EFFICIENT ALGORITHMS FOR APPROXIMATE
MEMBER EXTRACTION
By Swapnil Kharche and Pavan Basheerabad
INTRODUCTION
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AME
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How to efficiently extract a substring from a text document that approximately
match some strings in the given dictionary.
Applications – named entity recognition, data cleaning
Two Steps
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Filtration – filter out strings from dictionary which are very different from substring
Verification – each candidate string is verified to decide whether the substring
should be extracted
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INTRODUCTION: AN EXAMPLE
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A Dictionary of strings we are
interested in
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E.g. Conference names, author
names etc.
We are going to locate their
“approximate appearances”
in a series of documents.
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PROBLEM DEFINITION
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Given a dictionary R of strings and a similarity threshold δ∈[0,1],
then a query M is submitted. Here, M represents a relatively
long string (e.g. a text file). The task of AME is to extract all M’s
substrings m, such that there exists some r ∈ R satisfying
Sim(m,r) ≥ δ.
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r is a piece of evidence for m
Sim() is a function measuring the similarity of two strings
An example of similarity measure
Jaccard Similarity: J (r , m) 
wt (r  m)
wt (r  m)
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APPROACH
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When the input is given, we need to decide whether a
substring m should be extracted
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Simple verification on all dictionary strings may be inefficient
Pre-pruning and post-verifying is beneficial
But should it be running-speed-oriented or filtering-power-oriented?
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Less time or less survivors?
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FILTRATION-VERIFICATION
Filtration
R
Input Query M
Potential Matches
Verification
True Matches
Wrong Matches
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FILTRATION-VERIFICATION(CONT’D)
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We need to balance between the two stages
More(less)
filtration time
Overall performance
Strong(weak)
Filtration power
=Tf+Tv
Fewer(more)
candidates
??
Less(more)
verification time
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TECHNIQUES
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If Sim(m,r) ≥ δ, what do we have ?
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wt(Sig(m)∩Sig(r)) ≥ τ(m)
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wt(Sig(m)∩Sig(r)) ≥ min{τ(m),τ(r)}
Existing techniques
Technique used
Where,
• Sig(m) is a prefix signature set of string m
• τ(m) is wt(Sig(m))-(1- δ)wt(m)
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So the threshold does not remain constant
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Use inverted lists to count sig-token overlapping
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Using IDF weights (Inverse Document Frequency)
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SIGNATURE-BASED INVERTED LISTS(SIL)
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Lists indexed by sig-tokens
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Each sig-token of a string creates a node (containing the string’s id) in the
corresponding list.
E.g. R = { r1 = “canon eos 5d digital camera”, r2 =“Nikon digital slr camera”, r3 =
“canon slr” }.
• wt(5d, eos, slr, Nikon, canon, camera, digital) = (9, 7, 2, 2, 2, 1 , 1)
•
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SIL (CONT’D)
rid
String
Signature Set
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“canon eos 5d
digital camera”
{“canon”,”eos”,
“5d”}
2
“Nikon digital slr
camera”
{“nikon”, “slr”,
“camera”}
3
“canon slr”
{“canon”, “slr”}
Signature sets of R’s strings
Signature
String rids
5d
(1)
“canon”
(1), (3)
“camera”
(2)
“eos”
(1)
“Nikon”
(2)
“slr”
(2), (3)
SIL
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EvSCAN ALGORITHM BY SIL
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Compute the overlapped sig weight using wt(Sig(m)∩Sig(r))
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The best matched string will be the one which satisfy the
condition wt(Sig(m)∩Sig(r)) ≥ min{τ(m),τ(r)}
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E.g. m=“canon eos digital camera”, δ=0.8
rid
wt(Sig(m)∩Sig(r))
min{τ(m),τ(r)}
1
9.0
6.8
2
1.0
3.8
3
2.0
3.2
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EvITER Algorithm – Progressive
Computation
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Recall we are checking all substrings
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Formally we proved that
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Some of them are quite similar, indicating that they share duplicate
computation
This means that, if m have potential evidence r, then m t is very likely to
match r
Let ES(m) be the set of “potential evidence” for m, list[t]={s| all dictionary
strings that contain token t}
We have ES(m  t)  ES(m)∪ list[t]
ES(m) = { r ∈ R | wt(m ∩ sig(r)) ≥ min{ δ * wt(m) ,τ(r)}}
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EXAMPLE
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Document M:
m
t
“…. cannon eos digital camera lens…”
ES(m)
List[t]
…
{r1}
lens, 3.0
22
53
…
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We know that only r1, r22, r53 are possible to match “cannon
eos digital camera lens”
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FLOW OF EVIDENCE
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EvITER for “Evidence ITERATION”
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THE STATIC THRESHOLD PROBLEM
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How does this index work so far?
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-“Get ready for δ=0.8 please.”
-“Please wait 30min for index generation…”
-“Ready!”
-“Document M1, δ=0.8. Go!”
-“…Extraction complete.”
-“Document M2, and I want δ=0.9…”
-“Sorry, please wait another 30min for index regeneration…”
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THE STATIC THRESHOLD PROBLEM
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This One Seems Better
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-“Get ready for δ>=0.8 please.”
-“Please wait 30min for index generation…”
-“Ready!”
-“Document M1, δ=0.8. Go!”
-“…Extraction complete.”
-“Document M2, and I want δ=0.9…”
-“…Extraction complete.”
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EXPERIMENTAL DATASETS
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Paper titles from the DBLP website
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Author names from DBLP website
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RESULTS
Fig. Performance under different k (δ = 0.85)
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PERFORMANCE
Fig. Performance under different thresholds (k = 3)
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CONCLUSION
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This method causes no false negatives
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It achieves a good balance between the two phases of
filtration and verification.
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They proposed EvITER to eliminate duplicate computation
It achieves both effective & efficient performance
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THANK YOU!
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