parsing based on presentations from chris manning’s course on statistical parsing (stanford)
TRANSCRIPT
Parsing
Based on presentations from Chris Manning’s course on Statistical Parsing (Stanford)
S
NVP
VNP
D N
John hit the ball
Levels of analysis
Level Elements used
Morphology/Lexical Words
POS (morpho-synactic), WSD Words
Shallow syntax parsing Phrases
Full syntax parsing Sentence
NER, MWE Phrases
SRL Parsed trees
Full semantic parsing Parsed trees
Buffalo…
Parsing is a difficult task!
^______^ so excited! #Khaleesi #miniKhaleesi #GoT
Ambiguities
POS tags (e.g., books : a verb or a noun?)
Compositional expression meanings (e.g., he spilled the beans about his past)
Syntactic attachments (V N PP)(e.g., I ate my spaghettis with a fork)
Global semantic ambiguities (e.g., bear left at zoo) Usually,
ambiguities in one layer may be
resolved in upper layers
Ambiguities
Fed raises interest rates 0.5 % in effort to control inflation
Motivation
Parsing may help to resolve ambiguities
Parsing is a step toward understanding the sentence completely
Was shown to improve the results of several NLP applications: MT (Chiang, 2005) Question answering (Hovy et al., 2000) …
Grammar
S NP VP NN interest NP (DT) NN NNS rates NP NN NNS NNS raises NP NNP VBP interest VP V NP VBZ rates …
Minimal grammar on “Fed raises” sentence: 36 parses Simple 10 rule grammar: 592 parses Real-size broad-coverage grammar: millions of parses
Size of grammar
less more
Number of rules
Limits unlikely parses
Butgrammar is not robust
Parses more sentences
Butsentences end up with ever more parses
Statistical parsing
Statistical parsing can help selecting the rules that best fit the input sentence, allowing the grammar to contain more rules
Treebanks( (S
(NP-SBJ (DT The) (NN move))
(VP (VBD followed)
(NP
(NP (DT a) (NN round))
(PP (IN of)
(NP
(NP (JJ similar) (NNS increases))
(PP (IN by)
(NP (JJ other) (NNS lenders)))
(PP (IN against)
(NP (NNP Arizona) (JJ real) (NN estate) (NNS loans))))))
(, ,)
…
The Penn Treebank Project (PTB):Arabic, English, Chinese, Persian, French,…
Advantages of treebanks
Reusability of the laborBroad coverageFrequencies and distributional
informationA way to evaluate systems
Types of parsing
Constituency parsing Dependency parsing
Constituency parsing
Constituents are defined based on linguistic rules (phrases)
Constituents are recursive (NP may contain NP as part of its sub-constituents)
Different linguists may define constituents differently…
Dependency parsing
Dependency structure shows which words depend on (modify or are arguments of) which other words
Parsing
We want to run a grammar backwards to find possible structures for a sentence
Parsing can be viewed as a search problem
We can do this bottom-up or top-downWe search by building a search tree which
his distinct from the parse tree
Phrase structure grammars = context-free grammars (CFG)
G = (T, N, S, R)T is set of terminalsN is set of nonterminalsS is the start symbol (one of the
nonterminals)R is rules/productions of the form X ,
where X is a nonterminal and is a sequence of terminals and nonterminals (possibly an empty sequence)
A grammar G generates a language L
Probabilistic or stochastic context-free grammars (PCFGs)
G = (T, N, S, R, P) T is set of terminals N is set of nonterminals S is the start symbol (one of the nonterminals) R is rules/productions of the form X , where X is a
nonterminal and is a sequence of terminals and nonterminals (possibly an empty sequence)
P(R) gives the probability of each rule
A grammar G generates a language L
Soundness and completeness
A parser is sound if every parse it returns is valid/correct
A parser terminates if it is guaranteed to not go off into an infinite loop
A parser is complete if for any given grammar and sentence, it is sound, produces every valid parse for that sentence, and terminates
(For many purposes, we settle for sound but incomplete parsers: e.g., probabilistic parsers that return a k-best list.)
Top down parsing
Top-down parsing is goal directed
A top-down parser starts with a list of constituents to be built. The top-down parser rewrites the goals in the goal list by matching one against the LHS of the grammar rules, and expanding it with the RHS, attempting to match the sentence to be derived
If a goal can be rewritten in several ways, then there is a choice of which rule to apply (search problem)
Can use depth-first or breadth-first search, and goal ordering
Top down parsing
Disadvantages of top down
A top-down parser will do badly if there are many different rules for the same LHS. Consider if there are 600 rules for S, 599 of which start with NP, but one of which starts with V, and the sentence starts with V
Useless work: expands things that are possible top-down but not there
Repeated work
Repeated work
Bottom up chart parsing
Bottom-up parsing is data directed
The initial goal list of a bottom-up parser is the string to be parsed. If a sequence in the goal list matches the RHS of a rule, then this sequence may be replaced by the LHS of the rule
Parsing is finished when the goal list contains just the start category
If the RHS of several rules match the goal list, then there is a choice of which rule to apply (search problem)
The standard presentation is as shift-reduce parsing
Shift-reduce parsingcats scratch people with claws
cats scratch people with claws SHIFT
N scratch people with claws REDUCE
NP scratch people with claws REDUCE
NP scratch people with claws SHIFT
NP V people with claws REDUCE
NP V people with claws SHIFT
NP V N with clawsREDUCE
NP V NP with clawsREDUCE
NP V NP with claws SHIFT
NP V NP P claws REDUCE
NP V NP P claws SHIFT
NP V NP P N REDUCE
NP V NP P NP REDUCE
NP V NP PP REDUCE
NP VP REDUCE
S REDUCE
Disadvantages of bottom up
Useless work: locally possible, but globally impossible.
Inefficient when there is great lexical ambiguity (grammar-driven control might help here)
Repeated work: anywhere there is common substructure
Parsing as search
Left recursive structures must be found, not predicted
Doing these things doesn't fix the repeated work problem: Both TD and BU parsers can (and frequently do) do
work exponential in the sentence length on NLP problems
Grammar transformations can fix both left-recursion and epsilon productions
Then you parse the same language but with different trees (and fix them post hoc)
Dynamic programming
Rather than doing parsing-as-search, we do parsing as dynamic programming
Examples:CYK (bottom up), Early (top down)
It solves the problem of doing repeated work
Notation
w1n = w1 … wn = the word sequence from 1 to n
wab = the subsequence wa … wb
Njab
= the nonterminal Nj dominating wa … wb
We’ll write P(Ni ζj) to mean P(Ni ζj | Ni )
We’ll want to calculate maxt P(t * wab)
Tree and sentence probabilities
P(t) -- The probability of tree is the product of the probabilities of the rules used to generate it
P(w1n) -- The probability of the sentence is the sum of the probabilities of the trees which have that sentence as their yield
P(w1n) = Σj P(w1n, t) where t is a parse of w1n
= Σj P(t)
Phrase structure grammars = context-free grammars (CFG)
G = (T, N, S, R)T is set of terminalsN is set of nonterminalsS is the start symbol (one of the
nonterminals)R is rules/productions of the form X ,
where X is a nonterminal and is a sequence of terminals and nonterminals (possibly an empty sequence)
A grammar G generates a language L
Chomsky Normal Form (CNF) All rules are of the form X Y Z or X w
A transformation to this form doesn’t change the generative capacity of CFG
With some extra book-keeping in symbol names, you can even reconstruct the same trees with a de-transform Unaries/empties are removed recursively N-ary rules introduce new non-terminals (binarization):
VP V NP PP becomes VP V @VP-V and @VP-V NP PP
In practice it’s a pain Reconstructing n-aries is easy Reconstructing unaries can be trickier
But it makes parsing easier/more efficient
A treebank treeROOT
S
NP VP
N
cats
V NP PP
P NP
clawswithpeoplescratch
NN
After binarization
P
NP
claws
N
@PP->_P
with
NP
N
cats peoplescratch
N
VP
V NP PP
@VP->_V
@VP->_V_NP
ROOT
S
@S->_NP
CYK (Cocke-Younger-Kasami) algorithm
A bottom-up parser using dynamic programming
Assume the PCFG is in Chomsky normal form (CNF)
Maintain |N| nXn tables µ (|N| = number of non-terminals, n = number of input words [length of input sentence])
Fill out the table entries by induction
“Can1 you2 book3 ELAL4 flights5 ?”
w1,1 w1,2 w1,3 w1,4 w1,5
w2,2 w2,3 w2,4 w2,5
w3,3 w3,4 w3,5
w4,4 w4,5
w5,5
1 2 3 4 5
1
2
3
4
5
CYK Base case
–Consider the input strings of length one (i.e., each individual word wi) P(A wi)
–Since the grammar is in CNF: A * wi iff A wi
–So µ[i, i, A] = P(A wi)
CYK Base case
“Can1 you2 book3 ELAL4 flights5 ?”
Aux1
1
.4Nou
n
5
5.5
……
CYK Recursive case
For strings of words of length > 1,A * wij iff there is at least one rule A BCwhere B derives the first k words (between i and i-1 +k ) and C derives the remaining ones (between i+k and j)
(for each non-terminal)Choose the max among all possibilities
A
CB
i i-1+k i+k j
µ[i, j, A)] = µ [i, i-1 +k, B] *
µ [i+k, j, C] *
P(A BC)
CYK Termination
The max prob parse will be µ [1, n, S]
w1,1 w1,2 w1,3 w1,4 w1,5
w2,2 w2,3 w2,4 w2,5
w3,3 w3,4 w3,5
w4,4 w4,5
w5,5
S
Top down: Early algorithmFinds constituents and partial constituents in
inputA B C . D E is partial: only the first half of the A
A
B C D E
A B C . D E
D+ =A
B C D E
A B C D . E
i j i k
j k
Early algorithm
Proceeds incrementally, left-to-rightBefore it reads word 5, it has already
built all hypotheses that are consistent with first 4 words
Reads word 5 & attaches it to immediately preceding hypotheses. Might yield new constituents that are then attached to hypotheses immediately preceding them …
Use a parse table as we did in CKY, so we can look up anything we’ve discovered so far. “Dynamic programming.”
Example (grammar)
ROOT S
S NP VP NP Papa
NP Det N N caviar
NP NP PP N spoon
VP VP PPV ate
VP V NP P with
PP P NP Det the
Det a
0
0 ROOT . Sinitialize
Remember this stands for (0, ROOT . S)
0
0 ROOT . S0 S . NP VP
predict the kind of S we are looking for
Remember this stands for (0, S . NP VP)
0
0 ROOT . S0 S . NP VP0 NP . Det N0 NP . NP PP0 NP . Papa
predict the kind of NP we are looking for(actually we’ll look for 3 kinds: any of the 3 will do)
0
0 ROOT . S0 S . NP VP0 NP . Det N0 NP . NP PP0 NP . Papa0 Det . the0 Det . a
predict the kind of Det we are looking for (2 kinds)
0
0 ROOT . S
0 S . NP VP
0 NP . Det N
0 NP . NP PP
0 NP . Papa
0 Det . the
0 Det . a
predict the kind of NP we’re looking for but we were already looking for these sodon’t add duplicate goals! Note that this happenedwhen we were processing a left-recursive rule.
0 Papa 1
0 ROOT . S 0 NP Papa .0 S . NP VP0 NP . Det N0 NP . NP PP0 NP . Papa0 Det . the0 Det . a
scan: the desired word is in the input!
0 Papa 1
0 ROOT . S 0 NP Papa .0 S . NP VP0 NP . Det N0 NP . NP PP0 NP . Papa0 Det . the0 Det . a scan: failure
0 Papa 1
0 ROOT . S 0 NP Papa .0 S . NP VP0 NP . Det N0 NP . NP PP0 NP . Papa0 Det . the0 Det . a
scan: failure
0 Papa 1
0 ROOT . S 0 NP Papa .0 S . NP VP 0 S NP . VP0 NP . Det N 0 NP NP . PP0 NP . NP PP0 NP . Papa0 Det . the0 Det . a
attach the newly created NP(which starts at 0) to its customers (incomplete constituents that end at 0and have NP after the dot)
0 Papa 1
0 ROOT . S 0 NP Papa .0 S . NP VP 0 S NP . VP0 NP . Det N 0 NP NP . PP0 NP . NP PP 1 VP . V NP0 NP . Papa 1 VP . VP PP0 Det . the0 Det . a
predict
0 Papa 1
0 ROOT . S 0 NP Papa .0 S . NP VP 0 S NP . VP0 NP . Det N 0 NP NP . PP0 NP . NP PP 1 VP . V NP0 NP . Papa 1 VP . VP PP0 Det . the 1 PP . P NP0 Det . a
predict
0 Papa 1
0 ROOT . S 0 NP Papa .0 S . NP VP 0 S NP . VP0 NP . Det N 0 NP NP . PP0 NP . NP PP 1 VP . V NP0 NP . Papa 1 VP . VP PP0 Det . the 1 PP . P NP0 Det . a 1 V . ate
predict
0 Papa 1
0 ROOT . S 0 NP Papa .0 S . NP VP 0 S NP . VP0 NP . Det N 0 NP NP . PP0 NP . NP PP 1 VP . V NP0 NP . Papa 1 VP . VP PP0 Det . the 1 PP . P NP0 Det . a 1 V . ate
predict
0 Papa 1
0 ROOT . S 0 NP Papa .0 S . NP VP 0 S NP . VP0 NP . Det N 0 NP NP . PP0 NP . NP PP 1 VP . V NP0 NP . Papa 1 VP . VP PP0 Det . the 1 PP . P NP0 Det . a 1 V . ate
1 P . withpredict
0 Papa 1 ate 2
0 ROOT . S 0 NP Papa . 1 V ate .0 S . NP VP 0 S NP . VP0 NP . Det N 0 NP NP . PP0 NP . NP PP 1 VP . V NP0 NP . Papa 1 VP . VP PP0 Det . the 1 PP . P NP0 Det . a 1 V . ate
1 P . with scan: success!
0 Papa 1 ate 2
0 ROOT . S 0 NP Papa . 1 V ate .0 S . NP VP 0 S NP . VP0 NP . Det N 0 NP NP . PP0 NP . NP PP 1 VP . V NP0 NP . Papa 1 VP . VP PP0 Det . the 1 PP . P NP0 Det . a 1 V . ate
1 P . with
scan: failure
0 Papa 1 ate 2
0 ROOT . S 0 NP Papa . 1 V ate .0 S . NP VP 0 S NP . VP 1 VP V . NP0 NP . Det N 0 NP NP . PP0 NP . NP PP 1 VP . V NP0 NP . Papa 1 VP . VP PP0 Det . the 1 PP . P NP0 Det . a 1 V . ate
1 P . with
attach
0 Papa 1 ate 2
0 ROOT . S 0 NP Papa . 1 V ate .0 S . NP VP 0 S NP . VP 1 VP V . NP0 NP . Det N 0 NP NP . PP 2 NP . Det N0 NP . NP PP 1 VP . V NP 2 NP . NP PP0 NP . Papa 1 VP . VP PP 2 NP . Papa0 Det . the 1 PP . P NP0 Det . a 1 V . ate
1 P . with
predict
0 Papa 1 ate 2
0 ROOT . S 0 NP Papa . 1 V ate .0 S . NP VP 0 S NP . VP 1 VP V . NP0 NP . Det N 0 NP NP . PP 2 NP . Det N0 NP . NP PP 1 VP . V NP 2 NP . NP PP0 NP . Papa 1 VP . VP PP 2 NP . Papa0 Det . the 1 PP . P NP 2 Det . the0 Det . a 1 V . ate 2 Det . a
1 P . with
predict (these next few stepsshould look familiar)
0 Papa 1 ate 2
0 ROOT . S 0 NP Papa . 1 V ate .0 S . NP VP 0 S NP . VP 1 VP V . NP0 NP . Det N 0 NP NP . PP 2 NP . Det N0 NP . NP PP 1 VP . V NP 2 NP . NP PP0 NP . Papa 1 VP . VP PP 2 NP . Papa0 Det . the 1 PP . P NP 2 Det . the0 Det . a 1 V . ate 2 Det . a
1 P . with
predict
0 Papa 1 ate 2
0 ROOT . S 0 NP Papa . 1 V ate .0 S . NP VP 0 S NP . VP 1 VP V . NP0 NP . Det N 0 NP NP . PP 2 NP . Det N0 NP . NP PP 1 VP . V NP 2 NP . NP PP0 NP . Papa 1 VP . VP PP 2 NP . Papa0 Det . the 1 PP . P NP 2 Det . the0 Det . a 1 V . ate 2 Det . a
1 P . with
scan (this time we fail sincePapa is not the next word)
0 Papa 1 ate 2 the 3
0 ROOT . S 0 NP Papa . 1 V ate . 2 Det the .0 S . NP VP 0 S NP . VP 1 VP V . NP0 NP . Det N 0 NP NP . PP 2 NP . Det N0 NP . NP PP 1 VP . V NP 2 NP . NP PP0 NP . Papa 1 VP . VP PP 2 NP . Papa0 Det . the 1 PP . P NP 2 Det . the0 Det . a 1 V . ate 2 Det . a
1 P . withscan: success!
0 Papa 1 ate 2 the 3
0 ROOT . S 0 NP Papa . 1 V ate . 2 Det the .0 S . NP VP 0 S NP . VP 1 VP V . NP0 NP . Det N 0 NP NP . PP 2 NP . Det N0 NP . NP PP 1 VP . V NP 2 NP . NP PP0 NP . Papa 1 VP . VP PP 2 NP . Papa0 Det . the 1 PP . P NP 2 Det . the0 Det . a 1 V . ate 2 Det . a
1 P . with
0 Papa 1 ate 2 the 3
0 ROOT . S 0 NP Papa . 1 V ate . 2 Det the .0 S . NP VP 0 S NP . VP 1 VP V . NP 2 NP Det . N0 NP . Det N 0 NP NP . PP 2 NP . Det N0 NP . NP PP 1 VP . V NP 2 NP . NP PP0 NP . Papa 1 VP . VP PP 2 NP . Papa0 Det . the 1 PP . P NP 2 Det . the0 Det . a 1 V . ate 2 Det . a
1 P . with
0 Papa 1 ate 2 the 3
0 ROOT . S 0 NP Papa . 1 V ate . 2 Det the .0 S . NP VP 0 S NP . VP 1 VP V . NP 2 NP Det . N0 NP . Det N 0 NP NP . PP 2 NP . Det N 3 N . caviar0 NP . NP PP 1 VP . V NP 2 NP . NP PP 3 N . spoon0 NP . Papa 1 VP . VP PP 2 NP . Papa0 Det . the 1 PP . P NP 2 Det . the0 Det . a 1 V . ate 2 Det . a
1 P . with
0 Papa 1 ate 2 the 3 caviar 4
0 ROOT . S 0 NP Papa . 1 V ate . 2 Det the . 3 N caviar .0 S . NP VP 0 S NP . VP 1 VP V . NP 2 NP Det . N0 NP . Det N 0 NP NP . PP 2 NP . Det N 3 N . caviar0 NP . NP PP 1 VP . V NP 2 NP . NP PP 3 N . spoon0 NP . Papa 1 VP . VP PP 2 NP . Papa0 Det . the 1 PP . P NP 2 Det . the0 Det . a 1 V . ate 2 Det . a
1 P . with
0 Papa 1 ate 2 the 3 caviar 4
0 ROOT . S 0 NP Papa . 1 V ate . 2 Det the . 3 N caviar .0 S . NP VP 0 S NP . VP 1 VP V . NP 2 NP Det . N0 NP . Det N 0 NP NP . PP 2 NP . Det N 3 N . caviar0 NP . NP PP 1 VP . V NP 2 NP . NP PP 3 N . spoon0 NP . Papa 1 VP . VP PP 2 NP . Papa0 Det . the 1 PP . P NP 2 Det . the0 Det . a 1 V . ate 2 Det . a
1 P . with
0 Papa 1 ate 2 the 3 caviar 4
0 ROOT . S 0 NP Papa . 1 V ate . 2 Det the . 3 N caviar .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 . caviar0 NP . NP PP 1 VP . V NP 2 NP . NP PP 3 N . spoon0 NP . Papa 1 VP . VP PP 2 NP . Papa0 Det . the 1 PP . P NP 2 Det . the0 Det . a 1 V . ate 2 Det . a
1 P . with
attach
0 Papa 1 ate 2 the 3 caviar 4
0 ROOT . S 0 NP Papa . 1 V ate . 2 Det the . 3 N caviar .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 . caviar 1 VP V NP .0 NP . NP PP 1 VP . V NP 2 NP . NP PP 3 N . spoon 2 NP NP . PP0 NP . Papa 1 VP . VP PP 2 NP . Papa0 Det . the 1 PP . P NP 2 Det . the0 Det . a 1 V . ate 2 Det . a
1 P . with
attach(again!)
0 Papa 1 ate 2 the 3 caviar 4
0 ROOT . S 0 NP Papa . 1 V ate . 2 Det the . 3 N caviar .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 . caviar 1 VP V NP .0 NP . NP PP 1 VP . V NP 2 NP . NP PP 3 N . spoon 2 NP NP . PP0 NP . Papa 1 VP . VP PP 2 NP . Papa 0 S NP VP .0 Det . the 1 PP . P NP 2 Det . the 1 VP VP . PP0 Det . a 1 V . ate 2 Det . a
1 P . with
attach(again!)
0 Papa 1 ate 2 the 3 caviar 4
0 ROOT . S 0 NP Papa . 1 V ate . 2 Det the . 3 N caviar .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 . caviar 1 VP V NP .0 NP . NP PP 1 VP . V NP 2 NP . NP PP 3 N . spoon 2 NP NP . PP0 NP . Papa 1 VP . VP PP 2 NP . Papa 0 S NP VP .0 Det . the 1 PP . P NP 2 Det . the 1 VP VP . PP0 Det . a 1 V . ate 2 Det . a 4 PP . P NP
1 P . with
0 Papa 1 ate 2 the 3 caviar 4
0 ROOT . S 0 NP Papa . 1 V ate . 2 Det the . 3 N caviar .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 . caviar 1 VP V NP .0 NP . NP PP 1 VP . V NP 2 NP . NP PP 3 N . spoon 2 NP NP . PP0 NP . Papa 1 VP . VP PP 2 NP . Papa 0 S NP VP .0 Det . the 1 PP . P NP 2 Det . the 1 VP VP . PP0 Det . a 1 V . ate 2 Det . a 4 PP . P NP
1 P . with 0 ROOT S .
attach(again!)
0 Papa 1 ate 2 the 3 caviar 4
0 ROOT . S 0 NP Papa . 1 V ate . 2 Det the . 3 N caviar .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 . caviar 1 VP V NP .0 NP . NP PP 1 VP . V NP 2 NP . NP PP 3 N . spoon 2 NP NP . PP0 NP . Papa 1 VP . VP PP 2 NP . Papa 0 S NP VP .0 Det . the 1 PP . P NP 2 Det . the 1 VP VP . PP0 Det . a 1 V . ate 2 Det . a 4 PP . P NP
1 P . with 0 ROOT S .
0 Papa 1 ate 2 the 3 caviar 4
0 ROOT . S 0 NP Papa . 1 V ate . 2 Det the . 3 N caviar .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 . caviar 1 VP V NP .0 NP . NP PP 1 VP . V NP 2 NP . NP PP 3 N . spoon 2 NP NP . PP0 NP . Papa 1 VP . VP PP 2 NP . Papa 0 S NP VP .0 Det . the 1 PP . P NP 2 Det . the 1 VP VP . PP0 Det . a 1 V . ate 2 Det . a 4 PP . P NP
1 P . with 0 ROOT S .4 P . with
0 Papa 1 ate 2 the 3 caviar 4
0 ROOT . S 0 NP Papa . 1 V ate . 2 Det the . 3 N caviar .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 . caviar 1 VP V NP .0 NP . NP PP 1 VP . V NP 2 NP . NP PP 3 N . spoon 2 NP NP . PP0 NP . Papa 1 VP . VP PP 2 NP . Papa 0 S NP VP .0 Det . the 1 PP . P NP 2 Det . the 1 VP VP . PP0 Det . a 1 V . ate 2 Det . a 4 PP . P NP
1 P . with 0 ROOT S .4 P . with
0 Papa 1 ate 2 the 3 caviar 4 with 5
0 ROOT . S 0 NP Papa . 1 V ate . 2 Det the . 3 N caviar . 4 P with .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 . caviar 1 VP V NP .0 NP . NP PP 1 VP . V NP 2 NP . NP PP 3 N . spoon 2 NP NP . PP0 NP . Papa 1 VP . VP PP 2 NP . Papa 0 S NP VP .0 Det . the 1 PP . P NP 2 Det . the 1 VP VP . PP0 Det . a 1 V . ate 2 Det . a 4 PP . P NP
1 P . with 0 ROOT S .4 P . with
0 Papa 1 ate 2 the 3 caviar 4 with 5
0 ROOT . S 0 NP Papa . 1 V ate . 2 Det the . 3 N caviar . 4 P with .0 S . NP VP 0 S NP . VP 1 VP V . NP 2 NP Det . N 2 NP Det N . 4 PP P . NP0 NP . Det N 0 NP NP . PP 2 NP . Det N 3 N . caviar 1 VP V NP .0 NP . NP PP 1 VP . V NP 2 NP . NP PP 3 N . spoon 2 NP NP . PP0 NP . Papa 1 VP . VP PP 2 NP . Papa 0 S NP VP .0 Det . the 1 PP . P NP 2 Det . the 1 VP VP . PP0 Det . a 1 V . ate 2 Det . a 4 PP . P NP
1 P . with 0 ROOT S .4 P . with
0 Papa 1 ate 2 the 3 caviar 4 with 5
0 ROOT . S 0 NP Papa . 1 V ate . 2 Det the . 3 N caviar . 4 P with .0 S . NP VP 0 S NP . VP 1 VP V . NP 2 NP Det . N 2 NP Det N . 4 PP P . NP0 NP . Det N 0 NP NP . PP 2 NP . Det N 3 N . caviar 1 VP V NP . 5 NP . Det N0 NP . NP PP 1 VP . V NP 2 NP . NP PP 3 N . spoon 2 NP NP . PP 5 NP . NP PP0 NP . Papa 1 VP . VP PP 2 NP . Papa 0 S NP VP . 5 NP . Papa0 Det . the 1 PP . P NP 2 Det . the 1 VP VP . PP0 Det . a 1 V . ate 2 Det . a 4 PP . P NP
1 P . with 0 ROOT S .4 P . with
0 Papa 1 ate 2 the 3 caviar 4 with 5
0 ROOT . S 0 NP Papa . 1 V ate . 2 Det the . 3 N caviar . 4 P with .0 S . NP VP 0 S NP . VP 1 VP V . NP 2 NP Det . N 2 NP Det N . 4 PP P . NP0 NP . Det N 0 NP NP . PP 2 NP . Det N 3 N . caviar 1 VP V NP . 5 NP . Det N0 NP . NP PP 1 VP . V NP 2 NP . NP PP 3 N . spoon 2 NP NP . PP 5 NP . NP PP0 NP . Papa 1 VP . VP PP 2 NP . Papa 0 S NP VP . 5 NP . Papa0 Det . the 1 PP . P NP 2 Det . the 1 VP VP . PP 5 Det . the0 Det . a 1 V . ate 2 Det . a 4 PP . P NP 5 Det . a
1 P . with 0 ROOT S .4 P . with
0 Papa 1 ate 2 the 3 caviar 4 with 5
0 ROOT . S 0 NP Papa . 1 V ate . 2 Det the . 3 N caviar . 4 P with .0 S . NP VP 0 S NP . VP 1 VP V . NP 2 NP Det . N 2 NP Det N . 4 PP P . NP0 NP . Det N 0 NP NP . PP 2 NP . Det N 3 N . caviar 1 VP V NP . 5 NP . Det N0 NP . NP PP 1 VP . V NP 2 NP . NP PP 3 N . spoon 2 NP NP . PP 5 NP . NP PP0 NP . Papa 1 VP . VP PP 2 NP . Papa 0 S NP VP . 5 NP . Papa0 Det . the 1 PP . P NP 2 Det . the 1 VP VP . PP 5 Det . the0 Det . a 1 V . ate 2 Det . a 4 PP . P NP 5 Det . a
1 P . with 0 ROOT S .4 P . with
0 Papa 1 ate 2 the 3 caviar 4 with 5 0 ROOT . S 0 NP Papa . 1 V ate . 2 Det the . 3 N caviar . 4 P with .0 S . NP VP 0 S NP . VP 1 VP V . NP 2 NP Det . N 2 NP Det N . 4 PP P . NP0 NP . Det N 0 NP NP . PP 2 NP . Det N 3 N . caviar 1 VP V NP . 5 NP . Det N0 NP . NP PP 1 VP . V NP 2 NP . NP PP 3 N . spoon 2 NP NP . PP 5 NP . NP PP0 NP . Papa 1 VP . VP PP 2 NP . Papa 0 S NP VP . 5 NP . Papa0 Det . the 1 PP . P NP 2 Det . the 1 VP VP . PP 5 Det . the0 Det . a 1 V . ate 2 Det . a 4 PP . P NP 5 Det . a
1 P . with 0 ROOT S .4 P . with
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