6•863J Natural Language Processing Lecture 8: Not an Earley finish
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Transcript of 6•863J Natural Language Processing Lecture 8: Not an Earley finish
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6•863J Natural Language ProcessingLecture 8: Not an Earley finish
Instructor: Robert C• Berwickberwick@csail•mit•edu
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6•863J/9•611J SP05
The Menu Bar• Administrivia:Agenda:
Earley’s algorithmTime complexityParsing strategies: Earley algorithmWhat do people do?
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6•863J/9•611J SP05
Example GrammarS NP VP NP NameNP Det N N pjsNP NP PP N elephantVP V NP V shotVP VP PP P inPP P NP Det an
Det myName I
I shot an elephant in my pjs Name V Det N P Det N
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Marxist analysis ---- Rules -----Start -> SS -> NP VPNP -> Det NNP -> NP PPNP -> NamePP -> P NPVP -> VP PPVP -> V NPVP -> V---- Lexicon -----an Detmy Detshot Vshot Nmy Detelephant Npajamas NI Namein P
I shot an elephant in my pajamas
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6•863J/9•611J SP05
State set simulation of nondeterministic machine
• State Set S0 = set of all states (‘edges’) we can be in after reading 0 words/pos
• March along constructing each state set Si+1 from previous state set state set Si
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State-set constructionInitialize: S0initial state set= initial state edge [Start Sn] -closure of this set under predict, complete Loop: For word i=1,…,n Si+1 computed from Si (using scan, predict, complete)
scan; then predict, complete
Final: Is a final edge in Sn? [Start Sn]Sn ?
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6•863J/9•611J SP05
The basic state representation: an item triple
[NP Det • N, 0, 2]
Dotted ruleStart position(left edge)
Progress position(right edge)
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6•863J/9•611J SP05
Another way to view it
[NP Det N• 0 2]
N
guyScan
Det
theScan
[NP • Det N 0 0] [NP Det • N 0 1] [NP Det N • 0 2]
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6•863J/9•611J SP05
Earley Parser
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The chart represents ambiguity by multiple back links in matrix
1 2 3 4 5 6 70 NP S S S1 v, n VP VP2 d NP PP3 n4 p PP5 d NP6 n
Startposof edge
End position of edge
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[S •NP VP, 0,0]
Example: Top-down init w/ chart
0 I 1 2 shot 3 an 4 elephant 5 in 6 my 7 pjs
We are constructing State set S0 -
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6•863J/9•611J SP05
[NP • Name, 0, 0]
In picture form
State set S0 now done
[S •NP VP, 0, 0][NP • D N, 0, 0] (from td rule, or predict)
[NP • NP PP, 0, 0]+ all POS expansions
0 I 1 2 shot 3 an 4 elephant 5 in 6 my 7 pjs
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Construct S1 from S0: Scan to next word…follow the bouncing dot…
I shot an elephant in my pjs
[S •NP VP, 0, 0][NP • Det N, 0, 0][NP • Name, 0, 0][NP • NP PP, 0, 0]
[NP Name •, 0, 1]
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The Fundamental Rule(“complete”) Applies…
• As time goes by…• Actually, as NP goes by…• We can also extend the length of all the
other edges that had an NP with a dot before them…
• That is,
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In picture form
State set S1 now done
[S NP • VP, 0, 1][NP NP • PP, 0, 1]
0 I 1 2 shot 3 an 4 elephant 5 in 6 my 7 pjs
[NP Name •, 0, 1]
[VP • V NP, 1, 1][VP • VP PP, 1, 1][PP • P NP, 1, 1]
completecompletepredictpredictpredict
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6•863J/9•611J SP05
Scan Verb - extend edge
I shot an elephant in my pjsNP N •
S NP • VP
[VP V • NP, 1,2]
VP • VP PP
What next? … Predict NP, add all edgescorresponding to expansion of NP
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Picture: Complete combines edges (The “fundamental rule”)
NP D N •
NP • NP PP
I shot an elephant in my pjsNP N •
S NP • VP
VP V NP •
VP VP • PP
S NP VP •
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State set construction – cols in chart
State Set 0
0 Start • S
0 S • NP VP
0 NP • Name
0 NP • NP PP
0 NP • Det N
State Set i
1 V •
1 VP V • NP
2 NP • Det N
2 NP • NP PP
2 NP • Name
State Set n
6 N •
5 NP Det N •
4 PP P NP •
5 NP NP • PP
2 NP NP PP •
1 VP VP PP •
7 PP • P NP
1 VP V NP •
2 NP NP • PP
0 S NP VP •
1 VP VP • PP
7 P •
0 Start S •
… …
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6•863J/9•611J SP05
How does each of the 3 ops change Si?
• Scan: (jump over a token)• Before: [A tk, i-1] in State Set Si-1 & word i= t• Result: Add [A t k, i] to State Set Si
• (Do this for all items [triples] in State Set Si-1 )
• Predict (Push): (encounter nonterminal)• Before: [A Bk, i-1] , B= a nonterminal, in Si then• After: Add all new triples of form [B , i, i] to State
Set Si• Complete(Pop): (finish w/ nonterminal)
• Before: If Si contains triple in form [B k, i] then • After: go to state set Sk and for all rules of form [A
Bk, i-1], add triples [A B k, i] to state set Si
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6•863J/9•611J SP05
The main dealinput x = x1 …… xn
• S0 = {[Start •S, 0, 0]}• For 0 i n do:
Process each item s Si in order by applying to it the single applicable operation among:(a) Predictor (adds new items to Si )(b) Completer (adds new items to Si )(c) Scanner (adds new items to Si+1 )
• If Si+1 = , reject the input• If i= n and S n = {[Start S • , 0, n],…} then Accept
then input; else reject
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6•863J/9•611J SP05
Earley’s Algorithm: Predictor
• Predictor(AB, [i,j])
A B
A
B
i j B
B
Bi
For each rule: Add:
ABE
BCD
Input Rule
Example
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Predictor (wishor)
• Predict (Push): • Before: [A B k, i] , B=nonterminal, in
Si then• After: Add all new edges of form [B ,
i+1, i+1] to State Set Si+1
• Cries out its need for a phrase of type B
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Earley’s Algorithm: Scanner
• Scanner(AB, [i,j])
A B
A
B
i j
AB
A
B
i j+1
w
Bw
For each rule: Add edge:
Aw Aw
Input Rule
Example
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Scan – formally (“Find a word/POS”)
• Scan: (jump over a token)• Before: [A t k, i] in State Set Si & word
i= t• Result: Add [A t k, i+1] to State Set Si+1
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6•863J/9•611J SP05
Earley’s Algorithm: Completer
• Completer(B, [i,j])
AB
A
k j
For each edgeB
B
i j A B
A
B
k i
B
Add:
DEA DEA
ABC
Input Rule
Example
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More precisely
• Complete(Pop): (finish w/ phrase)• Before: If Si contains e in form [B k, i] then
go to back to state set Sk and for all rules of form [A B j, k], add edges E’ [A B j, i] to state set Si
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“The fundamental rule”: glues smaller trees into larger ones
VP V NP•
NP d n • VP V • NP =
shot start pos= 1, len 1
an elephantstart= 2, len=2
1 2 2 4
start pos= 1, len 3 1 4
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6•863J/9•611J SP05
Earley’s Algorithm: Rules
Aw Aw
Scanner
ABE
BCD
PredictorDEA DEA
ABC
Completer
SAB
Initialization
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6•863J/9•611J SP05
State set construction – cols in chart
State Set 0
0 Start • S
0 S • NP VP
0 NP • Name
0 NP • NP PP
0 NP • Det N
State Set i
1 V •
1 VP V • NP
2 NP • Det N
2 NP • NP PP
2 NP • Name
State Set n
6 N •
5 NP Det N •
4 PP P NP •
5 NP NP • PP
2 NP NP PP •
1 VP VP PP •
7 PP • P NP
1 VP V NP •
2 NP NP • PP
0 S NP VP •
1 VP VP • PP
7 P •
0 Start S •
… …
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6•863J/9•611J SP05
Take note of the start-stop indices
NP
Initially: S [i, k]
i k
the dogk k+2k+1
jPredictStart: k
S
Predict: NP [k, k]Scan: NP [k, k+1]Scan: NP [k, k+2]
Complete: NP [k, k+2]
After NP: S [i, k+2]
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6•863J/9•611J SP05
Indices: the left-hand edge ‘you are here’
• Predict: does not increment - NP[k,k]
• Scan: does increment, by 1, the left-hand edge: NP[k,k] NP[k,k+1] NP[k,k+2]
• Complete: increments left-hand edge of item in a previous State Set: S[j,k] S[j, k+2]
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6•863J/9•611J SP05
Example GrammarS NP VP NP NameNP Det N N pjsNP NP PP N elephantVP V NP V shotVP VP PP P inPP P NP Det an
Det myName I
I shot an elephant in my pjs Name V Det N P Det N
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00 Start • S
Initialize
Remember this stands for (0, Start • S)
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00 Start • S
0 S • NP VPpredict the kind of S we are looking for
Remember this stands for (0, S • NP VP)
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00 START • S
0 S • NP VP
0 NP • Det N
0 NP • NP PP
0 NP • I
predict the kind of NP we are looking for(actually we’ll look for 3 kinds: any of the 3 will do)
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00 START • S
0 S • NP VP
0 NP • Det N
0 NP • NP PP
0 NP • I
0 Det • my
0 Det • an
predict the kind of Det we are looking for (2 kinds)
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00 START • S
0 S • NP VP
0 NP • Det N
0 NP • NP PP
0 NP • I
0 Det • my
0 Det • an
predict the kind of NP we’re looking for but we were already looking for these sodon’t add duplicates! Note that this happenedwhen we were processing a left-recursive rule•
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0 I 1 0 START • S 0 NP I •
0 S • NP VP
0 NP • Det N
0 NP • NP PP
0 NP • I
0 Det • my
0 Det • an
scan: the desired word is in the input!
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0 I 1 0 START • S 0 NP I •
0 S • NP VP
0 NP • Det N
0 NP • NP PP
0 NP • I
0 Det • my
0 Det • an
scan: failure
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0 I 1 0 START • S 0 NP I •
0 S • NP VP
0 NP • Det N
0 NP • NP PP
0 NP • I
0 Det • my
0 Det • an scan: failure
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0 I 1 0 START • S 0 NP I •
0 S • NP VP 0 S NP • VP
0 NP • Det N 0 NP NP • PP
0 NP • NP PP
0 NP • I
0 Det • my
0 Det • an
attach the newly created NP(which starts at 0) to its customers (incomplete constituents that end at 0and have NP after the dot)
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0 I 1 0 START • S 0 NP I •
0 S • NP VP 0 S NP • VP
0 NP • Det N 0 NP NP • PP
0 NP • NP PP 1 VP • V NP
0 NP • I 1 VP • VP PP
0 Det • my
0 Det • an
predict
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0 I 1 0 START • S 0 NP I •
0 S • NP VP 0 S NP • VP
0 NP • Det N 0 NP NP • PP
0 NP • NP PP 1 VP • V NP
0 NP • I 1 VP • VP PP
0 Det • my 1 PP • P NP
0 Det • an
predict
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0 I 1 0 START • S 0 NP I •
0 S • NP VP 0 S NP • VP
0 NP • Det N 0 NP NP • PP
0 NP • NP PP 1 VP • V NP
0 NP • I 1 VP • VP PP
0 Det • my 1 PP • P NP
0 Det • an 1 V • shot
predict
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0 I 1 0 START • S 0 NP I •
0 S • NP VP 0 S NP • VP
0 NP • Det N 0 NP NP • PP
0 NP • NP PP 1 VP • V NP
0 NP • I 1 VP • VP PP
0 Det • my 1 PP • P NP
0 Det • an 1 V • shot
predict
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0 I 1 0 START • S 0 NP I •
0 S • NP VP 0 S NP • VP
0 NP • Det N 0 NP NP • PP
0 NP • NP PP 1 VP • V NP
0 NP • I 1 VP • VP PP
0 Det • my 1 PP • P NP
0 Det • an 1 V • shot
1 P • in
predict
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0 I 1 shot 20 START • S 0 NP I • 1 V shot •
0 S • NP VP 0 S NP • VP
0 NP • Det N 0 NP NP • PP
0 NP • NP PP 1 VP • V NP
0 NP • I 1 VP • VP PP
0 Det • my 1 PP • P NP
0 Det • an 1 V • shot
1 P • in
scan: success!
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0 I 1 shot 20 START • S 0 NP I • 1 V shot •
0 S • NP VP 0 S NP • VP
0 NP • Det N 0 NP NP • PP
0 NP • NP PP 1 VP • V NP
0 NP • I 1 VP • VP PP
0 Det • my 1 PP • P NP
0 Det • an 1 V • shot
1 P • in scan: failure
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0 I 1 shot 20 START • S 0 NP I • 1 V shot •
0 S • NP VP 0 S NP • VP 1 VP V • NP
0 NP • Det N 0 NP NP • PP
0 NP • NP PP 1 VP • V NP
0 NP • I 1 VP • VP PP
0 Det • my 1 PP • P NP
0 Det • an 1 V • shot
1 P • with
complete
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0 I 1 shot 20 START • S 0 NP I • 1 V shot •
0 S • NP VP 0 S NP • VP 1 VP V • NP
0 NP • Det N 0 NP NP • PP 2 NP • Det N
0 NP • NP PP 1 VP • V NP 2 NP • NP PP
0 NP • I 1 VP • VP PP 2 NP • I
0 Det • my 1 PP • P NP
0 Det • an 1 V • shot
1 P • with
predict
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0 I 1 shot 20 START • S 0 NP I • 1 V shot •
0 S • NP VP 0 S NP • VP 1 VP V • NP
0 NP • Det N 0 NP NP • PP 2 NP • Det N
0 NP • NP PP 1 VP • V NP 2 NP • NP PP
0 NP • I 1 VP • VP PP 2 NP • I
0 Det • my 1 PP • P NP 2 Det • my
0 Det • an 1 V • shot 2 Det • an
1 P • with
predict (these next few stepsshould look familiar)
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0 I 1 shot 20 START • S 0 NP I • 1 V shot •
0 S • NP VP 0 S NP • VP 1 VP V • NP
0 NP • Det N 0 NP NP • PP 2 NP • Det N
0 NP • NP PP 1 VP • V NP 2 NP • NP PP
0 NP • I 1 VP • VP PP 2 NP • I
0 Det • my 1 PP • P NP 2 Det • my
0 Det • an 1 V • shot 2 Det • an
1 P • with
predict
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0 I 1 shot 20 START • S 0 NP I • 1 V shot •
0 S • NP VP 0 S NP • VP 1 VP V • NP
0 NP • Det N 0 NP NP • PP 2 NP • Det N
0 NP • NP PP 1 VP • V NP 2 NP • NP PP
0 NP • I 1 VP • VP PP 2 NP • I
0 Det • my 1 PP • P NP 2 Det • my
0 Det • an 1 V • shot 2 Det • an
1 P • with
scan (this time we fail sinceI is not the next word)
![Page 54: 6•863J Natural Language Processing Lecture 8: Not an Earley finish](https://reader035.fdocuments.in/reader035/viewer/2022062521/56813ab4550346895da2b6b6/html5/thumbnails/54.jpg)
0 I 1 shot 2 an 30 START • S 0 NP I • 1 V shot • 2 Det an •
0 S • NP VP 0 S NP • VP 1 VP V • NP
0 NP • Det N 0 NP NP • PP 2 NP • Det N
0 NP • NP PP 1 VP • V NP 2 NP • NP PP
0 NP • I 1 VP • VP PP 2 NP • I
0 Det • my 1 PP • P NP 2 Det • my
0 Det • an 1 V • shot 2 Det • an
1 P • with
scan: success!
![Page 55: 6•863J Natural Language Processing Lecture 8: Not an Earley finish](https://reader035.fdocuments.in/reader035/viewer/2022062521/56813ab4550346895da2b6b6/html5/thumbnails/55.jpg)
0 I 1 shot 2 an 30 START • S 0 NP I • 1 V shot • 2 Det an •
0 S • NP VP 0 S NP • VP 1 VP V • NP
0 NP • Det N 0 NP NP • PP 2 NP • Det N
0 NP • NP PP 1 VP • V NP 2 NP • NP PP
0 NP • I 1 VP • VP PP 2 NP • I
0 Det • my 1 PP • P NP 2 Det • my
0 Det • an 1 V • shot 2 Det • an
1 P • in
![Page 56: 6•863J Natural Language Processing Lecture 8: Not an Earley finish](https://reader035.fdocuments.in/reader035/viewer/2022062521/56813ab4550346895da2b6b6/html5/thumbnails/56.jpg)
0 I 1 shot 2 an 30 START • S 0 NP I • 1 V shot • 2 Det an •
0 S • NP VP 0 S NP • VP 1 VP V • NP 2 NP Det • N
0 NP • Det N 0 NP NP • PP 2 NP • Det N
0 NP • NP PP 1 VP • V NP 2 NP • NP PP
0 NP • I 1 VP • VP PP 2 NP • I
0 Det • my 1 PP • P NP 2 Det • my
0 Det • an 1 V • shot 2 Det • an
1 P • in
![Page 57: 6•863J Natural Language Processing Lecture 8: Not an Earley finish](https://reader035.fdocuments.in/reader035/viewer/2022062521/56813ab4550346895da2b6b6/html5/thumbnails/57.jpg)
0 I 1 shot 2 an 3 0 START • S 0 NP I • 1 V shot • 2 Det an •
0 S • NP VP 0 S NP • VP 1 VP V • NP 2 NP Det • N
0 NP • Det N 0 NP NP • PP 2 NP • Det N 3 N • eleph
0 NP • NP PP 1 VP • V NP 2 NP • NP PP 3 N • pjs
0 NP • I 1 VP • VP PP 2 NP • I
0 Det • my 1 PP • P NP 2 Det • my
0 Det • an 1 V • shot 2 Det • an
1 P • in
![Page 58: 6•863J Natural Language Processing Lecture 8: Not an Earley finish](https://reader035.fdocuments.in/reader035/viewer/2022062521/56813ab4550346895da2b6b6/html5/thumbnails/58.jpg)
0 I 1 shot 2 an 3 eleph 40 START • S 0 NP I • 1 V shot • 2 Det an • 3 N eleph •
0 S • NP VP 0 S NP • VP 1 VP V • NP 2 NP Det • N
0 NP • Det N 0 NP NP • PP 2 NP • Det N 3 N • eleph
0 NP • NP PP 1 VP • V NP 2 NP • NP PP 3 N • pjs
0 NP • I 1 VP • VP PP 2 NP • I
0 Det • my 1 PP • P NP 2 Det • my
0 Det • a 1 V • shot 2 Det • a
1 P • in
![Page 59: 6•863J Natural Language Processing Lecture 8: Not an Earley finish](https://reader035.fdocuments.in/reader035/viewer/2022062521/56813ab4550346895da2b6b6/html5/thumbnails/59.jpg)
0 I 1 shot 2 an 3 eleph 40 START • S 0 NP I • 1 V shot • 2 Det an • 3 N eleph •
0 S • NP VP 0 S NP • VP 1 VP V • NP 2 NP Det • N
0 NP • Det N 0 NP NP • PP 2 NP • Det N 3 N • eleph
0 NP • NP PP 1 VP • V NP 2 NP • NP PP 3 N • pjs
0 NP • I 1 VP • VP PP 2 NP • I
0 Det • my 1 PP • P NP 2 Det • my
0 Det • a 1 V • shot 2 Det • a
1 P • in
![Page 60: 6•863J Natural Language Processing Lecture 8: Not an Earley finish](https://reader035.fdocuments.in/reader035/viewer/2022062521/56813ab4550346895da2b6b6/html5/thumbnails/60.jpg)
0 I 1 shot 2 an 3 eleph 40 START • S 0 NP I • 1 V shot • 2 Det an • 3 N eleph •
0 S • NP VP 0 S NP • VP 1 VP V • NP 2 NP Det • N 2 NP Det N •
0 NP • Det N 0 NP NP • PP 2 NP • Det N 3 N • eleph
0 NP • NP PP 1 VP • V NP 2 NP • NP PP 3 N • pjs
0 NP • I 1 VP • VP PP 2 NP • I
0 Det • my 1 PP • P NP 2 Det • my
0 Det • an 1 V • shot 2 Det • an
1 P • in
complete
![Page 61: 6•863J Natural Language Processing Lecture 8: Not an Earley finish](https://reader035.fdocuments.in/reader035/viewer/2022062521/56813ab4550346895da2b6b6/html5/thumbnails/61.jpg)
0 I 1 shot 2 an 3 eleph 40 START • S 0 NP I • 1 V shot • 2 Det an • 3 N eleph •
0 S • NP VP 0 S NP • VP 1 VP V • NP 2 NP Det • N 2 NP Det N •
0 NP • Det N 0 NP NP • PP 2 NP • Det N 3 N • eleph 1 VP V NP •
0 NP • NP PP 1 VP • V NP 2 NP • NP PP 3 N • pjs 2 NP NP • PP
0 NP • I 1 VP • VP PP 2 NP • I
0 Det • my 1 PP • P NP 2 Det • my
0 Det • an 1 V • shot 2 Det • an
1 P • in
complete(again!)
![Page 62: 6•863J Natural Language Processing Lecture 8: Not an Earley finish](https://reader035.fdocuments.in/reader035/viewer/2022062521/56813ab4550346895da2b6b6/html5/thumbnails/62.jpg)
0 I 1 shot 2 an 3 eleph 40 START • S 0 NP I • 1 V shot • 2 Det an • 3 N eleph •
0 S • NP VP 0 S NP • VP 1 VP V • NP 2 NP Det • N 2 NP Det N •
0 NP • Det N 0 NP NP • PP 2 NP • Det N 3 N • eleph 1 VP V NP •
0 NP • NP PP 1 VP • V NP 2 NP • NP PP 3 N • pjs 2 NP NP • PP
0 NP • I 1 VP • VP PP 2 NP • I 0 S NP VP •
0 Det • my 1 PP • P NP 2 Det • my 1 VP VP • PP
0 Det • an 1 V • shot 2 Det • an
1 P • in
complete(again!)
![Page 63: 6•863J Natural Language Processing Lecture 8: Not an Earley finish](https://reader035.fdocuments.in/reader035/viewer/2022062521/56813ab4550346895da2b6b6/html5/thumbnails/63.jpg)
0 I 1 shot 2 an 3 eleph 40 START • S 0 NP I • 1 V shot • 2 Det an • 3 N eleph •
0 S • NP VP 0 S NP • VP 1 VP V • NP 2 NP Det • N 2 NP Det N •
0 NP • Det N 0 NP NP • PP 2 NP • Det N 3 N • eleph 1 VP V NP •
0 NP • NP PP 1 VP • V NP 2 NP • NP PP 3 N • pjs 2 NP NP • PP
0 NP • I 1 VP • VP PP 2 NP • I 0 S NP VP •
0 Det • my 1 PP • P NP 2 Det • my 1 VP VP • PP
0 Det • an 1 V • shot 2 Det • an 4 PP • P NP
1 P • in
![Page 64: 6•863J Natural Language Processing Lecture 8: Not an Earley finish](https://reader035.fdocuments.in/reader035/viewer/2022062521/56813ab4550346895da2b6b6/html5/thumbnails/64.jpg)
0 I 1 shot 2 an 3 eleph 40 START • S 0 NP I • 1 V shot • 2 Det an • 3 N eleph •
0 S • NP VP 0 S NP • VP 1 VP V • NP 2 NP Det • N 2 NP Det N •
0 NP • Det N 0 NP NP • PP 2 NP • Det N 3 N • eleph 1 VP V NP •
0 NP • NP PP 1 VP • V NP 2 NP • NP PP 3 N • pjs 2 NP NP • PP
0 NP • I 1 VP • VP PP 2 NP • I 0 S NP VP •
0 Det • my 1 PP • P NP 2 Det • my 1 VP VP • PP
0 Det • an 1 V • shot 2 Det • an 4 PP • P NP
1 P • in 0 START S •
complete(again!)
![Page 65: 6•863J Natural Language Processing Lecture 8: Not an Earley finish](https://reader035.fdocuments.in/reader035/viewer/2022062521/56813ab4550346895da2b6b6/html5/thumbnails/65.jpg)
0 I 1 shot 2 an 3 eleph 40 START • S 0 NP I • 1 V shot • 2 Det an • 3 N eleph •
0 S • NP VP 0 S NP • VP 1 VP V • NP 2 NP Det • N 2 NP Det N •
0 NP • Det N 0 NP NP • PP 2 NP • Det N 3 N • eleph 1 VP V NP •
0 NP • NP PP 1 VP • V NP 2 NP • NP PP 3 N • pjs 2 NP NP • PP
0 NP • I 1 VP • VP PP 2 NP • I 0 S NP VP •
0 Det • my 1 PP • P NP 2 Det • my 1 VP VP • PP
0 Det • an 1 V • shot 2 Det • an 4 PP • P NP
1 P • in 0 START S •
![Page 66: 6•863J Natural Language Processing Lecture 8: Not an Earley finish](https://reader035.fdocuments.in/reader035/viewer/2022062521/56813ab4550346895da2b6b6/html5/thumbnails/66.jpg)
0 I 1 shot 2 an 3 eleph 40 START • S 0 NP I • 1 V shot • 2 Det an • 3 N eleph •
0 S • NP VP 0 S NP • VP 1 VP V • NP 2 NP Det • N 2 NP Det N •
0 NP • Det N 0 NP NP • PP 2 NP • Det N 3 N • eleph 1 VP V NP •
0 NP • NP PP 1 VP • V NP 2 NP • NP PP 3 N • pjs 2 NP NP • PP
0 NP • I 1 VP • VP PP 2 NP • I 0 S NP VP •
0 Det • my 1 PP • P NP 2 Det • my 1 VP VP • PP
0 Det • an 1 V • shot 2 Det • an 4 PP • P NP
1 P • in 0 START S •
4 P • in
![Page 67: 6•863J Natural Language Processing Lecture 8: Not an Earley finish](https://reader035.fdocuments.in/reader035/viewer/2022062521/56813ab4550346895da2b6b6/html5/thumbnails/67.jpg)
0 I 1 shot 2 an 3 eleph 40 START • S 0 NP I • 1 V shot • 2 Det an • 3 N eleph •
0 S • NP VP 0 S NP • VP 1 VP V • NP 2 NP Det • N 2 NP Det N •
0 NP • Det N 0 NP NP • PP 2 NP • Det N 3 N • eleph 1 VP V NP •
0 NP • NP PP 1 VP • V NP 2 NP • NP PP 3 N • pjs 2 NP NP • PP
0 NP • I 1 VP • VP PP 2 NP • I 0 S NP VP •
0 Det • my 1 PP • P NP 2 Det • my 1 VP VP • PP
0 Det • an 1 V • shot 2 Det • an 4 PP • P NP
1 P • in 0 START S •
4 P • in
![Page 68: 6•863J Natural Language Processing Lecture 8: Not an Earley finish](https://reader035.fdocuments.in/reader035/viewer/2022062521/56813ab4550346895da2b6b6/html5/thumbnails/68.jpg)
0 I 1 shot 2 an 3 eleph 4 in 50 START • S 0 NP I • 1 V shot • 2 Det an • 3 N eleph • 4 P in •
0 S • NP VP 0 S NP • VP 1 VP V • NP 2 NP Det • N 2 NP Det N •
0 NP • Det N 0 NP NP • PP 2 NP • Det N 3 N • eleph 1 VP V NP •
0 NP • NP PP 1 VP • V NP 2 NP • NP PP 3 N • pjs 2 NP NP • PP
0 NP • I 1 VP • VP PP 2 NP • I 0 S NP VP •
0 Det • my 1 PP • P NP 2 Det • my 1 VP VP • PP
0 Det • an 1 V • shot 2 Det • an 4 PP • P NP
1 P • in 0 START S •
4 P • in
![Page 69: 6•863J Natural Language Processing Lecture 8: Not an Earley finish](https://reader035.fdocuments.in/reader035/viewer/2022062521/56813ab4550346895da2b6b6/html5/thumbnails/69.jpg)
0 I 1 shot 2 an 3 eleph 4 in 50 START • S 0 NP I • 1 V shot • 2 Det an • 3 N eleph • 4 P in •
0 S • NP VP 0 S NP • VP 1 VP V • NP 2 NP Det • N 2 NP Det N • 4 PP P • NP
0 NP • Det N 0 NP NP • PP 2 NP • Det N 3 N • eleph 1 VP V NP •
0 NP • NP PP 1 VP • V NP 2 NP • NP PP 3 N • pjs 2 NP NP • PP
0 NP • I 1 VP • VP PP 2 NP • I 0 S NP VP •
0 Det • my 1 PP • P NP 2 Det • my 1 VP VP • PP
0 Det • an 1 V • shot 2 Det • an 4 PP • P NP
1 P • in 0 START S •
4 P • in
![Page 70: 6•863J Natural Language Processing Lecture 8: Not an Earley finish](https://reader035.fdocuments.in/reader035/viewer/2022062521/56813ab4550346895da2b6b6/html5/thumbnails/70.jpg)
0 I 1 shot 2 an 3 eleph 4 in 50 START • S 0 NP I • 1 V shot • 2 Det an • 3 N eleph • 4 P in •
0 S • NP VP 0 S NP • VP 1 VP V • NP 2 NP Det • N 2 NP Det N • 4 PP P • NP
0 NP • Det N 0 NP NP • PP 2 NP • Det N 3 N • eleph 1 VP V NP • 5 NP • Det N
0 NP • NP PP 1 VP • V NP 2 NP • NP PP 3 N • pjs 2 NP NP • PP 5 NP • NP PP
0 NP • I 1 VP • VP PP 2 NP • I 0 S NP VP • 5 NP • I
0 Det • my 1 PP • P NP 2 Det • my 1 VP VP • PP
0 Det • an 1 V • shot 2 Det • an 4 PP • P NP
1 P • in 0 START S •
4 P • in
![Page 71: 6•863J Natural Language Processing Lecture 8: Not an Earley finish](https://reader035.fdocuments.in/reader035/viewer/2022062521/56813ab4550346895da2b6b6/html5/thumbnails/71.jpg)
0 I 1 shot 2 an 3 eleph 4 in 50 START • S 0 NP I • 1 V shot • 2 Det an • 3 N eleph • 4 P in •
0 S • NP VP 0 S NP • VP 1 VP V • NP 2 NP Det • N 2 NP Det N • 4 PP P • NP
0 NP • Det N 0 NP NP • PP 2 NP • Det N 3 N • eleph 1 VP V NP • 5 NP • Det N
0 NP • NP PP 1 VP • V NP 2 NP • NP PP 3 N • pjs 2 NP NP • PP 5 NP • NP PP
0 NP • I 1 VP • VP PP 2 NP • I 0 S NP VP • 5 NP • I
0 Det • my 1 PP • P NP 2 Det • my 1 VP VP • PP 5 Det • my
0 Det • an 1 V • shot 2 Det • an 4 PP • P NP 5 Det • an
1 P • in 0 START S •
4 P • in
![Page 72: 6•863J Natural Language Processing Lecture 8: Not an Earley finish](https://reader035.fdocuments.in/reader035/viewer/2022062521/56813ab4550346895da2b6b6/html5/thumbnails/72.jpg)
0 I 1 shot 2 an 3 eleph 4 in 50 START • S 0 NP I • 1 V shot • 2 Det an• 3 N eleph • 4 P in •
0 S • NP VP 0 S NP • VP 1 VP V • NP 2 NP Det • N 2 NP Det N • 4 PP P • NP
0 NP • Det N 0 NP NP • PP 2 NP • Det N 3 N • eleph 1 VP V NP • 5 NP • Det N
0 NP • NP PP 1 VP • V NP 2 NP • NP PP 3 N • pjs 2 NP NP • PP 5 NP • NP PP
0 NP • I 1 VP • VP PP 2 NP • I 0 S NP VP • 5 NP • I
0 Det • my 1 PP • P NP 2 Det • my 1 VP VP • PP 5 Det • my
0 Det • an 1 V • shot 2 Det • an 4 PP • P NP 5 Det • an
1 P • in 0 START S •
4 P • in
![Page 73: 6•863J Natural Language Processing Lecture 8: Not an Earley finish](https://reader035.fdocuments.in/reader035/viewer/2022062521/56813ab4550346895da2b6b6/html5/thumbnails/73.jpg)
0 I 1 shot 2 an 3 eleph 4 in 5 0 START • S 0 NP I • 1 V shot • 2 Det an • 3 N eleph • 4 P in •
0 S • NP VP 0 S NP • VP 1 VP V • NP 2 NP Det • N 2 NP Det N • 4 PP P • NP
0 NP • Det N 0 NP NP • PP 2 NP • Det N 3 N • eleph 1 VP V NP • 5 NP • Det N
0 NP • NP PP 1 VP • V NP 2 NP • NP PP 3 N • pjs 2 NP NP • PP 5 NP • NP PP
0 NP • I 1 VP • VP PP 2 NP • I 0 S NP VP • 5 NP • I
0 Det • my 1 PP • P NP 2 Det • my 1 VP VP • PP 5 Det • my
0 Det • an 1 V • shot 2 Det • an 4 PP • P NP 5 Det • an
1 P • in 0 START S •
4 P • in
![Page 74: 6•863J Natural Language Processing Lecture 8: Not an Earley finish](https://reader035.fdocuments.in/reader035/viewer/2022062521/56813ab4550346895da2b6b6/html5/thumbnails/74.jpg)
0 I 1 shot 2 an 3 eleph 4 in 5 0 START • S 0 NP I • 1 V shot • 2 Det an • 3 N eleph • 4 P in •
0 S • NP VP 0 S NP • VP 1 VP V • NP 2 NP Det • N 2 NP Det N • 4 PP P • NP
0 NP • Det N 0 NP NP • PP 2 NP • Det N 3 N • eleph 1 VP V NP • 5 NP • Det N
0 NP • NP PP 1 VP • V NP 2 NP • NP PP 3 N • pjs 2 NP NP • PP 5 NP • NP PP
0 NP • I 1 VP • VP PP 2 NP • I 0 S NP VP • 5 NP • I
0 Det • my 1 PP • P NP 2 Det • my 1 VP VP • PP 5 Det • my
0 Det • an 1 V • shot 2 Det • an 4 PP • P NP 5 Det • an
1 P • in 0 START S •
4 P • in
![Page 75: 6•863J Natural Language Processing Lecture 8: Not an Earley finish](https://reader035.fdocuments.in/reader035/viewer/2022062521/56813ab4550346895da2b6b6/html5/thumbnails/75.jpg)
0 I 1 shot 2 an 3 eleph 4 in 5 my 60 START • S 0 NP I • 1 V shot • 2 Det an • 3 N eleph • 4 P in • 5 Det my •
0 S • NP VP 0 S NP • VP 1 VP V • NP 2 NP Det • N 2 NP Det N • 4 PP P • NP
0 NP • Det N 0 NP NP • PP 2 NP • Det N 3 N • eleph 1 VP V NP • 5 NP • Det N
0 NP • NP PP 1 VP • V NP 2 NP • NP PP 3 N • pjs 2 NP NP • PP 5 NP • NP PP
0 NP • I 1 VP • VP PP 2 NP • I 0 S NP VP • 5 NP • I
0 Det • my 1 PP • P NP 2 Det • my 1 VP VP • PP 5 Det • my
0 Det • a 1 V • shot 2 Det • an 4 PP • P NP 5 Det • an
1 P • in 0 START S •
4 P • in
![Page 76: 6•863J Natural Language Processing Lecture 8: Not an Earley finish](https://reader035.fdocuments.in/reader035/viewer/2022062521/56813ab4550346895da2b6b6/html5/thumbnails/76.jpg)
0 I 1 shot 2 an 3 eleph 4 in 5 my 60 START • S 0 NP I • 1 V shot • 2 Det my • 3 N eleph • 4 P in • 5 Det my •
0 S • NP VP 0 S NP • VP 1 VP V • NP 2 NP Det • N 2 NP Det N • 4 PP P • NP 5 NP Det • N
0 NP • Det N 0 NP NP • PP 2 NP • Det N 3 N • eleph 1 VP V NP • 5 NP • Det N
0 NP • NP PP 1 VP • V NP 2 NP • NP PP 3 N • pjs 2 NP NP • PP 5 NP • NP PP
0 NP • I 1 VP • VP PP 2 NP • I 0 S NP VP • 5 NP • I
0 Det • my 1 PP • P NP 2 Det • my 1 VP VP • PP 5 Det • my
0 Det • a 1 V • shot 2 Det • an 4 PP • P NP 5 Det • an
1 P • in 0 START S •
4 P • in
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0 I 1 shot 2 an 3 eleph 4 in 5 my 60 START • S 0 NP I • 1 V shot • 2 Det an • 3 N eleph • 4 P in • 5 Det my •
0 S • NP VP 0 S NP • VP 1 VP V • NP 2 NP Det • N 2 NP Det N • 4 PP P • NP 5 NP Det • N
0 NP • Det N 0 NP NP • PP 2 NP • Det N 3 N • eleph 1 VP V NP • 5 NP • Det N 6 N • eleph
0 NP • NP PP 1 VP • V NP 2 NP • NP PP 3 N • pjs 2 NP NP • PP 5 NP • NP PP 6 N • pjs
0 NP • I 1 VP • VP PP 2 NP • I 0 S NP VP • 5 NP • I
0 Det • my 1 PP • P NP 2 Det • my 1 VP VP • PP 5 Det • my
0 Det • a 1 V • shot 2 Det • an 4 PP • P NP 5 Det • an
1 P • in 0 START S •
4 P • in
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0 I 1 shot 2 an 3 eleph 4 in 5 my 60 START • S 0 NP I • 1 V shot • 2 Det an • 3 N eleph • 4 P in • 5 Det my •
0 S • NP VP 0 S NP • VP 1 VP V • NP 2 NP Det • N 2 NP Det N • 4 PP P • NP 5 NP Det • N
0 NP • Det N 0 NP NP • PP 2 NP • Det N 3 N • eleph 1 VP V NP • 5 NP • Det N 6 N • eleph
0 NP • NP PP 1 VP • V NP 2 NP • NP PP 3 N • pjs 2 NP NP • PP 5 NP • NP PP 6 N • pjs
0 NP • I 1 VP • VP PP 2 NP • I 0 S NP VP • 5 NP • I
0 Det • my 1 PP • P NP 2 Det • my 1 VP VP • PP 5 Det • my
0 Det • a 1 V • shot 2 Det • an 4 PP • P NP 5 Det • an
1 P • in 0 START S •
4 P • in
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0 I 1 shot 2 an 3 eleph 4 in 5 my 6 pjs 70 START • S 0 NP I • 1 V shot • 2 Det an • 3 N eleph • 4 P in • 5 Det my • 6 N pjs •
0 S • NP VP 0 S NP • VP 1 VP V • NP 2 NP Det • N 2 NP Det N • 4 PP P • NP 5 NP Det • N
0 NP • Det N 0 NP NP • PP 2 NP • Det N 3 N • eleph 1 VP V NP • 5 NP • Det N 6 N • eleph
0 NP • NP PP 1 VP • V NP 2 NP • NP PP 3 N • pjs 2 NP NP • PP 5 NP • NP PP 6 N • pjs
0 NP • I 1 VP • VP PP 2 NP • I 0 S NP VP • 5 NP • I
0 Det • my 1 PP • P NP 2 Det • my 1 VP VP • PP 5 Det • my
0 Det • a 1 V • shot 2 Det • an 4 PP • P NP 5 Det • a
1 P • in 0 START S •
4 P • in
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0 I 1 shot 2 an 3 eleph 4 in 5 my 6 pjs 70 START • S 0 NP I • 1 V shot • 2 Det an • 3 N eleph • 4 P in • 5 Det my • 6 N pjs •
0 S • NP VP 0 S NP • VP 1 VP V • NP 2 NP Det • N 2 NP Det N • 4 PP P • NP 5 NP Det • N 5 NP Det N •
0 NP • Det N 0 NP NP • PP 2 NP • Det N 3 N • eleph 1 VP V NP • 5 NP • Det N 6 N • eleph
0 NP • NP PP 1 VP • V NP 2 NP • NP PP 3 N • pjs 2 NP NP • PP 5 NP • NP PP 6 N • pjs
0 NP • I 1 VP • VP PP 2 NP • I 0 S NP VP • 5 NP • I
0 Det • my 1 PP • P NP 2 Det • my 1 VP VP • PP 5 Det • my
0 Det • a 1 V • shot 2 Det • an 4 PP • P NP 5 Det • an
1 P • in 0 START S •
4 P • in
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0 I 1 shot 2 an 3 eleph 4 in 5 my 6 pjs 70 START • S 0 NP I • 1 V shot • 2 Det an • 3 N eleph • 4 P in • 5 Det my • 6 N pjs •
0 S • NP VP 0 S NP • VP 1 VP V • NP 2 NP Det • N 2 NP Det N • 4 PP P • NP 5 NP Det • N 5 NP Det N •
0 NP • Det N 0 NP NP • PP 2 NP • Det N 3 N • eleph 1 VP V NP • 5 NP • Det N 6 N • eleph 4 PP P NP •
0 NP • NP PP 1 VP • V NP 2 NP • NP PP 3 N • pjs 2 NP NP • PP 5 NP • NP PP 6 N • pjs 5 NP NP • PP
0 NP • I 1 VP • VP PP 2 NP • I 0 S NP VP • 5 NP • I
0 Det • my 1 PP • P NP 2 Det • my 1 VP VP • PP 5 Det • my
0 Det • a 1 V • shot 2 Det • an 4 PP • P NP 5 Det • an
1 P • in 0 START S •
4 P • in
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0 I 1 shot 2 an 3 eleph 4 in my pjs 70 START • S 0 NP I • 1 V shot • 2 Det an • 3 N eleph • … 6 N pjs •
0 S • NP VP 0 S NP • VP 1 VP V • NP 2 NP Det • N 2 NP Det N • 5 NP Det N •
0 NP • Det N 0 NP NP • PP 2 NP • Det N 3 N • eleph 1 VP V NP • 4 PP P NP •
0 NP • NP PP 1 VP • V NP 2 NP • NP PP 3 N • pjs 2 NP NP • PP 5 NP NP • PP
0 NP • I 1 VP • VP PP 2 NP • I 0 S NP VP • 2 NP NP PP •
0 Det • my 1 PP • P NP 2 Det • my 1 VP VP • PP 1 VP VP PP •
0 Det • an 1 V • shot 2 Det • an 4 PP • P NP
1 P • in 0 START S •
4 P • in
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0 I 1 shot 2 my 3 eleph 4 in my pjs 70 START • S 0 NP I • 1 V shot • 2 Det an • 3 N eleph • … 6 N pjs •
0 S • NP VP 0 S NP • VP 1 VP V • NP 2 NP Det • N 2 NP Det N • 5 NP Det N •
0 NP • Det N 0 NP NP • PP 2 NP • Det N 3 N • eleph 1 VP V NP • 4 PP P NP •
0 NP • NP PP 1 VP • V NP 2 NP • NP PP 3 N • pjs 2 NP NP • PP 5 NP NP • PP
0 NP • I 1 VP • VP PP 2 NP • I 0 S NP VP • 2 NP NP PP •
0 Det • my 1 PP • P NP 2 Det • my 1 VP VP • PP 1 VP VP PP •
0 Det • an 1 V • shot 2 Det • an 4 PP • P NP 7 PP • P NP
1 P • in 0 START S •
4 P • in
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0 I 1 shot 2 an 3 eleph 4 in my pjs 70 START • S 0 NP I • 1 V shot • 2 Det an • 3 N eleph • … 6 N pjs •
0 S • NP VP 0 S NP • VP 1 VP V • NP 2 NP Det • N 2 NP Det N • 5 NP Det N •
0 NP • Det N 0 NP NP • PP 2 NP • Det N 3 N • eleph 1 VP V NP • 4 PP P NP •
0 NP • NP PP 1 VP • V NP 2 NP • NP PP 3 N • pjs 2 NP NP • PP 5 NP NP • PP
0 NP • I 1 VP • VP PP 2 NP • I 0 S NP VP • 2 NP NP PP •
0 Det • my 1 PP • P NP 2 Det • my 1 VP VP • PP 1 VP VP PP •
0 Det • an 1 V • shot 2 Det • an 4 PP • P NP 7 PP • P NP
1 P • in 0 START S • 1 VP V NP •
4 P • in 2 NP NP • PP
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0 I 1 shot 2 an 3 eleph 4 in my pjs 70 START • S 0 NP I • 1 V shot • 2 Det an • 3 N eleph • … 6 N pjs •
0 S • NP VP 0 S NP • VP 1 VP V • NP 2 NP Det • N 2 NP Det N • 5 NP Det N •
0 NP • Det N 0 NP NP • PP 2 NP • Det N 3 N • eleph 1 VP V NP • 4 PP P NP •
0 NP • NP PP 1 VP • V NP 2 NP • NP PP 3 N • pjs 2 NP NP • PP 5 NP NP • PP
0 NP • I 1 VP • VP PP 2 NP • I 0 S NP VP • 2 NP NP PP •
0 Det • my 1 PP • P NP 2 Det • my 1 VP VP • PP 1 VP VP PP •
0 Det • an 1 V • shot 2 Det • an 4 PP • P NP 7 PP • P NP
1 P • in 0 START S • 1 VP V NP •
4 P • in 2 NP NP • PP
0 S NP VP •
1 VP VP • PP
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0 I 1 shot 2 an 3 eleph 4 in my pjs 70 START • S 0 NP I • 1 V shot • 2 Det an • 3 N eleph • … 6 N pjs •
0 S • NP VP 0 S NP • VP 1 VP V • NP 2 NP Det • N 2 NP Det N • 5 NP Det N •
0 NP • Det N 0 NP NP • PP 2 NP • Det N 3 N • eleph 1 VP V NP • 4 PP P NP •
0 NP • NP PP 1 VP • V NP 2 NP • NP PP 3 N • pjs 2 NP NP • PP 5 NP NP • PP
0 NP • I 1 VP • VP PP 2 NP • I 0 S NP VP • 2 NP NP PP •
0 Det • my 1 PP • P NP 2 Det • my 1 VP VP • PP 1 VP VP PP •
0 Det • an 1 V • shot 2 Det • an 4 PP • P NP 7 PP • P NP
1 P • in 0 START S • 1 VP V NP •
4 P • in 2 NP NP • PP
0 S NP VP •
1 VP VP • PP
7 P • in
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0 I 1 shot 2 an 3 eleph 4 in my pjs 70 START • S 0 NP I • 1 V shot • 2 Det an • 3 N eleph • … 6 N pjs •
0 S • NP VP 0 S NP • VP 1 VP V • NP 2 NP Det • N 2 NP Det N • 5 NP Det N •
0 NP • Det N 0 NP NP • PP 2 NP • Det N 3 N • eleph 1 VP V NP • 4 PP P NP •
0 NP • NP PP 1 VP • V NP 2 NP • NP PP 3 N • pjs 2 NP NP • PP 5 NP NP • PP
0 NP • I 1 VP • VP PP 2 NP • I 0 S NP VP • 2 NP NP PP •
0 Det • my 1 PP • P NP 2 Det • my 1 VP VP • PP 1 VP VP PP •
0 Det • an 1 V • shot 2 Det • an 4 PP • P NP 7 PP • P NP
1 P • in 0 START S • 1 VP V NP •
4 P • in 2 NP NP • PP
0 S NP VP •
1 VP VP • PP
7 P • in
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0 I 1 shot 2 an 3 eleph 4 in my pjs 70 START • S 0 NP I • 1 V shot • 2 Det an • 3 N eleph • … 6 N pjs •
0 S • NP VP 0 S NP • VP 1 VP V • NP 2 NP Det • N 2 NP Det N • 5 NP Det N •
0 NP • Det N 0 NP NP • PP 2 NP • Det N 3 N • eleph 1 VP V NP • 4 PP P NP •
0 NP • NP PP 1 VP • V NP 2 NP • NP PP 3 N • pjs 2 NP NP • PP 5 NP NP • PP
0 NP • I 1 VP • VP PP 2 NP • I 0 S NP VP • 2 NP NP PP •
0 Det • my 1 PP • P NP 2 Det • my 1 VP VP • PP 1 VP VP PP •
0 Det • an 1 V • shot 2 Det • an 4 PP • P NP 7 PP • P NP
1 P • in 0 START S • 1 VP V NP •
4 P • in 2 NP NP • PP
0 S NP VP •
1 VP VP • PP
7 P • in
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0 I 1 shot 2 an 3 eleph 4 in my pjs 70 START • S 0 NP I • 1 V shot • 2 Det an • 3 N eleph • … 6 N pjs •
0 S • NP VP 0 S NP • VP 1 VP V • NP 2 NP Det • N 2 NP Det N • 5 NP Det N •
0 NP • Det N 0 NP NP • PP 2 NP • Det N 3 N • eleph 1 VP V NP • 4 PP P NP •
0 NP • NP PP 1 VP • V NP 2 NP • NP PP 3 N • pjs 2 NP NP • PP 5 NP NP • PP
0 NP • I 1 VP • VP PP 2 NP • I 0 S NP VP • 2 NP NP PP •
0 Det • my 1 PP • P NP 2 Det • my 1 VP VP • PP 1 VP VP PP •
0 Det • an 1 V • shot 2 Det • an 4 PP • P NP 7 PP • P NP
1 P • in 0 START S • 1 VP V NP •
4 P • in 2 NP NP • PP
0 S NP VP •
1 VP VP • PP
7 P • in
0 START S •
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0 I 1 shot 2 an 3 eleph 4 in my pjs 70 START • S 0 NP I • 1 V shot • 2 Det an • 3 N eleph • … 6 N pjs •
0 S • NP VP 0 S NP • VP 1 VP V • NP 2 NP Det • N 2 NP Det N • 5 NP Det N •
0 NP • Det N 0 NP NP • PP 2 NP • Det N 3 N • eleph 1 VP V NP • 4 PP P NP •
0 NP • NP PP 1 VP • V NP 2 NP • NP PP 3 N • pjs 2 NP NP • PP 5 NP NP • PP
0 NP • I 1 VP • VP PP 2 NP • I 0 S NP VP • 2 NP NP PP •
0 Det • my 1 PP • P NP 2 Det • my 1 VP VP • PP 1 VP VP PP •
0 Det • an 1 V • shot 2 Det • an 4 PP • P NP 7 PP • P NP
1 P • in 0 START S • 1 VP V NP •
4 P • in 2 NP NP • PP
0 S NP VP •
1 VP VP • PP
7 P • in
0 START S •
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0 I 1 shot 2 an 3 eleph 4 in my pjs 70 START • S 0 NP I • 1 V shot • 2 Det an • 3 N eleph • … 6 N pjs •
0 S • NP VP 0 S NP • VP 1 VP V • NP 2 NP Det • N 2 NP Det N • 5 NP Det N •
0 NP • Det N 0 NP NP • PP 2 NP • Det N 3 N • eleph 1 VP V NP • 4 PP P NP •
0 NP • NP PP 1 VP • V NP 2 NP • NP PP 3 N • pjs 2 NP NP • PP 5 NP NP • PP
0 NP • I 1 VP • VP PP 2 NP • I 0 S NP VP • 2 NP NP PP •
0 Det • my 1 PP • P NP 2 Det • my 1 VP VP • PP 1 VP VP PP •
0 Det • an 1 V • shot 2 Det • an 4 PP • P NP 7 PP • P NP
1 P • in 0 START S • 1 VP V NP •
4 P • in 2 NP NP • PP
0 S NP VP •
1 VP VP • PP
7 P • in
0 START S •
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0 I 1 shot 2 an 3 eleph 4 in my pjs 70 START • S 0 NP I • 1 V shot • 2 Det an • 3 N eleph • … 6 N pjs •
0 S • NP VP 0 S NP • VP 1 VP V • NP 2 NP Det • N 2 NP Det N • 5 NP Det N •
0 NP • Det N 0 NP NP • PP 2 NP • Det N 3 N • eleph 1 VP V NP • 4 PP P NP •
0 NP • NP PP 1 VP • V NP 2 NP • NP PP 3 N • pjs 2 NP NP • PP 5 NP NP • PP
0 NP • I 1 VP • VP PP 2 NP • I 0 S NP VP • 2 NP NP PP •
0 Det • my 1 PP • P NP 2 Det • my 1 VP VP • PP 1 VP VP PP •
0 Det • an 1 V • shot 2 Det • an 4 PP • P NP 7 PP • P NP
1 P • in 0 START S • 1 VP V NP •
4 P • in 2 NP NP • PP
0 S NP VP •
1 VP VP • PP
7 P • in
0 START S •
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0 Name 1 shot 2 an 3 eleph 4 in my pjs 70 Start • S 0 NP Name • 1 V shot • 2 Det my • 3 N eleph • … 6 N pjs •
0 S • NP VP 0 S NP • VP 1 VP V • NP 2 NP Det • N 2 NP Det N • 5 NP Det N •
0 NP • Det N 0 NP NP • PP 2 NP • Det N 3 N • eleph 1 VP V NP • 4 PP P NP •
0 NP • NP PP 1 VP • V NP 2 NP • NP PP 3 N • pjs 2 NP NP • PP 5 NP NP • PP
0 NP • Name 1 VP • VP PP 2 NP • Name 0 S NP VP • 2 NP NP PP •
0 Det • the 1 PP • P NP 2 Det • the 1 VP VP • PP 1 VP VP PP •
0 Det • an 1 V • shot 2 Det • an 4 PP • P NP 7 PP • P NP
1 P • in 0 Start S • 1 VP V NP •
4 P • in 2 NP NP • PP
0 S NP VP •
1 VP VP • PP
7 P • in
0 Start S •
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0 Name 1 shot 2 an 3 eleph 4 in my pjs 70 Start • S 0 NP Name • 1 V shot • 2 Det an • 3 N eleph • …6 N pjs •
0 S • NP VP 0 S NP • VP 1 VP V • NP 2 NP Det • N 2 NP Det N • 5 NP Det N •
0 NP • Det N 0 NP NP • PP 2 NP • Det N 3 N • eleph 1 VP V NP • 4 PP P NP •
0 NP • NP PP 1 VP • V NP 2 NP • NP PP 3 N • pjs 2 NP NP • PP 5 NP NP • PP
0 NP • Name 1 VP • VP PP 2 NP • Name 0 S NP VP • 2 NP NP PP •
0 Det • the 1 PP • P NP 2 Det • the 1 VP VP • PP 1 VP VP PP •
0 Det • an 1 V • shot 2 Det • an 4 PP • P NP 7 PP • P NP
1 P • in 0 Start S • 1 VP V NP •
4 P • in 2 NP NP • PP
0 S NP VP •
1 VP VP • PP
7 P • in
0 Start S •
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0 Name 1 shot 2 an 3 eleph 4 in my pjs 70 Start • S 0 NP Name • 1 V shot • 2 Det an • 3 N eleph • …6 N pjs •
0 S • NP VP 0 S NP • VP 1 VP V • NP 2 NP Det • N 2 NP Det N • 5 NP Det N •
0 NP • Det N 0 NP NP • PP 2 NP • Det N 3 N • eleph 1 VP V NP • 4 PP P NP •
0 NP • NP PP 1 VP • V NP 2 NP • NP PP 3 N • pjs 2 NP NP • PP 5 NP NP • PP
0 NP • Name 1 VP • VP PP 2 NP • Name 0 S NP VP • 2 NP NP PP •
0 Det • the 1 PP • P NP 2 Det • the 1 VP VP • PP 1 VP VP PP •
0 Det • an 1 V • shot 2 Det • an 4 PP • P NP 7 PP • P NP
1 P • in 0 Start S • 1 VP V NP •
4 P • in 2 NP NP • PP
0 S NP VP •
1 VP VP • PP
7 P • in
0 Start S •
Complexity Max # of state sets = O(n)
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0 Name 1 shot 2 an 3 eleph 4 in my pjs 70 Start • S 0 NP Name • 1 V shot • 2 Det an • 3 N eleph • …6 N pjs •
0 S • NP VP 0 S NP • VP 1 VP V • NP 2 NP Det • N 2 NP Det N • 5 NP Det N •
0 NP • Det N 0 NP NP • PP 2 NP • Det N 3 N • eleph 1 VP V NP • 4 PP P NP •
0 NP • NP PP 1 VP • V NP 2 NP • NP PP 3 N • pjs 2 NP NP • PP 5 NP NP • PP
0 NP • Name 1 VP • VP PP 2 NP • Name 0 S NP VP • 2 NP NP PP •
0 Det • the 1 PP • P NP 2 Det • the 1 VP VP • PP 1 VP VP PP •
0 Det • an 1 V • shot 2 Det • an 4 PP • P NP 7 PP • P NP
1 P • in 0 Start S • 1 VP V NP •
4 P • in 2 NP NP • PP
0 S NP VP •
1 VP VP • PP
7 P • in
0 Start S •
Complexity Max size of state set (column)
|G|n
O(|G| n)
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0 Name 1 shot 2 an 3 eleph 4 in my pjs 70 Start • S 0 NP Name • 1 V shot • 2 Det an • 3 N eleph • …6 N pjs •
0 S • NP VP 0 S NP • VP 1 VP V • NP 2 NP Det • N 2 NP Det N • 5 NP Det N •
0 NP • Det N 0 NP NP • PP 2 NP • Det N 3 N • eleph 1 VP V NP • 4 PP P NP •
0 NP • NP PP 1 VP • V NP 2 NP • NP PP 3 N • pjs 2 NP NP • PP 5 NP NP • PP
0 NP • Name 1 VP • VP PP 2 NP • Name 0 S NP VP • 2 NP NP PP •
0 Det • the 1 PP • P NP 2 Det • the 1 VP VP • PP 1 VP VP PP •
0 Det • an 1 V • shot 2 Det • an 4 PP • P NP 7 PP • P NP
1 P • in 0 Start S • 1 VP V NP •
4 P • in 2 NP NP • PP
0 S NP VP •
1 VP VP • PP
7 P • in
0 Start S •
Max time to process one item in set
Any possibleitem in set i, soO(|G|n)
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6•863J/9•611J SP05
Grand total
n x |G| n x |G| n = O(|G|2 n3)# state sets x # items/state set x max time/item
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6•863J/9•611J SP05
Left Recursion Kills Pure Top-Down Parsing …
VP
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6•863J/9•611J SP05
Left Recursion Kills Pure Top-Down Parsing …
VP
VP PP
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6•863J/9•611J SP05
Left Recursion Kills Pure Top-Down Parsing …
VP PP
VP
VP PP
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6•863J/9•611J SP05
Left Recursion Kills Pure Top-Down Parsing …
VP PP
VP
VP PP
VP PP
makes new hypothesesad infinitum before we’veseen the PPs at all
hypotheses try to predictin advance how many PP’s will arrive in input
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6•863J/9•611J SP05
… but Earley’s Alg is Okay!
VP
PPVP1 VP . VP PP
(in column 1)
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6•863J/9•611J SP05
… but Earley’s Alg is Okay!
VP
V NP
1 VP V NP .
shot an eleph
VP
PPVP1 VP . VP PP
(in column 1)
(in column 4)
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6•863J/9•611J SP05
… but Earley’s Alg is Okay!
VP
V NP
VP
PPVP
V NP
complete
shot an eleph
1 VP VP . PP
VP
PPVP1 VP . VP PP
(in column 1)
(in column 4)
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6•863J/9•611J SP05
… but Earley’s Alg is Okay!
VP
V NP
VP
PPVP
V NPshot an eleph
in a pjs
1 VP VP PP .
VP
PPVP1 VP . VP PP
(in column 1)
(in column 7)
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6•863J/9•611J SP05
… but Earley’s Alg is Okay!
VP
V NP
VP
PPVP
V NPshot an elephant
in my pjs
1 VP VP PP .
VP
PPVP1 VP . VP PPcan be reused
(in column 1)
(in column 7)
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6•863J/9•611J SP05
… but Earley’s Alg is Okay!
VP
V NP
VP
PPVP
V NP
VP
PP1 VP VP . PP
shot an eleph
In my pjs
VP
PPVP1 VP . VP PPcan be reused
(in column 1)
(in column 7)
complete
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6•863J/9•611J SP05
… but Earley’s Alg is Okay!
VP
V NP
VP
PPVP
V NP
VP
PP
shot an eleph
in my pjs
in his bed
1 VP VP PP .
VP
PPVP1 VP . VP PPcan be reused
(in column 1)
(in column 10)
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6•863J/9•611J SP05
… but Earley’s Alg is Okay!
VP
V NP
VP
PPVP
V NP
VP
PP
shot an eleph
in my pjs
in his bed
1 VP VP PP .
VP
PPVP1 VP . VP PPcan be reused again
(in column 1)
(in column 10)
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6•863J/9•611J SP05
… but Earley’s Alg is Okay!
VP
V NP
VP
PPVP
V NP
VP
PPVP1 VP . VP PPcan be reused again
VP
PP
VP
PP1 VP VP . PP
shot an eleph
in my pjs
in his bed
(in column 1)
(in column 10)
complete
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6•863J/9•611J SP05
How this algorithm is clever• Handling left-recursion• Tail-recursion trick• Example: John’s brother’s book• NP NP NP | NP Noun | Noun ’s• Note how this loops on endless call to
NPNP
NPNP
’sNP Noun
bookJohn NP’s brother
NP …but predict cutsoff after 1 round!
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6•863J/9•611J SP05
Note tail recursion• Stshot set S0 :• Add triples: [NPNoun, 0, 0]
[NPNPNP, 0, 0] predict: ?[NPNPNP, 0, 0] … No need!
Duplicates!
Note tail recursion: the call returns to itself – so noneed to ‘keep’ return addresses in stack!
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6•863J/9•611J SP05
The edge loops to itself:
[NPNPNP, 0, 0]
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0 I 1 shot 2 an 3 eleph 4 in my pjs 70 START . S 0 NP I . 1 V shot . 2 Det an . 3 N eleph … 6 N pjs .
0 S . NP VP 0 S NP . VP 1 VP V . NP 2 NP Det . N 2 NP Det N . 5 NP Det N .
0 NP . Det N 0 NP NP . PP 2 NP . Det N 3 N . eleph 1 VP V NP . 4 PP P NP .
0 NP . NP PP 1 VP . V NP 2 NP . NP PP 3 N . pjs 2 NP NP . PP 5 NP NP . PP
0 NP . I 1 VP . VP PP 2 NP . I 0 S NP VP . 2 NP NP PP .
0 Det . my 1 PP . P NP 2 Det . my 1 VP VP . PP 1 VP VP PP .
0 Det . an 1 V . shot 2 Det . an 4 PP . P NP 7 PP . P NP
1 P . in 0 START S . 1 VP V NP .
4 P . in 2 NP NP . PP
0 S NP VP .
1 VP VP . PP
7 P . in
0 START S .
completed a VP in col 4col 1 lets us use it in a VP PP structure
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0 I 1 shot 2 an 3 eleph 4 in my pjs 70 START . S 0 NP I . 1 V shot . 2 Det an . 3 N pjs. … 6 N pjs .
0 S . NP VP 0 S NP . VP 1 VP V . NP 2 NP Det . N 2 NP Det N . 5 NP Det N .
0 NP . Det N 0 NP NP . PP 2 NP . Det N 3 N . eleph 1 VP V NP . 4 PP P NP .
0 NP . NP PP 1 VP . V NP 2 NP . NP PP 3 N . pjs 2 NP NP . PP 5 NP NP . PP
0 NP . I 1 VP . VP PP 2 NP . I 0 S NP VP . 2 NP NP PP .
0 Det . my 1 PP . P NP 2 Det . my 1 VP VP . PP 1 VP VP PP .
0 Det . an 1 V . shot 2 Det . an 4 PP . P NP 7 PP . P NP
1 P . in 0 START S . 1 VP V NP .
4 P . in 2 NP NP . PP
0 S NP VP .
1 VP VP . PP7 P . in
0 START S .
completed that VP = VP PP in col 7col 1 would let us use it in a VP PP structurecan reuse col 1 as often as we need
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6•863J/9•611J SP05
What about parsing?
• If we retrieve all parses explicitly, then exponential
• We have to pack possible paths into the space
• Number the items, let’s see how this can work
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0 Name 1 shot 2 an 3 eleph 4 in my pjs 7
0 Start • S 1 0 NP Name • 8 1 V shot • 16 2 Det an • 23 3 N eleph • 27 … 6 N pjs • 40
0 S • NP VP 2 0 S NP • VP 9 1 VP V • NP 17 2 NP Det • N 24 2 NP Det N • 28 5 NP Det N • 41
0 NP • Det N 3 0 NP NP • PP 10 2 NP • Det N 18 3 N • eleph 25 1 VP V NP • 29 4 PP P NP • 42
0 NP • NP PP 4 1 VP • V NP 11 2 NP • NP PP 19 3 N • pjs 26 2 NP NP • PP 30 5 NP NP • PP 43
0 NP • Name 5 1 VP • VP PP 12 2 NP • Name 20 0 S NP VP • 31 2 NP NP PP • 44
0 Det • the 6 1 PP • P NP 13 2 Det • the 21 1 VP VP • PP 32 1 VP VP PP • 45
0 Det • an7 1 V • shot 14 2 Det • an 22 4 PP • P NP 33 7 PP • P NP 46
1 P • an 15 0 Start S • 34 1 VP V NP • 47
4 P • in 35 2 NP NP • PP 48
0 S NP VP • 49
1 VP VP • PP 50
7 P • in 51
0 Start S • 52
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This implicitly shares structure:packed, shared forest
NP [VP VP] [S, S]
Name
I
V
shot NP
Det
an
N
PPP NP
Det Neleph my pjsin
NP
VP
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+ and - for Earley algorithm• Note that Earley algorithm already does
some top-down filtering: any item (state, or triple) added to a given State Set must be compatible with some derivation on the left, e.g.,Swi where wi is the sentence seen so far
wi
S*
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+ and – for Earley algorithm
• Manipulates explicit representation of grammar – transparent
• Can add simple method to deal with optional elements in the rule, e.g., NPName (PP) or Kleene *, etc.
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What are the weak points?
• Explicit representation of rules: wastes time building them.
• Better to ‘compile’ items and their connections ahead of time: compute links between items in advance
• Example: if sentence starts an NP, and NP expands as Det N, go directly from S to Det N. (this is what LR parsing method uses)
• Does filtering on left but not on the right
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Weak points of Earley, cont’d• Example: What did John eat• The Earley parser will build all possible
expansions Swh, Sdid, etc.• This suggests that we define the following
lookahead filter for nonterminals A:FIRST(A)= {x|A x}, x= 1 tokene.g., FIRST(S)= who, did, the, etc.
• Apply by throwing out triples that whose first item doesn’t match this
• Reduces state set size by a factor of 1.5-1.8
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Use of FIRST calculation
• Example: John ate the ice-cream on the table
• Using FIRST, we throw out the expansions of NP that start with Det, etc. – and there are typically hundreds of these
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Anything else?
• If anything, Earley parsing is too good – it gets all the parses, even ones that people do not
• We shall see how to deal with this, using probabilities on rules; and
• Other parsing methods• But first, what do people do?• Consider the examples
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Precomputing predictor• Predictor(AB, [i,j]):
For every grammar rule B:add a rule B at [j,j]
• Note that this depends only on B and the grammar• We can save time by pre-computing predictor
For every grammar rule B:PREDICT[B].append(B)
Repeat until nothing new is added: For every B:For every CD in PREDICT[B]:For every grammar rule D:PREDICT[B].append(D)
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Worst case time for Earley algorithm
• Is the cubic bound ever reached in artificial or natural languages?
• Here is the artificial ‘worst case’ - # of parses arbitrarily large with sentence length; infinite ambiguity
• Here is the grammar: SSS, SSa{a, aa, aaa, aaaa,…}
• # of binary trees with n leaves= 1,1,2,5,14,42,132,429,1430,4862,16796,…=
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This does indeed get costly -Verb NP PP example
30201000
2
4
6
8
10
12
NP–PP ambiguity sentences NP P NP•••
with Earley's algorithm
length,words
Tim
e, se
cond
stime, secs•
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Anything else?
• If anything, Earley parsing is too good – it gets all the parses, even ones that people do not
• We shall see how to deal with this, using probabilities on rules; and
• Other parsing methods• But first, what do people do?• Consider the examples
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Wouldn’t matter so much – but it does seem to match what people do
• Both left- and right- branching ‘structures’ seem to be readily parseable by people without any sort of memory load (all other things being equal)
• John’s brother’s mother’s aunt…•• I believed that Mary shot that Fred knew
that Bill shoveled snow
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Pictures of this••
John’s brother’s book shot that Fred knew that Bill shoveled snow
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So what’s hard for people to process?
The rat died
the cat chased
the dog bit “center-embedded”
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Why is this hard?
• Model: people have to “hold onto” open predicates (definition: open if verb+arguments have not yet been put together)
• In the preceding example, we have to hold onto a stack of Subjects (the rat, the cat, the dog…) before the corresponding verbs are seen
• This even shows up in unexpected places – speech intonational pattern actually seems to transduce center-embedded structures into left- or right- branching ones
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Chomsky & Miller, 1959-63 analysis
the dog that chased the cat that bit the rat
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Parsing vs• intonational contours
Syntactic structure is center-embedded:NP
NPthe rat
S
thatchased
NP VPNP
the cat
proS
thatbit
NP VPNPthe ratpro
NP
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But the intonational structure follows this:
NPNP
the rat
S
thatNP
chased
VP
pro
NP
the catNP
Suggests 2-stage parser (proposed by C&M):Stage 1: parse into ‘flat’ structureStage 2: make 2nd pass & rearrange hierarchically
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Also hints at how to do semantic interpretation – akin to syntax-driven translation
• Recall from compilers: if we complete the right-hand side of a rule, we can now fire off any associsated semantic action (because we now have the item and all its ‘arguments’
• This amounts to getting left-most complete subtree at each point to interpret
• Example:VPV NP , e•g•, “shot the ice-cream”Can now ‘interpret’ thispair syntactic, ‘semantic’ rule: VPV NP, apply VP(NP)
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One more search space enumeration that will be of some value
• Left-corner parsing• Looks bottom-up in serial fashion for the
first symbol (left-corner) of a phrase; and then tries to confirm the rest of the phrase top-down
• Tries to combine best features of b-u and t-d
• Clearly geared to the way a particular language (eg English) is set up
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A picture of left-corner parsingS
NP
the
VP
Noun
SNP VP
NPthe Noun
12
find
predict
shot
VPshot NP
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This works well
• In a language like English:• A head-first language (function-
argument)• What about German, Dutch, Japanese?• dat het mesije van Holland houdt• “that the girl from Holland liked”• These are head-final languages
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Parsing as search: different search patterns different parsing methods different traversals thru
Top-down Bottom-up
Serial Recursive descent
Shift-reduce (LR)
Parallel(simulate ndet machine)
Earley CKY(Cocke-Kasami-Younger)
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A pictureRecursive descent
Earley 1
2 2
3
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And a few moreshift-reduce CKY
2
1 3
41
21
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What about constructing grammars?
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Grammars for natural languages
• Where do the rules come from?• Roughly: read them off of parse trees…• A “rule-based”, construction-based
point of view• Take ‘surface’ phrase patterns (mostly)• But we still want to map to an
underlying ‘logical’ form• How do we start out?
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Reading rules from parse trees…
SNP VPVPV NPNPDet NNP N*
Can’t we get a computer to do this?
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Key elements – part 1• Establish basic phrase types: S, VP, NP,
PP, …• Where do these come from???
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What kinds of phrases are there?
• Noun phrases, verb phrases, adjectival phrases (“green with envy”), adverbial phrases (“quickly up the hill”), prepositional phrases (“off the wall”), etc.
• In general: grounded on lexical items• Shows us the constraints on context-free
rules for natural grammars• Example:
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Phrase types are constrained by lexical projectionVerb Phrase Verb Noun Phrase
“is-a” (“kick the ball”)Prepositional Phrase Preposition Noun Phrase
(“on the table”)Adjective Phrase Adjective Prep.
Phrase(“green with envy”)
Etc. … what is the pattern?
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Function-argument relation
XP X arguments, where X= Noun, Verb, Preposition, Adjective (all lexical categories in the language)
Like function-argument structure(so-called “Xbar theory”)Constrains what grammar rules cannot be:Verb Phrase Noun Noun Phraseor evenVerb Phrase Noun Phrase Verb Noun Phrase
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English is function-argument form
function
at
args
green
soldthe stocka bargain pricewith envy
the over-priced stock
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Other languages are the mirror-inverse: arg-function
atgreen
soldthe stock
a bargain pricewith envy
the over-priced stockThis is like Japanese
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Key elements – part 2
• Establish verb subcategories• What are these?
• Different verbs take different # arguments• 0, 1, 2 arguments (‘complements’)• Poirot thought; Poirot thought the gun; Poirot
thought the gun was the cause.• Some verbs take certain sentence complements:• I know who John shot/? I think who John shot
propositional types: • Embedded questions: I wonder whether…• Embedded proposition: I think that John shot Mary
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Key elements
• Subtlety to this• Believe, know, think, wonder,…
• ? I believe why John likes ice-cream• I know why John likes ice-cream• I believe that John likes ice-cream• I believe (that) John likes ice-cream
• # args, type: Verb subcategories• How many subcategories are there?• What is the structure?
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Idea for phrases
• They are based on ‘projections’ of words (lexical items) – imagine features ‘percolating’ up
know [V +proposition]
XP [ ]V +proposition
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Heads of phrases
know [V +proposition]
V +proposition
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The parse structure for ‘embedded’ sentences
I believe (that) John likes ice-cream
S
NP VP
I
Vbelieve
that J. likes ice-cream
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New phrase type: S-bar
NP VP
I
Vbelieve
S
Sbar
that J. likes ice-cream
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Sbar VP
Vbelieve
Sbar
that
Comp S
J. likes ice-cream
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Sbar VP
Vbelieve
Sbar
Comp S
J. likes ice-cream
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In fact, true for all sentences…
Comp S
S
J. likes ice-cream
SbarJohn likes ice-cream
Why?
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What rules will we need?
• (U do it..)
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Verb types - continued
• What about:Clinton admires honesty/Honesty
admires ClintonHow do we encode these in a CFG?Should we encode them?• Colorless green ideas sleep furiously• Revolutionary new ideas appear
infrequently
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Features
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The trouble with tribbles
morphology of a single word: Verb[head=thrill, tense=present, num=sing, person=3,…] thrills
projection of features up to a bigger phrase VP[head=, tense=, num=…] V[head=, tense=, num=…] NP
provided is in the set TRANSITIVE-VERBS
agreement between sister phrases:S[head=, tense=] NP[num=,…] VP[head=, tense=, num=…]
provided is in the set TRANSITIVE-VERBS
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3 Common Ways to Use Features
NPVerb
VPNP
S
A roller coaster thrills every teenager
Verb[head=thrill, tense=present, num=sing, person=3,…] thrills
VP[head=, tense=, num=…] V[head=, tense=, num=…] NP
S[head=, tense=] NP[num=,…] VP[head=, tense=, num=…]
num=singnum=sing
(comprehensionperspective)
num=sing
thrills
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CFG Solution
• Encode constraints into the non-terminals• Noun/verb agreement
S SgSS PlSSgS SgNP SgVPSgNP SgDet SgNom
• Verb subcategories:IntransVP IntransVTransVP TransV NP
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Problems with this – how much info?
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Agreement gets complex…
POS
SUBPOS
GENDER
NUMBER
CASE
POSSG
POSSNPERSON
TENSEDCOMP
NEG
VOICE
VAR
–Czech: AGFS3----1A----
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Lots of features(tense, number, person, gaps, vowels, commas, wh, etc., etc....)
Sorry, that’s just how language is … You know too much to write it down easily!
He
gone
has
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Other sentence types
• Questions: • Will John eat ice-cream?• Did John eat ice-cream?
• How do we encode this?
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`Empty’ elements or categories• Where surface phrase is displaced from its canonical
syntactic position• Examples:
• The ice-cream was eaten vs.• John ate the ice-cream• What did John eat?• What did Bill say that that John thought the cat ate?• For What x, did Bill say… the cat ate x• Bush is too stubborn to talk to• Bush is too stubborn [x to talk to Bush]• Bush is too stubborn to talk to the Pope• Bush is too stubborn [Bush to talk to the Pope]
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6•863J/9•611J SP05
More interesting clause types• Apparently “long distance” effects:
‘displacement’ of phrases from their ‘base’ positions
1. So-called ‘wh-movement’:What did John eat ?
2. Topicalization (actually the same)On this day, it snowed two feet.
3. Other cases: so-called ‘passive’:The eggplant was eaten by John
• How to handle this?
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6•863J/9•611J SP05
We can think of this as ‘fillers’ and ‘gaps’
• Filler= the displaced item• Gap = the place where it belongs, as argument• Fillers can be NPs, PPs, S’s• Gaps are invisible- so hard to parse! (we have
to guess)• Can be complex:
Which book did you file__ without__ reading__ ?
Which violins are these sonatas difficult to play__ on ___