Download - FA16 11-711 lecture 13-- parsing V copy 2
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Parsing V Taylor Berg-‐Kirkpatrick – CMU
Slides: Dan Klein – UC Berkeley
Algorithms for NLP
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BinarizaEon / MarkovizaEon NP
DT JJ NN NN
v=1,h=∞
DT
NP
@NP[DT]
@NP[DT,JJ,NN] NN
JJ @NP[DT,JJ]
NN
v=1,h=0
DT
NP
JJ
@NP
@NP NN
@NP
NN
v=1,h=1
DT
NP
JJ
@NP[DT]
@NP[…,NN] NN
@NP[…,JJ]
NN
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BinarizaEon / MarkovizaEon NP
DT JJ NN NN
v=2,h=∞
DT^NP
NP^VP
JJ^NP
@NP^VP[DT]
@NP^VP[DT,JJ,NN] NN^NP
@NP^VP[DT,JJ]
NN^NP
v=2,h=0
DT^NP
NP^VP
JJ^NP
@NP
@NP NN^NP
@NP
NN^NP
v=2,h=1
DT^NP
NP^VP
JJ^NP
@NP^VP[DT]
@NP^VP[…,NN] NN^NP
@NP^VP[…,JJ]
NN^NP
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Grammar ProjecEons
NP → DT @NP
Coarse Grammar Fine Grammar
DT
NP
JJ
@NP
@NP NN
@NP
NN
DT^NP
NP^VP
JJ^NP
@NP^VP[DT]
@NP^VP[…,NN] NN^NP
@NP^VP[…,JJ]
NN^NP
NP^VP → DT^NP @NP^VP[DT]
Note: X-‐Bar Grammars are projec2ons with rules like XP → Y @X or XP → @X Y or @X → X
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Grammar ProjecEons
NP Coarse Symbols Fine Symbols
DT
@NP
NP^VP NP^S @NP^VP[DT] @NP^S[DT] @NP^VP[…,JJ] @NP^S[…,JJ] DT^NP
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Efficient Parsing for Structural AnnotaEon
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Coarse-‐to-‐Fine Pruning
… QP NP VP …
coarse:
fine:
E.g. consider the span 5 to 12:
< threshold P (X|i, j, S)
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Coarse-‐to-‐Fine Pruning
For each coarse chart item X[i,j], compute posterior probability:
… QP NP VP …
coarse:
fine:
E.g. consider the span 5 to 12:
< threshold ↵(X, i, j) · �(X, i, j)
↵(root, 0, n)
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CompuEng Marginals ↵(X, i, j) =
X
X!Y Z
X
k2(i,j)
P (X ! Y Z)↵(Y, i, k)↵(Z, k, j)
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CompuEng Marginals �(X, i, j) =
X
Y!ZX
X
k2[0,i)
P (Y ! ZX)�(Y, k, j)↵(B, k, i)
+X
Y!XZ
X
k2(j,n]
P (Y ! XZ)�(Y, i, k)↵(Z, j, k)
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Pruning with A*
§ You can also speed up the search without sacrificing opEmality
§ For agenda-‐based parsers: § Can select which items to process first
§ Can do with any “figure of merit” [Charniak 98]
§ If your figure-‐of-‐merit is a valid A* heurisEc, no loss of opEmiality [Klein and Manning 03]
X
n0 i j
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Efficient Parsing for Lexical Grammars
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Lexicalized Trees
§ Add “head words” to each phrasal node § SyntacEc vs. semanEc
heads § Headship not in (most)
treebanks § Usually use head rules,
e.g.: § NP:
§ Take lelmost NP § Take rightmost N* § Take rightmost JJ § Take right child
§ VP: § Take lelmost VB* § Take lelmost VP § Take lel child
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Lexicalized PCFGs? § Problem: we now have to esEmate probabiliEes like
§ Never going to get these atomically off of a treebank
§ SoluEon: break up derivaEon into smaller steps
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Lexical DerivaEon Steps § A derivaEon of a local tree [Collins 99]
Choose a head tag and word
Choose a complement bag
Generate children (incl. adjuncts)
Recursively derive children
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Lexicalized CKY
bestScore(X,i,j,h) if (j = i+1) return tagScore(X,s[i]) else return max max score(X[h]->Y[h] Z[h’]) * bestScore(Y,i,k,h) * bestScore(Z,k,j,h’) max score(X[h]->Y[h’] Z[h]) * bestScore(Y,i,k,h’) * bestScore(Z,k,j,h)
Y[h] Z[h’]
X[h]
i h k h’ j
k,h’,X->YZ
(VP->VBD •)[saw] NP[her]
(VP->VBD...NP •)[saw]
k,h’,X->YZ
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QuarEc Parsing § Turns out, you can do (a liple) beper [Eisner 99]
§ Gives an O(n4) algorithm § SEll prohibiEve in pracEce if not pruned
Y[h] Z[h’]
X[h]
i h k h’ j
Y[h] Z
X[h]
i h k j
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Pruning with Beams § The Collins parser prunes with per-‐
cell beams [Collins 99] § EssenEally, run the O(n5) CKY § Remember only a few hypotheses for
each span <i,j>. § If we keep K hypotheses at each span,
then we do at most O(nK2) work per span (why?)
§ Keeps things more or less cubic (and in pracEce is more like linear!)
§ Also: certain spans are forbidden enErely on the basis of punctuaEon (crucial for speed)
Y[h] Z[h’]
X[h]
i h k h’ j
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Pruning with a PCFG
§ The Charniak parser prunes using a two-‐pass, coarse-‐to-‐fine approach [Charniak 97+] § First, parse with the base grammar § For each X:[i,j] calculate P(X|i,j,s)
§ This isn’t trivial, and there are clever speed ups § Second, do the full O(n5) CKY
§ Skip any X :[i,j] which had low (say, < 0.0001) posterior § Avoids almost all work in the second phase!
§ Charniak et al 06: can use more passes § Petrov et al 07: can use many more passes
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Results
§ Some results § Collins 99 – 88.6 F1 (generaEve lexical) § Charniak and Johnson 05 – 89.7 / 91.3 F1 (generaEve lexical / reranked)
§ Petrov et al 06 – 90.7 F1 (generaEve unlexical) § McClosky et al 06 – 92.1 F1 (gen + rerank + self-‐train)
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Latent Variable PCFGs
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§ AnnotaEon refines base treebank symbols to improve staEsEcal fit of the grammar § Parent annotaEon [Johnson ’98] § Head lexicalizaEon [Collins ’99, Charniak ’00] § AutomaEc clustering?
The Game of Designing a Grammar
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Latent Variable Grammars
Parse Tree Sentence Parameters
...
Derivations
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Backward
Learning Latent AnnotaEons
EM algorithm:
X1
X2 X7 X4
X5 X6 X3
He was right
.
§ Brackets are known § Base categories are known § Only induce subcategories
Just like Forward-‐Backward for HMMs. Forward
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Refinement of the DT tag
DT
DT-1 DT-2 DT-3 DT-4
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Hierarchical refinement
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Hierarchical EsEmaEon Results
74
76
78
80
82
84
86
88
90
100 300 500 700 900 1100 1300 1500 1700
Total Number of grammar symbols
Par
sing
acc
urac
y (F
1)
Model F1 Flat Training 87.3 Hierarchical Training 88.4
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Refinement of the , tag § Spli}ng all categories equally is wasteful:
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Adaptive Splitting
§ Want to split complex categories more § Idea: split everything, roll back splits which
were least useful
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AdapEve Spli}ng Results
Model F1 Previous 88.4 With 50% Merging 89.5
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0
5
10
15
20
25
30
35
40
NP
VP PP
ADVP S
ADJP
SBAR Q
P
WH
NP
PRN
NX
SIN
V
PRT
WH
PP SQ
CO
NJP
FRAG
NAC UC
P
WH
ADVP INTJ
SBAR
Q
RR
C
WH
ADJP X
RO
OT
LST
Number of Phrasal Subcategories
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Number of Lexical Subcategories
0
10
20
30
40
50
60
70
NNP JJ
NNS NN VBN RB
VBG VB VBD CD IN
VBZ
VBP DT
NNPS CC JJ
RJJ
S :PR
PPR
P$ MD
RBR
WP
POS
PDT
WRB
-LRB
- .EX
WP$
WDT
-RRB
- ''FW RB
S TO$
UH, ``
SYM RP LS #
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Learned Splits
§ Proper Nouns (NNP):
§ Personal pronouns (PRP):
NNP-14 Oct. Nov. Sept. NNP-12 John Robert James NNP-2 J. E. L. NNP-1 Bush Noriega Peters
NNP-15 New San Wall NNP-3 York Francisco Street
PRP-0 It He I PRP-1 it he they PRP-2 it them him
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§ RelaEve adverbs (RBR):
§ Cardinal Numbers (CD):
RBR-0 further lower higher RBR-1 more less More RBR-2 earlier Earlier later
CD-7 one two Three CD-4 1989 1990 1988 CD-11 million billion trillion CD-0 1 50 100 CD-3 1 30 31 CD-9 78 58 34
Learned Splits
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Final Results (Accuracy)
≤ 40 words F1
all F1
ENG
Charniak&Johnson ‘05 (generative) 90.1 89.6
Split / Merge 90.6 90.1
GER
Dubey ‘05 76.3 -
Split / Merge 80.8 80.1
CH
N
Chiang et al. ‘02 80.0 76.6
Split / Merge 86.3 83.4
Still higher numbers from reranking / self-training methods
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Efficient Parsing for Hierarchical Grammars
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Coarse-‐to-‐Fine Inference § Example: PP apachment
?????????
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Hierarchical Pruning
… QP NP VP … coarse:
split in two: … QP1 QP2 NP1 NP2 VP1 VP2 …
… QP1 QP1 QP3 QP4 NP1 NP2 NP3 NP4 VP1 VP2 VP3 VP4 … split in four:
split in eight: … … … … … … … … … … … … … … … … …
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Bracket Posteriors
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1621 min 111 min 35 min
15 min (no search error)
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Other SyntacEc Models
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Dependency Parsing
§ Lexicalized parsers can be seen as producing dependency trees
§ Each local binary tree corresponds to an apachment in the dependency graph
questioned
lawyer witness
the the
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Dependency Parsing
§ Pure dependency parsing is only cubic [Eisner 99]
§ Some work on non-‐projec2ve dependencies § Common in, e.g. Czech parsing § Can do with MST algorithms [McDonald and Pereira 05]
Y[h] Z[h’]
X[h]
i h k h’ j
h h’
h
h k h’
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Shil-‐Reduce Parsers
§ Another way to derive a tree:
§ Parsing § No useful dynamic programming search § Can sEll use beam search [Ratnaparkhi 97]
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Tree InserEon Grammars
§ Rewrite large (possibly lexicalized) subtrees in a single step
§ Formally, a tree-‐inser2on grammar § DerivaEonal ambiguity whether subtrees were generated atomically
or composiEonally § Most probable parse is NP-‐complete
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TIG: InserEon
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Tree-‐adjoining grammars
§ Start with local trees § Can insert structure
with adjunc2on operators
§ Mildly context-‐sensiEve
§ Models long-‐distance dependencies naturally
§ … as well as other weird stuff that CFGs don’t capture well (e.g. cross-‐serial dependencies)
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TAG: Long Distance
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CCG Parsing
§ Combinatory Categorial Grammar § Fully (mono-‐)
lexicalized grammar § Categories encode
argument sequences § Very closely related
to the lambda calculus (more later)
§ Can have spurious ambiguiEes (why?)
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Empty Elements
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Empty Elements
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Empty Elements § In the PTB, three kinds of empty elements:
§ Null items (usually complemenEzers) § DislocaEon (WH-‐traces, topicalizaEon, relaEve clause and heavy NP extraposiEon)
§ Control (raising, passives, control, shared argumentaEon)
§ Need to reconstruct these (and resolve any indexaEon)
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Example: English
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Example: German
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Types of EmpEes
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A Papern-‐Matching Approach § [Johnson 02]
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Papern-‐Matching Details § Something like transformaEon-‐based learning § Extract paperns
§ Details: transiEve verb marking, auxiliaries § Details: legal subtrees
§ Rank paperns § Pruning ranking: by correct / match rate § ApplicaEon priority: by depth
§ Pre-‐order traversal § Greedy match
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Top Paperns Extracted
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Results
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SemanEc Roles
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SemanEc Role Labeling (SRL)
§ Characterize clauses as rela2ons with roles:
§ Says more than which NP is the subject (but not much more): § RelaEons like subject are syntacEc, relaEons like agent or message are
semanEc § Typical pipeline:
§ Parse, then label roles § Almost all errors locked in by parser § Really, SRL is quite a lot easier than parsing
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SRL Example
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PropBank / FrameNet
§ FrameNet: roles shared between verbs § PropBank: each verb has its own roles § PropBank more used, because it’s layered over the treebank (and so has
greater coverage, plus parses) § Note: some linguisEc theories postulate fewer roles than FrameNet (e.g.
5-‐20 total: agent, paEent, instrument, etc.)
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PropBank Example
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PropBank Example
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PropBank Example
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Shared Arguments
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Path Features
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Results
§ Features: § Path from target to filler § Filler’s syntacEc type, headword, case § Target’s idenEty § Sentence voice, etc. § Lots of other second-‐order features
§ Gold vs parsed source trees
§ SRL is fairly easy on gold trees
§ Harder on automaEc parses