Sentence Parsing Parsing 3 Dynamic Programming. Jan 2009 Speech and Language Processing - Jurafsky...
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Transcript of Sentence Parsing Parsing 3 Dynamic Programming. Jan 2009 Speech and Language Processing - Jurafsky...
Sentence Parsing
Parsing 3Dynamic Programming
Jan 2009 Speech and Language Processing - Jurafsky and Martin 2
Acknowledgement
Lecture based on Jurafsky and Martin Ch. 13 (2nd Edition) J & M Lecture Notes
Jan 2009 Speech and Language Processing - Jurafsky and Martin 3
Avoiding Repeated Work
Dynamic Programming CKY Parsing Earley Algorithm Chart Parsing
Jan 2009 Speech and Language Processing - Jurafsky and Martin 4
Dynamic Programming
DP search methods fill tables with partial results and thereby Avoid doing avoidable repeated work Solve exponential problems in polynomial time
(well, no not really) Efficiently store ambiguous structures with
shared sub-parts. We’ll cover two approaches that roughly
correspond to top-down and bottom-up approaches. CKY (after authors Cocke, Kasami and Younger) Earley (often referred to as chart parsing,
because it uses a data structure called a chart)
Jan 2009 Speech and Language Processing - Jurafsky and Martin 5
CKY Parsing
First we’ll limit our grammar to epsilon-free, binary rules (more later)
Key intuition: consider the rule A BC If there is an A somewhere in the input then
there must be a B followed by a C in the input.
If the A spans from i to j in the input then there must be some k st. i<k<j i.e. The B splits from the C someplace.
This intuition plays a role in both CKY and Earley methods.
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Problem
What if your grammar isn’t binary? As in the case of the TreeBank grammar?
Convert it to binary… any arbitrary CFG can be rewritten into Chomsky-Normal Form automatically.
What does this mean? The resulting grammar accepts (and rejects)
the same set of strings as the original grammar.
But the resulting derivations (trees) are different.
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Problem
More specifically, we want our rules to be of the formA B C
Or
A w
That is, rules can expand to either 2 non-terminals or to a single terminal.
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Binarization Intuition
Eliminate chains of unit productions. Introduce new intermediate non-
terminals into the grammar that distribute rules with length > 2 over several rules. So… S A B C turns into S X C andX A BWhere X is a symbol that doesn’t occur
anywhere else in the the grammar.
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Sample L1 Grammar
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CNF Conversion
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CKY
Build a table so that an A spanning from i to j in the input is placed in cell [i,j] in the table.
So a non-terminal spanning an entire string will sit in cell [0, n] Hopefully an S
If we build the table bottom-up, we’ll know that the parts of the A must go from i to k and from k to j, for some k.
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CKY
Meaning that for a rule like A B C we should look for a B in [i,k] and a C in [k,j].
In other words, if we think there might be an A spanning i,j in the input… AND
A B C is a rule in the grammar THEN
There must be a B in [i,k] and a C in [k,j] for some i<k<j
Jan 2009 Speech and Language Processing - Jurafsky and Martin 13
CKY
So to fill the table loop over the cell[i,j] values in some systematic way What constraint should we put on that
systematic search?
For each cell, loop over the appropriate k values to search for things to add.
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CKY Algorithm
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CKY Parsing
Is that really a parser?
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CKY Parsing
Is that really a parser? No it’s a recogniser. But the parse tree
can in principle be recovered from the table provided that each time a LH symbol A is put into the table deriving from a rule A B C, a pointer is kept to the RH instances B, C from which it was derived.
The parse tree is then reconstructed by following the pointers from the top item.
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Note
We arranged the loops to fill the table a column at a time, from left to right, bottom to top. This assures us that whenever we’re
filling a cell, the parts needed to fill it are already in the table (to the left and below)
It’s somewhat natural in that it processes the input left to right one word at a time Known as online Other ways of filling the table are possible
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Example
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Example
Filling column 5
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Example
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Example
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Example
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Example
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CKY Notes
Since it’s bottom up, CKY populates the table with a lot of phantom constituents. Segments that by themselves are
constituents but cannot really occur in the context in which they are being suggested.
To avoid this we can switch to a top-down control strategy
In addition we can add some kind of filtering that blocks constituents where they can not happen in a final analysis.
Jan 2009 Speech and Language Processing - Jurafsky and Martin 25
Earley Parsing
Allows arbitrary CFGs (not just binary ones). This requires dotted rules
Some top-down control Uses a prediction operation
Parallel top-down search Fills a table in a single sweep over
the input Table is length N+1; N is number of
words Table entries consist of dotted rules
Jan 2009 Speech and Language Processing - Jurafsky and Martin 26
Earley Parsing Dynamic Programming: solution
involves filling in table of solutions to subproblems.
Parallel Top Down Search Worst case complexity = O(N3) in
length N of sentence. Table is sometimes called a chart.
Earley parsing also called chart parsing.
Chart contains N+1 entries● book ● that ● flight ● 0 1 2 3
Jan 2009 Speech and Language Processing - Jurafsky and Martin 27
The Chart Each table entry contains a list of
states Each state represents all partial parses
that have been reached so far at that point in the sentence.
States are represented using dotted rules containing information about Rule/subtree: which rule has been used Progress: dot indicates how much of rule's
RHS has been recognised. Position: text segment to which this parse
applies
Jan 2009 Speech and Language Processing - Jurafsky and Martin 28
Examples of Dotted Rules
Initial S Rule (incomplete) S -> . VP,[0,0]
Partially recognised NP (incomplete) NP -> Det . Nominal,[1,2]
Fully recognised VP (complete)VP -> V VP .,[0,3]
These states can also be represented graphically on the chart
Jan 2009 Speech and Language Processing - Jurafsky and Martin 29
The Chart
Jan 2009 Speech and Language Processing - Jurafsky and Martin 30
Earley Algorithm
Main Algorithm: proceeds through each text position, applying one of the three operators below.
Predictor: Creates "initial states" (ie states whose RHS is completely unparsed).
Scanner: checks current input when next category to be recognised is pre-terminal.
Completer: when a state is "complete" (nothing after dot), advance all states to the left that are looking for the associated category.
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Earley Main Algorithm
Jan 2009 Speech and Language Processing - Jurafsky and Martin 32
Earley Sub Functions
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Early Algorithm – Sub Functions
Predictor(A -> alpha . B beta, [i,j])
for each B -> gamma in Grammar(B)
enqueue((B -> . gamma, [j,j]), chart[j])
Scanner(A -> alpha . B beta, [i,j])
if B in PartOfSpeech(word[j]) then
enqueue((B -> word[j], [j,j+1]), chart[j+1])
Completer(B -> gamma . , [j,k])
for each (A -> . B beta) in chart[j]
enqueue((A -> B . beta , [j,j]), chart[j])
Jan 2009 Speech and Language Processing - Jurafsky and Martin 34
Grammar
S -> NP VPS -> Aux NP VPS -> VPNP -> Det NominalNominal -> NounNominal -> Noun NominalNP -> Proper-NounVP -> VerbVP -> Verb NP
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Jan 2009 Speech and Language Processing - Jurafsky and Martin 36
Jan 2009 Speech and Language Processing - Jurafsky and Martin 37
fl
Jan 2009 Speech and Language Processing - Jurafsky and Martin 38
Retrieving Trees
To turn recogniser into a parser, representation of each state must also include information about completed states that generated its constituents
Jan 2009 Speech and Language Processing - Jurafsky and Martin 39
Jan 2009 Speech and Language Processing - Jurafsky and Martin 40
Chart[3]
↑Extra Field
Jan 2009 Speech and Language Processing - Jurafsky and Martin 41
Back to Ambiguity
Did we solve it?
Jan 2009 Speech and Language Processing - Jurafsky and Martin 42
Ambiguity
No… Both CKY and Earley will result in
multiple S structures for the [0,N] table entry.
They both efficiently store the sub-parts that are shared between multiple parses.
And they obviously avoid re-deriving those sub-parts.
But neither can tell us which one is right.