Dictionaries Wildcard queries Edit distance Spelling correction Soundex
PV211: Introduction to Information Retrieval
https://www.fi.muni.cz/~sojka/PV211
IIR 3: Dictionaries and tolerant retrieval
Handout version
Petr Sojka, Hinrich Schütze et al.
Faculty of Informatics, Masaryk University, Brno
Center for Information and Language Processing, University of Munich
2024-02-28
(compiled on 2024-02-29 18:20:43)
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Dictionaries Wildcard queries Edit distance Spelling correction Soundex
Overview
1 Dictionaries
2 Wildcard queries
3 Edit distance
4 Spelling correction
5 Soundex
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Take-away
Tolerant retrieval: What to do if there is no exact match
between query term and document term
Wildcard queries
Spelling correction
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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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Dictionaries Wildcard queries Edit distance Spelling correction Soundex
Dictionaries
The dictionary is the data structure for storing the term
vocabulary.
Term vocabulary: the data
Dictionary: the data structure for storing the term vocabulary
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Dictionary as array of fixed-width entries
For each term, we need to store a couple of items:
document frequency
pointer to postings list
. . .
Assume for the time being that we can store this information
in a fixed-length entry.
Assume that we store these entries in an array.
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Dictionary as array of fixed-width entries
term document
frequency
pointer to
postings list
a 656,265 −→
aachen 65 −→
. . . . . . . . .
zulu 221 −→
space needed: 20 bytes 4 bytes 4 bytes
How do we look up a query term qi in this array at query time?
That is: which data structure do we use to locate the entry (row)
in the array where qi is stored?
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Dictionaries Wildcard queries Edit distance Spelling correction Soundex
Data structures for looking up term
Two main classes of data structures: hashes and trees
Some IR systems use hashes, some use trees.
Criteria for when to use hashes vs. trees:
Is there a fixed number of terms or will it keep growing?
What are the relative frequencies with which various keys will
be accessed?
How many terms are we likely to have?
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Dictionaries Wildcard queries Edit distance Spelling correction Soundex
Hashes
Each vocabulary term is hashed into an integer, its row
number in the array.
At query time: hash query term, locate entry in fixed-width
array.
Pros: Lookup in a hash is faster than lookup in a tree.
Lookup time is constant.
Cons
no way to find minor variants (resume vs. résumé)
no prefix search (all terms starting with automat)
need to rehash everything periodically if vocabulary keeps
growing
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Trees
Trees solve the prefix problem (find all terms starting with
automat).
Simplest tree: binary tree.
Search is slightly slower than in hashes: O(log M), where M is
the size of the vocabulary.
O(log M) only holds for balanced trees.
Rebalancing binary trees is expensive.
B-trees mitigate the rebalancing problem.
B-tree definition: every internal node has a number of
children in the interval [a, b] where a, b are appropriate
positive integers, e.g., [2, 4].
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Binary tree
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B-tree
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Wildcard queries
mon*: find all docs containing any term beginning with mon
Easy with B-tree dictionary: retrieve all terms t in the range:
mon ≤ t < moo
*mon: find all docs containing any term ending with mon
Maintain an additional tree for terms backwards.
Then retrieve all terms t in the range: nom ≤ t < non
Result: A set of terms that are matches for wildcard query.
Then retrieve documents that contain any of these terms.
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Dictionaries Wildcard queries Edit distance Spelling correction Soundex
Query processing
At this point, we have an enumeration of all terms in the
dictionary that match the wildcard query.
We still have to look up the postings for each enumerated
term.
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How to handle * in the middle of a term
Example: m*nchen
We could look up m* and *nchen in the B-tree and intersect
the two term sets.
Expensive
Alternative: permuterm index
Basic idea: Rotate every wildcard query, so that the * occurs
at the end.
Store each of these rotations in the dictionary, say, in a B-tree
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Permuterm index
For term hello: add hello$, ello$h, llo$he, lo$hel, o$hell, and
$hello to the B-tree where $ is a special symbol
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Permuterm → term mapping
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Permuterm index
For hello, we’ve stored: hello$, ello$h, llo$he, lo$hel, o$hell,
$hello
Queries
For X, look up X$
For X*, look up $X*
For *X, look up X$*
For *X*, look up X*
For X*Y, look up Y$X*
Example: For hel*o, look up o$hel*
Permuterm index would better be called a permuterm tree.
But permuterm index is the more common name.
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Dictionaries Wildcard queries Edit distance Spelling correction Soundex
Processing a lookup in the permuterm index
Rotate query wildcard to the right
Use B-tree lookup as before
Problem: Permuterm more than quadruples the size of the
dictionary compared to a regular B-tree. (empirical number)
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Dictionaries Wildcard queries Edit distance Spelling correction Soundex
k-gram indexes
More space-efficient than permuterm index
Enumerate all character k-grams (sequence of k characters)
occurring in a term
2-grams are called bigrams.
Example: from April is the cruelest month we get the bigrams:
$a ap pr ri il l$ $i is s$ $t th he e$ $c cr ru ue el le es st t$ $m
mo on nt th h$
$ is a special word boundary symbol, as before.
Maintain an inverted index from bigrams to the terms that
contain the bigram
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Dictionaries Wildcard queries Edit distance Spelling correction Soundex
Postings list in a 3-gram inverted index
etr beetroot metric petrify retrieval✲ ✲ ✲ ✲
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k-gram (bigram, trigram, . . . ) indexes
Note that we now have two different types of inverted indexes
The term-document inverted index for finding documents
based on a query consisting of terms
The k-gram index for finding terms based on a query
consisting of k-grams
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Dictionaries Wildcard queries Edit distance Spelling correction Soundex
Processing wildcarded terms in a bigram index
Query mon* can now be run as:
$m and mo and on
Gets us all terms with the prefix mon . . .
. . . but also many “false positives” like moon.
We must postfilter these terms against query.
Surviving terms are then looked up in the term-document
inverted index.
k-gram index vs. permuterm index
k-gram index is more space efficient.
Permuterm index doesn’t require postfiltering.
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Dictionaries Wildcard queries Edit distance Spelling correction Soundex
Exercise
Google has very limited support for wildcard queries.
For example, this query doesn’t work very well on Google:
[gen* universit*]
Intention: you are looking for the University of Geneva, but
don’t know which accents to use for the French words for
university and Geneva.
According to Google search basics, 2010-04-29: “Note that
the * operator works only on whole words, not parts of words.”
But this is not entirely true. Try [pythag*] and [m*nchen]
Exercise: Why doesn’t Google fully support wildcard queries?
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Processing wildcard queries in the term-document index
Problem 1: we must potentially execute a large number of
Boolean queries.
Most straightforward semantics: Conjunction of disjunctions
For [gen* universit*]: geneva university or geneva université
or genève university or genève université or general
universities or . . .
Very expensive
Problem 2: Users hate to type.
If abbreviated queries like [pyth* theo*] for [pythagoras’
theorem] are allowed, users will use them a lot.
This would significantly increase the cost of answering queries.
Somewhat alleviated by Google Suggest
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Dictionaries Wildcard queries Edit distance Spelling correction Soundex
Spelling correction
Two principal uses
Correcting documents being indexed
Correcting user queries
Two different methods for spelling correction
Isolated word spelling correction
Check each word on its own for misspelling
Will not catch typos resulting in correctly spelled words, e.g.,
an asteroid that fell form the sky
Context-sensitive spelling correction
Look at surrounding words
Can correct form/from error above
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Correcting documents
We are not interested in interactive spelling correction of
documents (e.g., MS Word) in this class.
In IR, we use document correction primarily for OCR’ed
documents. (OCR = optical character recognition)
The general philosophy in IR is: do not change the documents.
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Dictionaries Wildcard queries Edit distance Spelling correction Soundex
Correcting queries
First: isolated word spelling correction
Premise 1: There is a list of “correct words” from which the
correct spellings come.
Premise 2: We have a way of computing the distance between
a misspelled word and a correct word.
Simple spelling correction algorithm: return the “correct”
word that has the smallest distance to the misspelled word.
Example: informaton → information
For the list of correct words, we can use the vocabulary of all
words that occur in our collection.
Why is this problematic?
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Alternatives to using the term vocabulary
A standard dictionary (Webster’s, OED, etc.)
An industry-specific dictionary (for specialized IR systems)
The term vocabulary of the collection, appropriately weighted
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Dictionaries Wildcard queries Edit distance Spelling correction Soundex
Distance between misspelled word and “correct” word
We will study several alternatives.
Edit distance and Levenshtein distance
Weighted edit distance
k-gram overlap
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Dictionaries Wildcard queries Edit distance Spelling correction Soundex
Edit distance
The edit distance between string s1 and string s2 is the
minimum number of basic operations that convert s1 to s2.
Levenshtein distance: The admissible basic operations are
insert, delete, and replace
Levenshtein distance dog-do: 1
Levenshtein distance cat-cart: 1
Levenshtein distance cat-cut: 1
Levenshtein distance cat-act: 2
Damerau-Levenshtein distance cat-act: 1
Damerau-Levenshtein includes transposition as a fourth
possible operation.
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Dictionaries Wildcard queries Edit distance Spelling correction Soundex
Levenshtein distance: Computation
f a s t
0 1 2 3 4
c 1 1 2 3 4
a 2 2 1 2 3
t 3 3 2 2 2
s 4 4 3 2 3
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Levenshtein distance: Algorithm
LevenshteinDistance(s1, s2)
1 for i ← 0 to |s1|
2 do m[i, 0] = i
3 for j ← 0 to |s2|
4 do m[0, j] = j
5 for i ← 1 to |s1|
6 do for j ← 1 to |s2|
7 do if s1[i] = s2[j]
8 then m[i, j] = min{m[i-1, j]+1, m[i, j-1]+1, m[i-1, j-1]}
9 else m[i, j] = min{m[i-1, j]+1, m[i, j-1]+1, m[i-1, j-1]+1}
10 return m[|s1|, |s2|]
Operations: insert (cost 1), delete (cost 1), replace (cost 1), copy (cost 0)
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Dictionaries Wildcard queries Edit distance Spelling correction Soundex
Levenshtein distance: Algorithm
LevenshteinDistance(s1, s2)
1 for i ← 0 to |s1|
2 do m[i, 0] = i
3 for j ← 0 to |s2|
4 do m[0, j] = j
5 for i ← 1 to |s1|
6 do for j ← 1 to |s2|
7 do if s1[i] = s2[j]
8 then m[i, j] = min{m[i-1, j]+1, m[i, j-1]+1, m[i-1, j-1]}
9 else m[i, j] = min{m[i-1, j]+1, m[i, j-1]+1, m[i-1, j-1]+1}
10 return m[|s1|, |s2|]
Operations: insert (cost 1), delete (cost 1), replace (cost 1), copy (cost 0)
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Dictionaries Wildcard queries Edit distance Spelling correction Soundex
Levenshtein distance: Algorithm
LevenshteinDistance(s1, s2)
1 for i ← 0 to |s1|
2 do m[i, 0] = i
3 for j ← 0 to |s2|
4 do m[0, j] = j
5 for i ← 1 to |s1|
6 do for j ← 1 to |s2|
7 do if s1[i] = s2[j]
8 then m[i, j] = min{m[i-1, j]+1, m[i, j-1]+1, m[i-1, j-1]}
9 else m[i, j] = min{m[i-1, j]+1, m[i, j-1]+1, m[i-1, j-1]+1}
10 return m[|s1|, |s2|]
Operations: insert (cost 1), delete (cost 1), replace (cost 1), copy (cost 0)
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Dictionaries Wildcard queries Edit distance Spelling correction Soundex
Levenshtein distance: Algorithm
LevenshteinDistance(s1, s2)
1 for i ← 0 to |s1|
2 do m[i, 0] = i
3 for j ← 0 to |s2|
4 do m[0, j] = j
5 for i ← 1 to |s1|
6 do for j ← 1 to |s2|
7 do if s1[i] = s2[j]
8 then m[i, j] = min{m[i-1, j]+1, m[i, j-1]+1, m[i-1, j-1]}
9 else m[i, j] = min{m[i-1, j]+1, m[i, j-1]+1, m[i-1, j-1]+1}
10 return m[|s1|, |s2|]
Operations: insert (cost 1), delete (cost 1), replace (cost 1), copy (cost 0)
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Dictionaries Wildcard queries Edit distance Spelling correction Soundex
Levenshtein distance: Algorithm
LevenshteinDistance(s1, s2)
1 for i ← 0 to |s1|
2 do m[i, 0] = i
3 for j ← 0 to |s2|
4 do m[0, j] = j
5 for i ← 1 to |s1|
6 do for j ← 1 to |s2|
7 do if s1[i] = s2[j]
8 then m[i, j] = min{m[i − 1, j] + 1, m[i, j − 1] + 1, m[i − 1, j − 1]}
9 else m[i, j] = min{m[i − 1, j] + 1, m[i, j − 1] + 1, m[i − 1, j − 1] + 1}
10 return m[|s1|, |s2|]
Operations: insert (cost 1), delete (cost 1), replace (cost 1), copy (cost 0)
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Dictionaries Wildcard queries Edit distance Spelling correction Soundex
Levenshtein distance: algorithm
LevenshteinDistance(s1, s2)
1 for i ← 1 to |s1|
2 do m[i, 0] = i
3 for j ← 0 to |s2|
4 do m[0, j] = j
5 for i ← 0 to |s1|
6 do for j ← 1 to |s2|
7 do if s1[i] = s2[j]
8 then m[i, j] = min{m[i − 1, j] + 1, m[i, j − 1] + 1, m[i − 1, j − 1]}
9 else m[i, j] = min{m[i − 1, j] + 1, m[i, j − 1] + 1, m[i − 1, j − 1] + 1}
10 return m[|s1|, |s2|]
Operations: insert, delete, replace, copy
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Dictionaries Wildcard queries Edit distance Spelling correction Soundex
Levenshtein distance: algorithm
LevenshteinDistance(s1, s2)
1 for i ← 1 to |s1|
2 do m[i, 0] = i
3 for j ← 0 to |s2|
4 do m[0, j] = j
5 for i ← 0 to |s1|
6 do for j ← 1 to |s2|
7 do if s1[i] = s2[j]
8 then m[i, j] = min{m[i − 1, j] + 1, m[i, j − 1] + 1, m[i − 1, j − 1]}
9 else m[i, j] = min{m[i − 1, j] + 1, m[i, j − 1] + 1, m[i − 1, j − 1] + 1}
10 return m[|s1|, |s2|]
Operations: insert, delete, replace, copy
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Dictionaries Wildcard queries Edit distance Spelling correction Soundex
Levenshtein distance: Example
f a s t
0 1 1 2 2 3 3 4 4
c
1
1
1 2
2 1
2 3
2 2
3 4
3 3
4 5
4 4
a
2
2
2 2
3 2
1 3
3 1
3 4
2 2
4 5
3 3
t
3
3
3 3
4 3
3 2
4 2
2 3
3 2
2 4
3 2
s
4
4
4 4
5 4
4 3
5 3
2 3
4 2
3 3
3 3
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Dictionaries Wildcard queries Edit distance Spelling correction Soundex
Each cell of Levenshtein matrix
cost of getting here from
my upper left neighbor
(copy or replace)
cost of getting here from
my upper neighbor (de-
lete)
cost of getting here from
my left neighbor (insert)
the minimum of the
three possible “movements”;
the cheapest
way of getting here
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Dictionaries Wildcard queries Edit distance Spelling correction Soundex
Levenshtein distance: Example
f a s t
0 1 1 2 2 3 3 4 4
c
1
1
1 2
2 1
2 3
2 2
3 4
3 3
4 5
4 4
a
2
2
2 2
3 2
1 3
3 1
3 4
2 2
4 5
3 3
t
3
3
3 3
4 3
3 2
4 2
2 3
3 2
2 4
3 2
s
4
4
4 4
5 4
4 3
5 3
2 3
4 2
3 3
3 3
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Dictionaries Wildcard queries Edit distance Spelling correction Soundex
Dynamic programming (Cormen et al.)
Optimal substructure: The optimal solution to the problem
contains within it subsolutions, i.e., optimal solutions to
subproblems.
Overlapping subsolutions: The subsolutions overlap. These
subsolutions are computed over and over again when
computing the global optimal solution in a brute-force
algorithm.
Subproblem in the case of edit distance: what is the edit
distance of two prefixes
Overlapping subsolutions: We need most distances of prefixes
3 times – this corresponds to moving right, diagonally, down.
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Dictionaries Wildcard queries Edit distance Spelling correction Soundex
Weighted edit distance
As above, but weight of an operation depends on the
characters involved.
Meant to capture keyboard errors, e.g., m more likely to be
mistyped as n than as q.
Therefore, replacing m by n is a smaller edit distance than by
q.
We now require a weight matrix as input.
Modify dynamic programming to handle weights
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Dictionaries Wildcard queries Edit distance Spelling correction Soundex
Using edit distance for spelling correction
Given query, first enumerate all character sequences within a
preset (possibly weighted) edit distance
Intersect this set with our list of “correct” words
Then suggest terms in the intersection to the user.
→ exercise in a few slides
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Dictionaries Wildcard queries Edit distance Spelling correction Soundex
Exercise
1 Compute Levenshtein distance matrix for oslo – snow
2 What are the Levenshtein editing operations that transform
cat into catcat?
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Dictionaries Wildcard queries Edit distance Spelling correction Soundex
s n o w
0 1 1 2 2 3 3 4 4
o
1
1
s
2
2
l
3
3
o
4
4
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Dictionaries Wildcard queries Edit distance Spelling correction Soundex
s n o w
0 1 1 2 2 3 3 4 4
o
1
1
1 2
2 ?
s
2
2
l
3
3
o
4
4
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Dictionaries Wildcard queries Edit distance Spelling correction Soundex
s n o w
0 1 1 2 2 3 3 4 4
o
1
1
1 2
2 1
s
2
2
l
3
3
o
4
4
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Dictionaries Wildcard queries Edit distance Spelling correction Soundex
s n o w
0 1 1 2 2 3 3 4 4
o
1
1
1 2
2 1
2 3
2 ?
s
2
2
l
3
3
o
4
4
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Dictionaries Wildcard queries Edit distance Spelling correction Soundex
s n o w
0 1 1 2 2 3 3 4 4
o
1
1
1 2
2 1
2 3
2 2
s
2
2
l
3
3
o
4
4
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Dictionaries Wildcard queries Edit distance Spelling correction Soundex
s n o w
0 1 1 2 2 3 3 4 4
o
1
1
1 2
2 1
2 3
2 2
2 4
3 ?
s
2
2
l
3
3
o
4
4
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Dictionaries Wildcard queries Edit distance Spelling correction Soundex
s n o w
0 1 1 2 2 3 3 4 4
o
1
1
1 2
2 1
2 3
2 2
2 4
3 2
s
2
2
l
3
3
o
4
4
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Dictionaries Wildcard queries Edit distance Spelling correction Soundex
s n o w
0 1 1 2 2 3 3 4 4
o
1
1
1 2
2 1
2 3
2 2
2 4
3 2
4 5
3 ?
s
2
2
l
3
3
o
4
4
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Dictionaries Wildcard queries Edit distance Spelling correction Soundex
s n o w
0 1 1 2 2 3 3 4 4
o
1
1
1 2
2 1
2 3
2 2
2 4
3 2
4 5
3 3
s
2
2
l
3
3
o
4
4
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Dictionaries Wildcard queries Edit distance Spelling correction Soundex
s n o w
0 1 1 2 2 3 3 4 4
o
1
1
1 2
2 1
2 3
2 2
2 4
3 2
4 5
3 3
s
2
2
1 2
3 ?
l
3
3
o
4
4
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Dictionaries Wildcard queries Edit distance Spelling correction Soundex
s n o w
0 1 1 2 2 3 3 4 4
o
1
1
1 2
2 1
2 3
2 2
2 4
3 2
4 5
3 3
s
2
2
1 2
3 1
l
3
3
o
4
4
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Dictionaries Wildcard queries Edit distance Spelling correction Soundex
s n o w
0 1 1 2 2 3 3 4 4
o
1
1
1 2
2 1
2 3
2 2
2 4
3 2
4 5
3 3
s
2
2
1 2
3 1
2 3
2 ?
l
3
3
o
4
4
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Dictionaries Wildcard queries Edit distance Spelling correction Soundex
s n o w
0 1 1 2 2 3 3 4 4
o
1
1
1 2
2 1
2 3
2 2
2 4
3 2
4 5
3 3
s
2
2
1 2
3 1
2 3
2 2
l
3
3
o
4
4
Sojka, IIR Group: PV211: Dictionaries and tolerant retrieval 62 / 111
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s n o w
0 1 1 2 2 3 3 4 4
o
1
1
1 2
2 1
2 3
2 2
2 4
3 2
4 5
3 3
s
2
2
1 2
3 1
2 3
2 2
3 3
3 ?
l
3
3
o
4
4
Sojka, IIR Group: PV211: Dictionaries and tolerant retrieval 63 / 111
Dictionaries Wildcard queries Edit distance Spelling correction Soundex
s n o w
0 1 1 2 2 3 3 4 4
o
1
1
1 2
2 1
2 3
2 2
2 4
3 2
4 5
3 3
s
2
2
1 2
3 1
2 3
2 2
3 3
3 3
l
3
3
o
4
4
Sojka, IIR Group: PV211: Dictionaries and tolerant retrieval 64 / 111
Dictionaries Wildcard queries Edit distance Spelling correction Soundex
s n o w
0 1 1 2 2 3 3 4 4
o
1
1
1 2
2 1
2 3
2 2
2 4
3 2
4 5
3 3
s
2
2
1 2
3 1
2 3
2 2
3 3
3 3
3 4
4 ?
l
3
3
o
4
4
Sojka, IIR Group: PV211: Dictionaries and tolerant retrieval 65 / 111
Dictionaries Wildcard queries Edit distance Spelling correction Soundex
s n o w
0 1 1 2 2 3 3 4 4
o
1
1
1 2
2 1
2 3
2 2
2 4
3 2
4 5
3 3
s
2
2
1 2
3 1
2 3
2 2
3 3
3 3
3 4
4 3
l
3
3
o
4
4
Sojka, IIR Group: PV211: Dictionaries and tolerant retrieval 66 / 111
Dictionaries Wildcard queries Edit distance Spelling correction Soundex
s n o w
0 1 1 2 2 3 3 4 4
o
1
1
1 2
2 1
2 3
2 2
2 4
3 2
4 5
3 3
s
2
2
1 2
3 1
2 3
2 2
3 3
3 3
3 4
4 3
l
3
3
3 2
4 ?
o
4
4
Sojka, IIR Group: PV211: Dictionaries and tolerant retrieval 67 / 111
Dictionaries Wildcard queries Edit distance Spelling correction Soundex
s n o w
0 1 1 2 2 3 3 4 4
o
1
1
1 2
2 1
2 3
2 2
2 4
3 2
4 5
3 3
s
2
2
1 2
3 1
2 3
2 2
3 3
3 3
3 4
4 3
l
3
3
3 2
4 2
o
4
4
Sojka, IIR Group: PV211: Dictionaries and tolerant retrieval 68 / 111
Dictionaries Wildcard queries Edit distance Spelling correction Soundex
s n o w
0 1 1 2 2 3 3 4 4
o
1
1
1 2
2 1
2 3
2 2
2 4
3 2
4 5
3 3
s
2
2
1 2
3 1
2 3
2 2
3 3
3 3
3 4
4 3
l
3
3
3 2
4 2
2 3
3 ?
o
4
4
Sojka, IIR Group: PV211: Dictionaries and tolerant retrieval 69 / 111
Dictionaries Wildcard queries Edit distance Spelling correction Soundex
s n o w
0 1 1 2 2 3 3 4 4
o
1
1
1 2
2 1
2 3
2 2
2 4
3 2
4 5
3 3
s
2
2
1 2
3 1
2 3
2 2
3 3
3 3
3 4
4 3
l
3
3
3 2
4 2
2 3
3 2
o
4
4
Sojka, IIR Group: PV211: Dictionaries and tolerant retrieval 70 / 111
Dictionaries Wildcard queries Edit distance Spelling correction Soundex
s n o w
0 1 1 2 2 3 3 4 4
o
1
1
1 2
2 1
2 3
2 2
2 4
3 2
4 5
3 3
s
2
2
1 2
3 1
2 3
2 2
3 3
3 3
3 4
4 3
l
3
3
3 2
4 2
2 3
3 2
3 4
3 ?
o
4
4
Sojka, IIR Group: PV211: Dictionaries and tolerant retrieval 71 / 111
Dictionaries Wildcard queries Edit distance Spelling correction Soundex
s n o w
0 1 1 2 2 3 3 4 4
o
1
1
1 2
2 1
2 3
2 2
2 4
3 2
4 5
3 3
s
2
2
1 2
3 1
2 3
2 2
3 3
3 3
3 4
4 3
l
3
3
3 2
4 2
2 3
3 2
3 4
3 3
o
4
4
Sojka, IIR Group: PV211: Dictionaries and tolerant retrieval 72 / 111
Dictionaries Wildcard queries Edit distance Spelling correction Soundex
s n o w
0 1 1 2 2 3 3 4 4
o
1
1
1 2
2 1
2 3
2 2
2 4
3 2
4 5
3 3
s
2
2
1 2
3 1
2 3
2 2
3 3
3 3
3 4
4 3
l
3
3
3 2
4 2
2 3
3 2
3 4
3 3
4 4
4 ?
o
4
4
Sojka, IIR Group: PV211: Dictionaries and tolerant retrieval 73 / 111
Dictionaries Wildcard queries Edit distance Spelling correction Soundex
s n o w
0 1 1 2 2 3 3 4 4
o
1
1
1 2
2 1
2 3
2 2
2 4
3 2
4 5
3 3
s
2
2
1 2
3 1
2 3
2 2
3 3
3 3
3 4
4 3
l
3
3
3 2
4 2
2 3
3 2
3 4
3 3
4 4
4 4
o
4
4
Sojka, IIR Group: PV211: Dictionaries and tolerant retrieval 74 / 111
Dictionaries Wildcard queries Edit distance Spelling correction Soundex
s n o w
0 1 1 2 2 3 3 4 4
o
1
1
1 2
2 1
2 3
2 2
2 4
3 2
4 5
3 3
s
2
2
1 2
3 1
2 3
2 2
3 3
3 3
3 4
4 3
l
3
3
3 2
4 2
2 3
3 2
3 4
3 3
4 4
4 4
o
4
4
4 3
5 ?
Sojka, IIR Group: PV211: Dictionaries and tolerant retrieval 75 / 111
Dictionaries Wildcard queries Edit distance Spelling correction Soundex
s n o w
0 1 1 2 2 3 3 4 4
o
1
1
1 2
2 1
2 3
2 2
2 4
3 2
4 5
3 3
s
2
2
1 2
3 1
2 3
2 2
3 3
3 3
3 4
4 3
l
3
3
3 2
4 2
2 3
3 2
3 4
3 3
4 4
4 4
o
4
4
4 3
5 3
Sojka, IIR Group: PV211: Dictionaries and tolerant retrieval 76 / 111
Dictionaries Wildcard queries Edit distance Spelling correction Soundex
s n o w
0 1 1 2 2 3 3 4 4
o
1
1
1 2
2 1
2 3
2 2
2 4
3 2
4 5
3 3
s
2
2
1 2
3 1
2 3
2 2
3 3
3 3
3 4
4 3
l
3
3
3 2
4 2
2 3
3 2
3 4
3 3
4 4
4 4
o
4
4
4 3
5 3
3 3
4 ?
Sojka, IIR Group: PV211: Dictionaries and tolerant retrieval 77 / 111
Dictionaries Wildcard queries Edit distance Spelling correction Soundex
s n o w
0 1 1 2 2 3 3 4 4
o
1
1
1 2
2 1
2 3
2 2
2 4
3 2
4 5
3 3
s
2
2
1 2
3 1
2 3
2 2
3 3
3 3
3 4
4 3
l
3
3
3 2
4 2
2 3
3 2
3 4
3 3
4 4
4 4
o
4
4
4 3
5 3
3 3
4 3
Sojka, IIR Group: PV211: Dictionaries and tolerant retrieval 78 / 111
Dictionaries Wildcard queries Edit distance Spelling correction Soundex
s n o w
0 1 1 2 2 3 3 4 4
o
1
1
1 2
2 1
2 3
2 2
2 4
3 2
4 5
3 3
s
2
2
1 2
3 1
2 3
2 2
3 3
3 3
3 4
4 3
l
3
3
3 2
4 2
2 3
3 2
3 4
3 3
4 4
4 4
o
4
4
4 3
5 3
3 3
4 3
2 4
4 ?
Sojka, IIR Group: PV211: Dictionaries and tolerant retrieval 79 / 111
Dictionaries Wildcard queries Edit distance Spelling correction Soundex
s n o w
0 1 1 2 2 3 3 4 4
o
1
1
1 2
2 1
2 3
2 2
2 4
3 2
4 5
3 3
s
2
2
1 2
3 1
2 3
2 2
3 3
3 3
3 4
4 3
l
3
3
3 2
4 2
2 3
3 2
3 4
3 3
4 4
4 4
o
4
4
4 3
5 3
3 3
4 3
2 4
4 2
Sojka, IIR Group: PV211: Dictionaries and tolerant retrieval 80 / 111
Dictionaries Wildcard queries Edit distance Spelling correction Soundex
s n o w
0 1 1 2 2 3 3 4 4
o
1
1
1 2
2 1
2 3
2 2
2 4
3 2
4 5
3 3
s
2
2
1 2
3 1
2 3
2 2
3 3
3 3
3 4
4 3
l
3
3
3 2
4 2
2 3
3 2
3 4
3 3
4 4
4 4
o
4
4
4 3
5 3
3 3
4 3
2 4
4 2
4 5
3 ?
Sojka, IIR Group: PV211: Dictionaries and tolerant retrieval 81 / 111
Dictionaries Wildcard queries Edit distance Spelling correction Soundex
s n o w
0 1 1 2 2 3 3 4 4
o
1
1
1 2
2 1
2 3
2 2
2 4
3 2
4 5
3 3
s
2
2
1 2
3 1
2 3
2 2
3 3
3 3
3 4
4 3
l
3
3
3 2
4 2
2 3
3 2
3 4
3 3
4 4
4 4
o
4
4
4 3
5 3
3 3
4 3
2 4
4 2
4 5
3 3
Sojka, IIR Group: PV211: Dictionaries and tolerant retrieval 82 / 111
Dictionaries Wildcard queries Edit distance Spelling correction Soundex
s n o w
0 1 1 2 2 3 3 4 4
o
1
1
1 2
2 1
2 3
2 2
2 4
3 2
4 5
3 3
s
2
2
1 2
3 1
2 3
2 2
3 3
3 3
3 4
4 3
l
3
3
3 2
4 2
2 3
3 2
3 4
3 3
4 4
4 4
o
4
4
4 3
5 3
3 3
4 3
2 4
4 2
4 5
3 3
Sojka, IIR Group: PV211: Dictionaries and tolerant retrieval 83 / 111
Dictionaries Wildcard queries Edit distance Spelling correction Soundex
s n o w
0 1 1 2 2 3 3 4 4
o
1
1
1 2
2 1
2 3
2 2
2 4
3 2
4 5
3 3
s
2
2
1 2
3 1
2 3
2 2
3 3
3 3
3 4
4 3
l
3
3
3 2
4 2
2 3
3 2
3 4
3 3
4 4
4 4
o
4
4
4 3
5 3
3 3
4 3
2 4
4 2
4 5
3 3
How do I read out the editing operations that transform oslo into snow?
Sojka, IIR Group: PV211: Dictionaries and tolerant retrieval 84 / 111
Dictionaries Wildcard queries Edit distance Spelling correction Soundex
s n o w
0 1 1 2 2 3 3 4 4
o
1
1
1 2
2 1
2 3
2 2
2 4
3 2
4 5
3 3
s
2
2
1 2
3 1
2 3
2 2
3 3
3 3
3 4
4 3
l
3
3
3 2
4 2
2 3
3 2
3 4
3 3
4 4
4 4
o
4
4
4 3
5 3
3 3
4 3
2 4
4 2
4 5
3 3
cost operation input output
1 insert * w
Sojka, IIR Group: PV211: Dictionaries and tolerant retrieval 85 / 111
Dictionaries Wildcard queries Edit distance Spelling correction Soundex
s n o w
0 1 1 2 2 3 3 4 4
o
1
1
1 2
2 1
2 3
2 2
2 4
3 2
4 5
3 3
s
2
2
1 2
3 1
2 3
2 2
3 3
3 3
3 4
4 3
l
3
3
3 2
4 2
2 3
3 2
3 4
3 3
4 4
4 4
o
4
4
4 3
5 3
3 3
4 3
2 4
4 2
4 5
3 3
cost operation input output
0 (copy) o o
1 insert * w
Sojka, IIR Group: PV211: Dictionaries and tolerant retrieval 86 / 111
Dictionaries Wildcard queries Edit distance Spelling correction Soundex
s n o w
0 1 1 2 2 3 3 4 4
o
1
1
1 2
2 1
2 3
2 2
2 4
3 2
4 5
3 3
s
2
2
1 2
3 1
2 3
2 2
3 3
3 3
3 4
4 3
l
3
3
3 2
4 2
2 3
3 2
3 4
3 3
4 4
4 4
o
4
4
4 3
5 3
3 3
4 3
2 4
4 2
4 5
3 3
cost operation input output
1 replace l n
0 (copy) o o
1 insert * w
Sojka, IIR Group: PV211: Dictionaries and tolerant retrieval 87 / 111
Dictionaries Wildcard queries Edit distance Spelling correction Soundex
s n o w
0 1 1 2 2 3 3 4 4
o
1
1
1 2
2 1
2 3
2 2
2 4
3 2
4 5
3 3
s
2
2
1 2
3 1
2 3
2 2
3 3
3 3
3 4
4 3
l
3
3
3 2
4 2
2 3
3 2
3 4
3 3
4 4
4 4
o
4
4
4 3
5 3
3 3
4 3
2 4
4 2
4 5
3 3
cost operation input output
0 (copy) s s
1 replace l n
0 (copy) o o
1 insert * w
Sojka, IIR Group: PV211: Dictionaries and tolerant retrieval 88 / 111
Dictionaries Wildcard queries Edit distance Spelling correction Soundex
s n o w
0 1 1 2 2 3 3 4 4
o
1
1
1 2
2 1
2 3
2 2
2 4
3 2
4 5
3 3
s
2
2
1 2
3 1
2 3
2 2
3 3
3 3
3 4
4 3
l
3
3
3 2
4 2
2 3
3 2
3 4
3 3
4 4
4 4
o
4
4
4 3
5 3
3 3
4 3
2 4
4 2
4 5
3 3
cost operation input output
1 delete o *
0 (copy) s s
1 replace l n
0 (copy) o o
1 insert * w
Sojka, IIR Group: PV211: Dictionaries and tolerant retrieval 89 / 111
Dictionaries Wildcard queries Edit distance Spelling correction Soundex
c a t c a t
0 1 1 2 2 3 3 4 4 5 5 6 6
c
1
1
0 2
2 0
2 3
1 1
3 4
2 2
3 5
3 3
5 6
4 4
6 7
5 5
a
2
2
2 1
3 1
0 2
2 0
2 3
1 1
3 4
2 2
3 5
3 3
5 6
4 4
t
3
3
3 2
4 2
2 1
3 1
0 2
2 0
2 3
1 1
3 4
2 2
3 5
3 3
Sojka, IIR Group: PV211: Dictionaries and tolerant retrieval 90 / 111
Dictionaries Wildcard queries Edit distance Spelling correction Soundex
c a t c a t
0 1 1 2 2 3 3 4 4 5 5 6 6
c
1
1
0 2
2 0
2 3
1 1
3 4
2 2
3 5
3 3
5 6
4 4
6 7
5 5
a
2
2
2 1
3 1
0 2
2 0
2 3
1 1
3 4
2 2
3 5
3 3
5 6
4 4
t
3
3
3 2
4 2
2 1
3 1
0 2
2 0
2 3
1 1
3 4
2 2
3 5
3 3
cost operation input output
1 insert * c
1 insert * a
1 insert * t
0 (copy) c c
0 (copy) a a
0 (copy) t t
Sojka, IIR Group: PV211: Dictionaries and tolerant retrieval 91 / 111
Dictionaries Wildcard queries Edit distance Spelling correction Soundex
c a t c a t
0 1 1 2 2 3 3 4 4 5 5 6 6
c
1
1
0 2
2 0
2 3
1 1
3 4
2 2
3 5
3 3
5 6
4 4
6 7
5 5
a
2
2
2 1
3 1
0 2
2 0
2 3
1 1
3 4
2 2
3 5
3 3
5 6
4 4
t
3
3
3 2
4 2
2 1
3 1
0 2
2 0
2 3
1 1
3 4
2 2
3 5
3 3
cost operation input output
0 (copy) c c
1 insert * a
1 insert * t
1 insert * c
0 (copy) a a
0 (copy) t t
Sojka, IIR Group: PV211: Dictionaries and tolerant retrieval 92 / 111
Dictionaries Wildcard queries Edit distance Spelling correction Soundex
c a t c a t
0 1 1 2 2 3 3 4 4 5 5 6 6
c
1
1
0 2
2 0
2 3
1 1
3 4
2 2
3 5
3 3
5 6
4 4
6 7
5 5
a
2
2
2 1
3 1
0 2
2 0
2 3
1 1
3 4
2 2
3 5
3 3
5 6
4 4
t
3
3
3 2
4 2
2 1
3 1
0 2
2 0
2 3
1 1
3 4
2 2
3 5
3 3
cost operation input output
0 (copy) c c
0 (copy) a a
1 insert * t
1 insert * c
1 insert * a
0 (copy) t t
Sojka, IIR Group: PV211: Dictionaries and tolerant retrieval 93 / 111
Dictionaries Wildcard queries Edit distance Spelling correction Soundex
c a t c a t
0 1 1 2 2 3 3 4 4 5 5 6 6
c
1
1
0 2
2 0
2 3
1 1
3 4
2 2
3 5
3 3
5 6
4 4
6 7
5 5
a
2
2
2 1
3 1
0 2
2 0
2 3
1 1
3 4
2 2
3 5
3 3
5 6
4 4
t
3
3
3 2
4 2
2 1
3 1
0 2
2 0
2 3
1 1
3 4
2 2
3 5
3 3
cost operation input output
0 (copy) c c
0 (copy) a a
0 (copy) t t
1 insert * c
1 insert * a
1 insert * t
Sojka, IIR Group: PV211: Dictionaries and tolerant retrieval 94 / 111
Dictionaries Wildcard queries Edit distance Spelling correction Soundex
Spelling correction
Now that we can compute edit distance: how to use it for
isolated word spelling correction – this is the last slide in this
section.
k-gram indexes for isolated word spelling correction.
Context-sensitive spelling correction
General issues
Sojka, IIR Group: PV211: Dictionaries and tolerant retrieval 96 / 111
Dictionaries Wildcard queries Edit distance Spelling correction Soundex
k-gram indexes for spelling correction
Enumerate all k-grams in the query term
Example: bigram index, misspelled word bordroom
Bigrams: bo, or, rd, dr, ro, oo, om
Use the k-gram index to retrieve “correct” words that match
query term k-grams
Threshold by number of matching k-grams
E.g., only vocabulary terms that differ by at most 3 k-grams
Sojka, IIR Group: PV211: Dictionaries and tolerant retrieval 97 / 111
Dictionaries Wildcard queries Edit distance Spelling correction Soundex
k-gram indexes for spelling correction: bordroom
rd aboard ardent boardroom border
or border lord morbid sordid
bo aboard about boardroom border
✲ ✲ ✲ ✲
✲ ✲ ✲ ✲
✲ ✲ ✲ ✲
Sojka, IIR Group: PV211: Dictionaries and tolerant retrieval 98 / 111
Dictionaries Wildcard queries Edit distance Spelling correction Soundex
Context-sensitive spelling correction
Our example was: an asteroid that fell form the sky
How can we correct form here?
One idea: hit-based spelling correction
Retrieve “correct” terms close to each query term
for flew form munich: flea for flew, from for form, munch for
munich
Now try all possible resulting phrases as queries with one word
“fixed” at a time
Try query “flea form munich”
Try query “flew from munich”
Try query “flew form munch”
The correct query “flew from munich” has the most hits.
Suppose we have 7 alternatives for flew, 20 for form and 3 for
munich, how many “corrected” phrases will we enumerate?
Sojka, IIR Group: PV211: Dictionaries and tolerant retrieval 99 / 111
Dictionaries Wildcard queries Edit distance Spelling correction Soundex
Context-sensitive spelling correction
The “hit-based” algorithm we just outlined is not very
efficient.
More efficient alternative: look at “collection” of queries, not
documents.
Sojka, IIR Group: PV211: Dictionaries and tolerant retrieval 100 / 111
Dictionaries Wildcard queries Edit distance Spelling correction Soundex
General issues in spelling correction
User interface
automatic vs. suggested correction
Did you mean only works for one suggestion.
What about multiple possible corrections?
Tradeoff: simple vs. powerful UI
Cost
Spelling correction is potentially expensive.
Avoid running on every query?
Maybe just on queries that match few documents.
Guess: Spelling correction of major search engines is efficient
enough to be run on every query.
Sojka, IIR Group: PV211: Dictionaries and tolerant retrieval 101 / 111
Dictionaries Wildcard queries Edit distance Spelling correction Soundex
Exercise: Understand Peter Norvig’s spelling corrector
import re, collections
def words(text): return re.findall(’[a-z]+’, text.lower())
def train(features):
model = collections.defaultdict(lambda: 1)
for f in features:
model[f] += 1
return model
NWORDS = train(words(file(’big.txt’).read()))
alphabet = ’abcdefghijklmnopqrstuvwxyz’
def edits1(word):
splits = [(word[:i], word[i:]) for i in range(len(word) + 1)]
deletes = [a + b[1:] for a, b in splits if b]
transposes = [a + b[1] + b[0] + b[2:] for a, b in splits if len(b) gt 1]
replaces = [a + c + b[1:] for a, b in splits for c in alphabet if b]
inserts = [a + c + b for a, b in splits for c in alphabet]
return set(deletes + transposes + replaces + inserts)
def known_edits2(word):
return set(e2 for e1 in edits1(word) for e2 in edits1(e1) if e2 in NWORDS)
def known(words): return set(w for w in words if w in NWORDS)
def correct(word):
candidates = known([word]) or known(edits1(word)) or known_edits2(word) or [word]
return max(candidates, key=NWORDS.get)
Sojka, IIR Group: PV211: Dictionaries and tolerant retrieval 102 / 111
Dictionaries Wildcard queries Edit distance Spelling correction Soundex
https://norvig.com/spell-correct.html
Sojka, IIR Group: PV211: Dictionaries and tolerant retrieval 103 / 111
Dictionaries Wildcard queries Edit distance Spelling correction Soundex
Soundex
Soundex is the basis for finding phonetic (as opposed to
orthographic) alternatives.
Example: chebyshev / tchebyscheff
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
Sojka, IIR Group: PV211: Dictionaries and tolerant retrieval 105 / 111
Dictionaries Wildcard queries Edit distance Spelling correction Soundex
Soundex algorithm
1 Retain the first letter of the term.
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 to 1
C, G, J, K, Q, S, X, Z to 2
D,T to 3
L to 4
M, N to 5
R to 6
4 Repeatedly remove one out of each pair of consecutive identical digits
5 Remove all zeros from the resulting string; pad the resulting string
with trailing zeros and return the first four positions, which will
consist of a letter followed by three digits
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Dictionaries Wildcard queries Edit distance Spelling correction Soundex
Example: Soundex of HERMAN
Retain H
ERMAN → 0RM0N
0RM0N → 06505
06505 → 06505
06505 → 655
Return H655
Note: HERMANN will generate the same code
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Dictionaries Wildcard queries Edit distance Spelling correction Soundex
How useful is Soundex?
Not very – for information retrievali, obsolete
Ok for “high recall” tasks in other applications (e.g., Interpol).
Zobel and Dart (1996) suggest better alternatives for phonetic
matching in IR.
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Dictionaries Wildcard queries Edit distance Spelling correction Soundex
Exercise
Compute Soundex code of your last name
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Dictionaries Wildcard queries Edit distance Spelling correction Soundex
Take-away
Tolerant retrieval: What to do if there is no exact match
between query term and document term
Wildcard queries
Spelling correction
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Dictionaries Wildcard queries Edit distance Spelling correction Soundex
Resources
Chapter 3 of IIR
Resources at https://www.fi.muni.cz/~sojka/PV211/
and http://cislmu.org, materials in MU IS and FI MU
library
trie vs hash vs ternary tree
Soundex demo
Edit distance demo
Peter Norvig’s spelling corrector
Google: wild card search, spelling correction gone wrong, a
misspelling that is more frequent that the correct spelling
NLP IR boolean search system example: Sketch Engine –
https://ske.fi.muni.cz
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