Show terms you found to user as suggestions
 Alternatively,
 We can look up all possible corrections in our inverted
index and return all docs … slow
 We can run with a single most likely correction
 The alternatives disempower the user, but save a
round of interaction with the user
Sec. 3.3.4
29

Edit distance to all dictionary terms?
 Given a (mis-spelled) query – do we compute its edit
distance to every dictionary term?
 Expensive and slow
 Alternative?
 How do we cut the set of candidate dictionary
terms?
 One possibility is to use n-gram overlap for this
 This can also be used by itself for spelling correction.
Sec. 3.3.4
30

n-gram overlap
 Enumerate all the n-grams in the query string as well
as in the lexicon
 Use the n-gram index (recall wild-card search) to
retrieve all lexicon terms matching any of the query
n-grams
 Threshold by number of matching n-grams
 Variants – weight by keyboard layout, etc.
Sec. 3.3.4
31

Example with trigrams
 Suppose the text is november
 Trigrams are nov, ove, vem, emb, mbe, ber.
 The query is december
 Trigrams are dec, ece, cem, emb, mbe, ber.
 So 3 trigrams overlap (of 6 in each term)
 How can we turn this into a normalized measure of
overlap?
Sec. 3.3.4
32

One option – Jaccard coefficient
 A commonly-used measure of overlap
 Let X and Y be two sets; then the J.C. is
 Equals 1 when X and Y have the same elements and
zero when they are disjoint
 X and Y don’t have to be of the same size
 Always assigns a number between 0 and 1
 Now threshold to decide if you have a match
 E.g., if J.C. > 0.8, declare a match
Y
X
Y
X 
 /
Sec. 3.3.4
33

lore
lore
Matching trigrams
 Consider the query lord – we wish to identify words
matching 2 of its 3 bigrams (lo, or, rd)
lo
or
rd
alone sloth
morbid
border card
border
ardent
Standard postings “merge” will enumerate …
Adapt this to using Jaccard (or another) measure.
Sec. 3.3.4
34

Context-sensitive spell correction
 Text: I flew from Heathrow to Narita.
 Consider the phrase query “flew form Heathrow”
 We’d like to respond
Did you mean “flew from Heathrow”?
because no docs matched the query phrase.
Sec. 3.3.5
35

Context-sensitive correction
 Need surrounding context to catch this.
 First idea: retrieve dictionary terms close (in
weighted edit distance) to each query term
 Now try all possible resulting phrases with one word
“fixed” at a time
 flew from heathrow
 fled form heathrow
 flea form heathrow
 Hit-based spelling correction: Suggest the alternative
that has lots of hits.
Sec. 3.3.5
36

Exercise
 Suppose that for “flew form Heathrow” we have 7
alternatives for flew, 19 for form and 3 for heathrow.
How many “corrected” phrases will we enumerate in
this scheme?
Sec. 3.3.5
37

Another approach
 Break phrase query into a conjunction of biwords
(Lecture 2).
 Look for biwords that need only one term corrected.
 Enumerate only phrases containing “common”
biwords.
Sec. 3.3.5
38

General issues in spell correction
 We enumerate multiple alternatives for “Did you
mean?”
 Need to figure out which to present to the user
 The alternative hitting most docs
 Query log analysis
 More generally, rank alternatives probabilistically
argmaxcorr P(corr | query)
 From Bayes rule, this is equivalent to
argmaxcorr P(query | corr) * P(corr)
Sec. 3.3.5
39
Noisy channel Language model

SOUNDEX
40

Soundex
 Class of heuristics to expand a query into phonetic
equivalents
 Language specific – mainly for names
 E.g., chebyshev  tchebycheff
 Invented for the U.S. census … in 1918
Sec. 3.4
41

Soundex – typical algorithm
 Turn every token to be indexed into a 4-character
reduced form
 Do the same with query terms
 Build and search an index on the reduced forms
 (when the query calls for a soundex match)
 http://www.creativyst.com/Doc/Articles/SoundEx1/SoundEx1.htm#Top
Sec. 3.4
42

Soundex – typical algorithm
1. Retain the first letter of the word.
2. Change all occurrences of the following letters to '0'
(zero):
'A', E', 'I', 'O', 'U', 'H', 'W', 'Y'.
3. Change letters to digits as follows:
 B, F, P, V  1
 C, G, J, K, Q, S, X, Z  2
 D,T  3
 L  4
 M, N  5
 R  6
Sec. 3.4
43

Soundex continued
4. Remove all pairs of consecutive digits.
5. Remove all zeros from the resulting string.
6. Pad the resulting string with trailing zeros and
return the first four positions, which will be of the
form <uppercase letter> <digit> <digit> <digit>.
E.g., Herman becomes H655.
Will hermann generate the same code?
Sec. 3.4
44

Soundex
 Soundex is the classic algorithm, provided by most
databases (Oracle, Microsoft, …)
 How useful is soundex?
 Not very – for information retrieval
 Okay for “high recall” tasks (e.g., Interpol), though
biased to names of certain nationalities
 Zobel and Dart (1996) show that other algorithms for
phonetic matching perform much better in the
context of IR
Sec. 3.4
45

What queries can we process?
 We have
 Positional inverted index with skip pointers
 Wild-card index
 Spell-correction
 Soundex
 Queries such as
(SPELL(moriset) /3 toron*to) OR SOUNDEX(chaikofski)
46

Exercise
 Draw yourself a diagram showing the various indexes
in a search engine incorporating all the functionality
we have talked about
 Identify some of the key design choices in the index
pipeline:
 Does stemming happen before the Soundex index?
 What about n-grams?
 Given a query, how would you parse and dispatch
sub-queries to the various indexes?
47

Resources
 IIR 3, MG 4.2
 Efficient spell retrieval:
 K. Kukich. Techniques for automatically correcting words in text. ACM
Computing Surveys 24(4), Dec 1992.
 J. Zobel and P. Dart. Finding approximate matches in large lexicons.
Software - practice and experience 25(3), March 1995.
http://citeseer.ist.psu.edu/zobel95finding.html
 Mikael Tillenius: Efficient Generation and Ranking of Spelling Error Corrections.
Master’s thesis at Sweden’s Royal Institute of Technology.
http://citeseer.ist.psu.edu/179155.html
 Nice, easy reading on spell correction:
 Peter Norvig: How to write a spelling corrector
http://norvig.com/spell-correct.html
Sec. 3.5
48

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