ie with dictionaries cohen & sarawagi. announcements current statistics: –days with...
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IE with Dictionaries
Cohen & Sarawagi
Announcements
• Current statistics:– days with unscheduled student talks: 2– students with unscheduled student talks: 0– Projects are due: 4/28 (last day of class)– Additional requirement: draft (for comments)
no later than 4/21
Finding names you know about
• Problem: given dictionary of names, find them in email text– Important task beyond email (biology, link analysis,...)– Exact match is unlikely to work perfectly, due to
nicknames (Will Cohen), abbreviations (William C) , misspellings (Willaim Chen), polysemous words (June, Bill), etc
– In informal text it sometimes works very poorly– Problem is similar to record linkage (aka data
cleaning, de-duping, merge-purge, ...) problem of finding duplicate database records in heterogeneous databases.
Finding names you know about
• Technical problem:– Hard to combine state of the art similarity
metrics (as used in record linkage) with state of the art NER system due to representational mismatch:
• Opening up the box, modern NER systems don’t really know anything about names....
IE as Sequential Word Classification
Yesterday Pedro Domingos spoke this example sentence.
Person name: Pedro Domingos
A trained IE systemmodels the relative probability of labeled sequences of words.
To classify, find the most likely state sequence for the given words:
Any words said to be generated by the designated “person name”state extract as a person name:
person name
location name
background
IE as Sequential Word Classification
Modern IE systems use a rich representation for words, and clever probabilistic models of how labels interact in a sequence, but do not explicitly represent the names extracted.
wt -1
wt
Ot
wt+1
Ot +1
Ot -1
identity of wordends in “-ski”is capitalizedis part of a noun phraseis in a list of city namesis under node X in WordNetis in bold fontis indentedis in hyperlink anchorlast person name was femalenext two words are “and Associates”
…
…part of
noun phrase
is “Wisniewski”
ends in “-ski”
Semi-Markov models for IE
• Train on sequences of labeled segments, not labeled words.S=(start,end,label)
• Build probability model of segment sequences, not word sequences
• Define features f of segments
• (Approximately) optimize feature weights on training data
f(S) = words xt...xu, length, previous words, case information, ..., distance to known name
maximize:
m
iii
1
)|Pr(log xS
with Sunita Sarawagi, IIT Bombay
Details: Semi-Markov model
Segments vs tagging
1 2 3 4 5 6 7 8
Fred please stop by my office this afternoon
Person other other other Loc Loc other Time
t1=u1=1 t2=2, u2=4 t3=5,u3=6 t4=u4=7 t5=u5=8
Fred please stop by my office this afternoon
Person other Loc other Time
t
x
y
t,u
x
y
f(xt,yt)
f(xj,yj)
Details: Semi-Markov model
Conditional Semi-Markov models
CMM:
CSMM:
A training algorithm for CSMM’s (1)
Review: Collins’ perceptron training algorithm
Correct tags
Viterbi tags
A training algorithm for CSMM’s (2)
Variant of Collins’ perceptron training algorithm:
voted perceptron learner for TTRANS
like Viterbi
A training algorithm for CSMM’s (3)
Variant of Collins’ perceptron training algorithm:
voted perceptron learner for TTRANS
like Viterbi
A training algorithm for CSMM’s (3)
Variant of Collins’ perceptron training algorithm:
voted perceptron learner for TSEGTRANS
like Viterbi
Viterbi for HMMs
Viterbi for SMM
Sample CSMM features
Experimental results
• Baseline algorithms:– HMM-VP/1: tags are “in entity”, “other”– HMM-VP/4: tags are “begin entity”, “end entity”,
“continue entity”, “unique”, “other”– SMM-VP: all features f(w) have versions for “f(w) true for
some w in segment that is first (last, any) word of segment”– dictionaries: like Borthwick
• HMM-VP/1: fD(w)=“word w is in D”• HMM-VP/4: fD,begin(w)=“word w begins entity in D”,
etc, etc• Dictionary lookup
Datasets used
Used small training sets (10% of available) in experiments.
Results
Results: varying history
Results: changing the dictionary
Results: vs CRF
Results: vs CRF
