semantic role labeling for arabic using kernel methods mona diab alessandro moschitti daniele pighin
TRANSCRIPT
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Semantic Role Labeling for Arabic using Kernel Methods
Mona DiabAlessandro Moschitti
Daniele Pighin
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What is SRL?
Proposition
John opened the door
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What is SRL?
Proposition
[John]Agent [opened]Predicate [the door]Theme
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What is SRL?
Proposition
[John]Agent [opened]Predicate [the door]Theme
Subject Object
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What is SRL?
Proposition
[John]Agent [opened]Predicate [the door]Theme
Subject Object
[The door]Theme [opened]Predicate
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What is SRL?
Proposition
[John]Agent [opened]Predicate [the door]Theme
Object
Subject[The door]Theme [opened]Predicate
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What is SRL?
Proposition
[John]Agent [opened]Predicate [the door]Theme
FrameNet Agent Container_portal
[The door]Theme [opened]Predicate
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What is SRL?
Proposition
[John]Agent [opened]Predicate [the door]Theme
PropBank ARG0 ARG1
[The door]Theme [opened]Predicate
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Why SRL?
• Useful for information extraction
• Useful for Question Answering
• Useful for Machine Translation?
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Our Goal
Last Sunday India to official visit Rongji Zhu the-Chinese the-Ministers president started
The Chinese Prime Minister Zho Rongji started an official visit to India last sunday
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Our Goal
Last Sunday India to official visit Rongji Zhu the-Chinese the-Ministers president started
The Chinese Prime Minister Zho Rongji started an official visit to India last Sunday
ARGM-TMP
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RoadMap
• Arabic Characteristics
• Our Approach
• Experiments & Results
• Conclusions & Future Directions
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Morphology
• Rich complex morphology– Templatic, concatenative, derivational,
inflectional• wbHsnAthm• w+b+Hsn+At+hm• and by virtue(s) their
– Verbs are marked for tense, person, gender, aspect, mood, voice
– Nominals are marked for case, number, gender, definiteness
• Orthography is underspecified for short vowels and consonant doubling (diacritics)
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Syntax
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Characteristics relevant for SRL
• Typical underspecification of short vowels masks morphological features such as case and agreement– Example:
rjl Albyt AlkbyrMan_masc the-house_masc the-big_masc
“the big man of the house” or “the man of the big house”
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Characteristics relevant for SRL
• Typical underspecification of short vowels masks morphological features such as case and agreement– Example:
rjlu Albyti AlkbyriMan_masc-Nom the-house_masc-Gen the-big_masc-Gen
the man of the big house
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Characteristics relevant for SRL
• Typical underspecification of short vowels masks morphological features such as case and agreement– Example:
rjlu Albyti AlkbyruMan_masc-Nom the-house_masc-Gen the-big_masc-Nom
the big man of the house
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Characteristics relevant for SRL
• Idafa constructions make indefinite nominals syntactically definite hence allowing for agreement, therefore better scoping– Example:
[rjlu Albyti] AlkbyruMan_masc-Nom-Def the-house_masc-Gen the-big_masc-Nom-Def
the big man of the house
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Characteristics relevant for SRL
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Characteristics relevant for SRL
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Characteristics relevant for SRL
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Characteristics relevant for SRL
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Characteristics relevant for SRL
• Passive constructions differ from English in that they can not have an explicit non-instrument underlying subject, hence only ARG1 and ARG2. ARG0 are not allowed.
– Example:qutil Emru bslAHiK qAtliK*qutl [Emru]ARG1 [bslmY]ARG0
*[Amr]ARG1 was killed [by SalmA]ARG0
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Characteristics relevant for SRL
• Passive constructions differ from English in that they can not have an explicit non-instrument underlying subject, hence only ARG1 and ARG2. ARG0 are not allowed.
– Example:qutil [Emru]ARG1 [bslAHiK qAtliK]ARG2
[Amr]ARG1 was killed [by a deadly weapon]ARG2
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Characteristics relevant for SRL
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Our Approach
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Semantic Role Labeling Steps
• Given a sentence and an associated syntactic parse
• An SRL system identifies the arguments for a given predicate
• The arguments are identified in two steps– Argument boundary detection– Argument role classification
• For the overall system we apply a heuristic for argument label conflict resolution
• one label per argument
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The Sentence
The Chinese Prime Minister Zho Rongji started an official visit to India last sunday
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The Parse Tree
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Boundary Identification
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Role Classification
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Our Approach
• Experiment with different kernels
• Experiment with Standard Features (similar to English) and rich morphological features specific to Arabic
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Different Kernels• Polynomial Kernels (1-6) with standard
features • Tree Kernels
Where Nt1 and Nt2 are the sets of nodes in t1 and t2, and Δ(.) evaluates the common substructures rooted in n1 and n2
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Argument Structure Trees (AST)
NP
D N
VP
V
delivers
a talk
S
N
Paul
in
PP
IN NP
jj
formal
N
styleArg. 1
Defined as the minimal subtree encompassing the predicate and one of its arguments
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Tree Substructure Representations
NP
D N
VP
V
delivers
a talk
NP
D N
VP
V
delivers
a
NP
D N
VP
V
delivers
NP
D N
VP
V NP
VP
V
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The overall set of AST substructures
NP
D N
a talk
NP
D N
NP
D N
a D N
a talk
NP
D N NP
D N
VP
V
delivers
a talk
V
delivers
NP
D N
VP
V
a talk
NP
D N
VP
V
NP
D N
VP
V
a
NP
D
VP
V
talk
N
a
NP
D N
VP
V
delivers
talk
NP
D N
VP
V
delivers NP
D N
VP
V
delivers
NP
VP
V NP
VP
V
delivers
talk
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Explicit feature space
zxrr
⋅
..,0)..,0,..,1, .,1,.,1,..,0,. ..,0,..,0,..,1, ..,1,..,1,..,0, 0,(=xr
• counts the number of common substructures
NP
D N
a talk
NP
D N
a
NP
D N NP
D N
VP
V
delivers
a talk
NP
D N
VP
V
a talk
NP
D N
VP
V
talk
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Standard Features• Predicate: Lemmatization of the predicate• Path: Syntactic path linking the predicate and an
argument NNNPVPVBD• Partial Path: Path feature limited to the branching of
arg• No Direction path without the traversals• Phrase type• Last and first POS of words in the arguments• Verb subcategorization frame: production expanding
the predicate parent node• Position of the argument relative to predicate• Syntactic Frame: positions of the surrounding NPs
relative to predicate
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Extended Features for Arabic
Definiteness, Number, Gender, Case, Mood, Person, Lemma (vocalized), English Gloss, Unvocalized surface
form, Vocalized Surface form
• Expanded the leaf nodes in AST with 10 attribute value pairs creating EAST
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Arabic AST
Sample AST from our example
ARG0
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Arabic AST
Sample AST from our example
ARG0
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Extended AST (EAST)
……
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Experiments & Results
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Experimental Set Up
• SemEval 2007 Task 18 data set, Pilot Arabic Propbank
• 95 most frequent verbs in ATB3v2• Gold parses, Unvowelized, Bies
reduced POS tag set (25 tags)• Num Sentences: Dev (886), Test (902),
Train (8402)• 26 role types (5 numbered ARGs)
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Experimental Set Up
• Experimented only with 350k examples
• We use the SVM-Light TK Toolkit (Moschitti, 2004, 2006) with SVM light default parameters
• Evaluation metrics of precision, recall and F measure are obtained using the CoNLL evaluator
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Boundary Detection Results
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Role Classification Results
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Overall Results
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Observations-BD
• AST and EAST don’t differ much for boundary detection
• AST+EAST+ Poly (3) gives best BD results
• AST and EAST perform significantly better than Poly (1)
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Observations – RC & SRL
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Conclusions
• Explicitly encoding the rich morphological features helps with SRL in Arabic
• Tree Kernels is indeed a feasible way of dealing with large feature spaces that are structural in nature
• Combining kernels yields better results
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Future Directions
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Thank You
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The parse tree