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Learning domain-specific Framenetsfrom texts
Roberto Basili Cristina Giannone Diego De Cao
DISPUniversity of Rome Tor Vergata, Rome, Italy
basili,giannone,[email protected]
Ontology Learning and Population WorkshopECAI 2008, Patras, July 22nd 2008
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Outline
MotivationsReuse: Frames as ontology design patterns
An Unsupervised Ontology Learning model based on Framenet
Semantic Spaces (Pado and Lapata, CL 2007) and LSALexical Semantics for LU and Frame modelingSemantic disambiguation through Wordnet
Lexical Unit Classification and semantic browsingLeave-One-Out testsSyntactic Pattern Acquisition and InterpretationAcquisition of Domain-Specific FramesA Short Demo
ConclusionsCurrent Empirical Evidence and Future Problems
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Outline
MotivationsReuse: Frames as ontology design patterns
An Unsupervised Ontology Learning model based on Framenet
Semantic Spaces (Pado and Lapata, CL 2007) and LSALexical Semantics for LU and Frame modelingSemantic disambiguation through Wordnet
Lexical Unit Classification and semantic browsingLeave-One-Out testsSyntactic Pattern Acquisition and InterpretationAcquisition of Domain-Specific FramesA Short Demo
ConclusionsCurrent Empirical Evidence and Future Problems
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Outline
MotivationsReuse: Frames as ontology design patterns
An Unsupervised Ontology Learning model based on Framenet
Semantic Spaces (Pado and Lapata, CL 2007) and LSALexical Semantics for LU and Frame modelingSemantic disambiguation through Wordnet
Lexical Unit Classification and semantic browsingLeave-One-Out testsSyntactic Pattern Acquisition and InterpretationAcquisition of Domain-Specific FramesA Short Demo
ConclusionsCurrent Empirical Evidence and Future Problems
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Outline
MotivationsReuse: Frames as ontology design patterns
An Unsupervised Ontology Learning model based on Framenet
Semantic Spaces (Pado and Lapata, CL 2007) and LSALexical Semantics for LU and Frame modelingSemantic disambiguation through Wordnet
Lexical Unit Classification and semantic browsingLeave-One-Out testsSyntactic Pattern Acquisition and InterpretationAcquisition of Domain-Specific FramesA Short Demo
ConclusionsCurrent Empirical Evidence and Future Problems
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Reuse: Frames as Ontology Design Patterns
CODeP (Conceptual (or Content) Ontology DesignPattern) are general rules for coding domain knowledge.Their reuse is ensured through specialization orcomposition operation in the modeling of new domains.
Assuming Frames as CODeP is interesting for their tightconnection with language constraints at lexical, syntaticand semantic levelMost relations in (useful) ontologies can be seen asspecialization of some frames with a cleaner design effect
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Reuse: Frames as Ontology Design Patterns
CODeP (Conceptual (or Content) Ontology DesignPattern) are general rules for coding domain knowledge.Their reuse is ensured through specialization orcomposition operation in the modeling of new domains.Assuming Frames as CODeP is interesting for their tightconnection with language constraints at lexical, syntaticand semantic level
Most relations in (useful) ontologies can be seen asspecialization of some frames with a cleaner design effect
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Reuse: Frames as Ontology Design Patterns
CODeP (Conceptual (or Content) Ontology DesignPattern) are general rules for coding domain knowledge.Their reuse is ensured through specialization orcomposition operation in the modeling of new domains.Assuming Frames as CODeP is interesting for their tightconnection with language constraints at lexical, syntaticand semantic levelMost relations in (useful) ontologies can be seen asspecialization of some frames with a cleaner design effect
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Frames as Conceptual Patterns
Frames (Fillmore, 1985) are conceptual structures modelingprototypical situations. A frame is evoked in texts through theoccurrence of its lexical units.
Frames and knowledge constraintsConceptual constraints: Frames are characterized by roles,as Frame elementsLexical constraints: (predicate) words evoke frames.Semantic constraints. Predicate arguments areselectionally constrained by a system of semantic types
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Frames as Conceptual Patterns
An example: the killing frameFrame: KILLING
A KILLER or CAUSE causes the death of the VICTIM.
KILLER John drowned Martha.VICTIM John drowned Martha.MEANS The flood exterminated the rats by cutting off access
to food.CAUSE The rockslide killed nearly half of the climbers.INSTRUMENT It’s difficult to suicide with only a pocketknife.
Fram
eE
lem
ents
Pred
icat
es
annihilate.v, annihilation.n, asphyxiate.v,assassin.n, assassinate.v,assassination.n, behead.v, beheading.n, blood-bath.n, butcher.v,butchery.n, carnage.n, crucifixion.n, crucify.v, deadly.a, decapi-tate.v, decapitation.n, destroy.v, dispatch.v, drown.v, eliminate.v,euthanasia.n, euthanize.v, . . .
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Main assumptions
Ontological Assumptions
Frames as backbones of ontologically relevant predicates in a domain.
We want to automatize the learning of domain specific-frames throughthe specialization of general Framenet predicates.
These latter act as constraints during the OL process
Geometrical models (i.e. Semantic spaces) can be used to representframe properties.
Advantages in an inductive perspective
Semantic spaces are useful for most of the involved lexical inductionsteps, such as LU and sentence classification, ...
The extracted information captures the textual realisations ofpredicates where (semantic) ambiguity and data sparseness are lesspervasive
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Main assumptions
Ontological Assumptions
Frames as backbones of ontologically relevant predicates in a domain.
We want to automatize the learning of domain specific-frames throughthe specialization of general Framenet predicates.
These latter act as constraints during the OL process
Geometrical models (i.e. Semantic spaces) can be used to representframe properties.
Advantages in an inductive perspective
Semantic spaces are useful for most of the involved lexical inductionsteps, such as LU and sentence classification, ...
The extracted information captures the textual realisations ofpredicates where (semantic) ambiguity and data sparseness are lesspervasive
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Main assumptions
Ontological Assumptions
Frames as backbones of ontologically relevant predicates in a domain.
We want to automatize the learning of domain specific-frames throughthe specialization of general Framenet predicates.
These latter act as constraints during the OL process
Geometrical models (i.e. Semantic spaces) can be used to representframe properties.
Advantages in an inductive perspective
Semantic spaces are useful for most of the involved lexical inductionsteps, such as LU and sentence classification, ...
The extracted information captures the textual realisations ofpredicates where (semantic) ambiguity and data sparseness are lesspervasive
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Main assumptions
Ontological Assumptions
Frames as backbones of ontologically relevant predicates in a domain.
We want to automatize the learning of domain specific-frames throughthe specialization of general Framenet predicates.
These latter act as constraints during the OL process
Geometrical models (i.e. Semantic spaces) can be used to representframe properties.
Advantages in an inductive perspective
Semantic spaces are useful for most of the involved lexical inductionsteps, such as LU and sentence classification, ...
The extracted information captures the textual realisations ofpredicates where (semantic) ambiguity and data sparseness are lesspervasive
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Main assumptions
Ontological Assumptions
Frames as backbones of ontologically relevant predicates in a domain.
We want to automatize the learning of domain specific-frames throughthe specialization of general Framenet predicates.
These latter act as constraints during the OL process
Geometrical models (i.e. Semantic spaces) can be used to representframe properties.
Advantages in an inductive perspective
Semantic spaces are useful for most of the involved lexical inductionsteps, such as LU and sentence classification, ...
The extracted information captures the textual realisations ofpredicates where (semantic) ambiguity and data sparseness are lesspervasive
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Main assumptions
Ontological Assumptions
Frames as backbones of ontologically relevant predicates in a domain.
We want to automatize the learning of domain specific-frames throughthe specialization of general Framenet predicates.
These latter act as constraints during the OL process
Geometrical models (i.e. Semantic spaces) can be used to representframe properties.
Advantages in an inductive perspective
Semantic spaces are useful for most of the involved lexical inductionsteps, such as LU and sentence classification, ...
The extracted information captures the textual realisations ofpredicates where (semantic) ambiguity and data sparseness are lesspervasive
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Harvesting ontologies in semantic fields
Semantic Spaces: a definitionA Semantic Space for a set of N targets is 4-tuple < B,A,S,V > where
B is the set of basic features (e.g. words co-occurring with the targets)
A is a lexical association function that weights the correlationsbetween b ∈ B and the targets
S is a similarity function between targets (i.e. in ℜ|B|×ℜ|B|)
V is a linear transformation over the original N×|B| matrix
Examples
In IR systems targets are documents, B is the term vocabulary, A is thetf · idf score. The S function is usually the cosine similarity, i.e.sim(~t1,~t2) = ∑i t1i·t2i
||~t1||·||~t2||
In Latent Semantic Analysis (Berry et al. 94) targets are documents (orwords), and the SVD transrmation is used as V
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Harvesting ontologies in semantic fields
Semantic Spaces: a definitionA Semantic Space for a set of N targets is 4-tuple < B,A,S,V > where
B is the set of basic features (e.g. words co-occurring with the targets)
A is a lexical association function that weights the correlationsbetween b ∈ B and the targets
S is a similarity function between targets (i.e. in ℜ|B|×ℜ|B|)
V is a linear transformation over the original N×|B| matrix
Examples
In IR systems targets are documents, B is the term vocabulary, A is thetf · idf score. The S function is usually the cosine similarity, i.e.sim(~t1,~t2) = ∑i t1i·t2i
||~t1||·||~t2||
In Latent Semantic Analysis (Berry et al. 94) targets are documents (orwords), and the SVD transrmation is used as V
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Harvesting ontologies in semantic fields
Semantic Spaces: a definitionA Semantic Space for a set of N targets is 4-tuple < B,A,S,V > where
B is the set of basic features (e.g. words co-occurring with the targets)
A is a lexical association function that weights the correlationsbetween b ∈ B and the targets
S is a similarity function between targets (i.e. in ℜ|B|×ℜ|B|)
V is a linear transformation over the original N×|B| matrix
Examples
In IR systems targets are documents, B is the term vocabulary, A is thetf · idf score. The S function is usually the cosine similarity, i.e.sim(~t1,~t2) = ∑i t1i·t2i
||~t1||·||~t2||
In Latent Semantic Analysis (Berry et al. 94) targets are documents (orwords), and the SVD transrmation is used as V
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Harvesting ontologies in semantic fields
Semantic Spaces: a definitionA Semantic Space for a set of N targets is 4-tuple < B,A,S,V > where
B is the set of basic features (e.g. words co-occurring with the targets)
A is a lexical association function that weights the correlationsbetween b ∈ B and the targets
S is a similarity function between targets (i.e. in ℜ|B|×ℜ|B|)
V is a linear transformation over the original N×|B| matrix
Examples
In IR systems targets are documents, B is the term vocabulary, A is thetf · idf score. The S function is usually the cosine similarity, i.e.sim(~t1,~t2) = ∑i t1i·t2i
||~t1||·||~t2||
In Latent Semantic Analysis (Berry et al. 94) targets are documents (orwords), and the SVD transrmation is used as V
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Harvesting ontologies in semantic fields
Semantic Spaces: a definitionA Semantic Space for a set of N targets is 4-tuple < B,A,S,V > where
B is the set of basic features (e.g. words co-occurring with the targets)
A is a lexical association function that weights the correlationsbetween b ∈ B and the targets
S is a similarity function between targets (i.e. in ℜ|B|×ℜ|B|)
V is a linear transformation over the original N×|B| matrix
Examples
In IR systems targets are documents, B is the term vocabulary, A is thetf · idf score. The S function is usually the cosine similarity, i.e.sim(~t1,~t2) = ∑i t1i·t2i
||~t1||·||~t2||
In Latent Semantic Analysis (Berry et al. 94) targets are documents (orwords), and the SVD transrmation is used as V
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Harvesting ontologies in semantic fields
Semantic Spaces: a definitionA Semantic Space for a set of N targets is 4-tuple < B,A,S,V > where
B is the set of basic features (e.g. words co-occurring with the targets)
A is a lexical association function that weights the correlationsbetween b ∈ B and the targets
S is a similarity function between targets (i.e. in ℜ|B|×ℜ|B|)
V is a linear transformation over the original N×|B| matrix
Examples
In IR systems targets are documents, B is the term vocabulary, A is thetf · idf score. The S function is usually the cosine similarity, i.e.sim(~t1,~t2) = ∑i t1i·t2i
||~t1||·||~t2||
In Latent Semantic Analysis (Berry et al. 94) targets are documents (orwords), and the SVD transrmation is used as V
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Harvesting ontologies in semantic fields
Semantic Spaces and Frame semantics
These lexicalized models corresponds to useful generalizations regardingsinonimy, class membership or topical similarity
As frames are rich linguistic structures it is clear that more than one ofsuch properties hold among members (i.e. LUs) of the same frame
Topical similarity plays a role as frames evoke events in very similartopical situations (e.g. KILLING vs. ARREST)
Sinonimy is also informative as LU’s in a frame can be synonyms(such as kid, child), quasi-sinonyms (such as mother vs. father) andco-hyponims
Which feature models and metrics correspond to a suitable geometricalnotion of framehood?
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Harvesting ontologies in semantic fields
Semantic Spaces and Frame semantics
These lexicalized models corresponds to useful generalizations regardingsinonimy, class membership or topical similarity
As frames are rich linguistic structures it is clear that more than one ofsuch properties hold among members (i.e. LUs) of the same frame
Topical similarity plays a role as frames evoke events in very similartopical situations (e.g. KILLING vs. ARREST)
Sinonimy is also informative as LU’s in a frame can be synonyms(such as kid, child), quasi-sinonyms (such as mother vs. father) andco-hyponims
Which feature models and metrics correspond to a suitable geometricalnotion of framehood?
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Harvesting ontologies in semantic fields
Semantic Spaces and Frame semantics
These lexicalized models corresponds to useful generalizations regardingsinonimy, class membership or topical similarity
As frames are rich linguistic structures it is clear that more than one ofsuch properties hold among members (i.e. LUs) of the same frame
Topical similarity plays a role as frames evoke events in very similartopical situations (e.g. KILLING vs. ARREST)
Sinonimy is also informative as LU’s in a frame can be synonyms(such as kid, child), quasi-sinonyms (such as mother vs. father) andco-hyponims
Which feature models and metrics correspond to a suitable geometricalnotion of framehood?
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Harvesting ontologies in semantic fields
Semantic Spaces and Frame semantics
These lexicalized models corresponds to useful generalizations regardingsinonimy, class membership or topical similarity
As frames are rich linguistic structures it is clear that more than one ofsuch properties hold among members (i.e. LUs) of the same frame
Topical similarity plays a role as frames evoke events in very similartopical situations (e.g. KILLING vs. ARREST)
Sinonimy is also informative as LU’s in a frame can be synonyms(such as kid, child), quasi-sinonyms (such as mother vs. father) andco-hyponims
Which feature models and metrics correspond to a suitable geometricalnotion of framehood?
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Latent Semantic Spaces
LSA and Frame semanticsIn our approach SVD is applied to source co-occurrence matrices in order to
Reduce the original dimensionality
Capture topical similarity latent in the original documents, i.e. secondorder relations among targets
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Latent Semantic Spaces
LSA and Frame semanticsIn our approach SVD is applied to source co-occurrence matrices in order to
Reduce the original dimensionality
Capture topical similarity latent in the original documents, i.e. secondorder relations among targets
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Latent Semantic Spaces
LSA and Frame semanticsIn our approach SVD is applied to source co-occurrence matrices in order to
Reduce the original dimensionality
Capture topical similarity latent in the original documents, i.e. secondorder relations among targets
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Latent Semantic Spaces
LSA and Frame semanticsIn our approach SVD is applied to source co-occurrence matrices in order to
Reduce the original dimensionality
Capture topical similarity latent in the original documents, i.e. secondorder relations among targets
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Latent Semantic Spaces
LSA and Frame semanticsIn our approach SVD is applied to source co-occurrence matrices in order to
Reduce the original dimensionality
Capture topical similarity latent in the original documents, i.e. secondorder relations among targets
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
LSA: semantic interpretation
LSA and PCA
SVD let the principal components of the distribution emerge
Principal components are linear combinations of the originaldimensions, i.e. pseudo concepts
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Harvesting ontologies in semantic fields
Framehood in a semantic space
Frames are rich polymorphic classes and clustering is applied formultiple centroidsRegions of the space where LU’s manifest are also useful for capturingsentences including the intended predicate semantics
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Harvesting ontologies in semantic fields
Framehood in a semantic space
Frames are rich polymorphic classes and clustering is applied formultiple centroids
Regions of the space where LU’s manifest are also useful for capturingsentences including the intended predicate semantics
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Harvesting ontologies in semantic fields
Framehood in a semantic space
Frames are rich polymorphic classes and clustering is applied formultiple centroidsRegions of the space where LU’s manifest are also useful for capturingsentences including the intended predicate semantics
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Challenges: Semantic ambiguity
Sources of Semantic Ambiguity
Word occurrences in texts
Multiply classified LUs in Framenet
Argument semantics (e.g. to kill the head vs. the leader)
Role interpretation
SolutionsLatent Semantic spaces let the most significant topical similarityemerge in the model
Semantic similarity can be computed in Wordnet given a proper notionof context
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Generalizing Syntactic Arguments throughWordnet
Task Definition
Given a lexical unit lu ∈ F, and one of its syntactic relations rDetermine the suitable generalizations α in WN able to subsume most ofthe fillers Fr of r
Lexical Fillers for the Subj of killGeneralizations CD Score Cluster of Lexical Fillers
12718325 fire, flame, flaming 1,00 blaze, fire
6859884explosion, detonation,blowup 0,25 explosion, blast
3945064 rocket 0,13 rocket, missile
5921623grammatical category,syntactic category 0,05 agent, number
914938attack, onslaught, on-set, onrush 0,05 shelling, attack, fire
7701234military unit, militaryforce, military group,force
0,04force, troop, armyunit, troop
9770195policeman, police offi-cer, officer 0,04 police officer
9424359executive, executivedirector 0,04 president, minister
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Generalizing Arguments through Wordnet
SolutionFor each dependency relation r, and the corresponding set of lexical fillersFr, the semantic similarity in Fr is computed according to the conceptualdensity metric (Basili et al., 2004).
Given Fr, a synset α in Wordnet used to generalize n different nouns w ∈ Fr,the conceptual density, cdFr(α), of α with respect to Fr is defined as:
cdFr(α) = ∑hi=0 µ i
area(α)
where h is the estimated depth of a tree able to generalize the n nouns, i.e.
h =blogµ nc iff µ 6= 1n otherwise
µ is the average branching factor in the Wordnet subhierarchy dominated byα , area(α) is the number of nodes in the α subhierarchy.
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Generalizing Arguments through Wordnet
SolutionFor each dependency relation r, and the corresponding set of lexical fillersFr, the semantic similarity in Fr is computed according to the conceptualdensity metric (Basili et al., 2004).
Given Fr, a synset α in Wordnet used to generalize n different nouns w ∈ Fr,the conceptual density, cdFr(α), of α with respect to Fr is defined as:
cdFr(α) = ∑hi=0 µ i
area(α)
where h is the estimated depth of a tree able to generalize the n nouns, i.e.
h =blogµ nc iff µ 6= 1n otherwise
µ is the average branching factor in the Wordnet subhierarchy dominated byα , area(α) is the number of nodes in the α subhierarchy.
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Generalizing Arguments through Wordnet
SolutionFor each dependency relation r, and the corresponding set of lexical fillersFr, the semantic similarity in Fr is computed according to the conceptualdensity metric (Basili et al., 2004).
Given Fr, a synset α in Wordnet used to generalize n different nouns w ∈ Fr,the conceptual density, cdFr(α), of α with respect to Fr is defined as:
cdFr(α) = ∑hi=0 µ i
area(α)
where h is the estimated depth of a tree able to generalize the n nouns, i.e.
h =blogµ nc iff µ 6= 1n otherwise
µ is the average branching factor in the Wordnet subhierarchy dominated byα , area(α) is the number of nodes in the α subhierarchy.
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
An example of CD estimation
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Semantic disambiguation and LU classification
Distributional information and WordnetThe algorithm that decides which frames better characterize a candidate(unknown) lexical unit works as follows
(Candidate) LU’s are first modeled in a semantic space
The set of known LU for a Frame F are the input of a clusterng processresulting in a set of clusters for each F
Vectors of the candidate LU l are compared with the clusters Ci andthe best K frames are retained in this way
CD is computed over the set l∪Ci and the corresponding frames areranked accordingly
The best k of this ranking are suggested as possible frames for l
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Semantic disambiguation and LU classification
Distributional information and WordnetThe algorithm that decides which frames better characterize a candidate(unknown) lexical unit works as follows
(Candidate) LU’s are first modeled in a semantic space
The set of known LU for a Frame F are the input of a clusterng processresulting in a set of clusters for each F
Vectors of the candidate LU l are compared with the clusters Ci andthe best K frames are retained in this way
CD is computed over the set l∪Ci and the corresponding frames areranked accordingly
The best k of this ranking are suggested as possible frames for l
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Semantic disambiguation and LU classification
Distributional information and WordnetThe algorithm that decides which frames better characterize a candidate(unknown) lexical unit works as follows
(Candidate) LU’s are first modeled in a semantic space
The set of known LU for a Frame F are the input of a clusterng processresulting in a set of clusters for each F
Vectors of the candidate LU l are compared with the clusters Ci andthe best K frames are retained in this way
CD is computed over the set l∪Ci and the corresponding frames areranked accordingly
The best k of this ranking are suggested as possible frames for l
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Semantic disambiguation and LU classification
Distributional information and WordnetThe algorithm that decides which frames better characterize a candidate(unknown) lexical unit works as follows
(Candidate) LU’s are first modeled in a semantic space
The set of known LU for a Frame F are the input of a clusterng processresulting in a set of clusters for each F
Vectors of the candidate LU l are compared with the clusters Ci andthe best K frames are retained in this way
CD is computed over the set l∪Ci and the corresponding frames areranked accordingly
The best k of this ranking are suggested as possible frames for l
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
The FOLIE approach
The processing cascade
LU classification
Geometrical modeling in the word-based LSA space
Clustering applied as a model of polymorphic framesSynonimy based distanceClassification (in a k-NN perspective)
Extraction of Syntactic Collocations and Acquisition ofSyntactic Patterns
Argument Generalization through WN
Argument Interpretation via FE’s
Compilation of Domain-specific Frames
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
The FOLIE approach
The processing cascade
LU classification
Geometrical modeling in the word-based LSA spaceClustering applied as a model of polymorphic frames
Synonimy based distanceClassification (in a k-NN perspective)
Extraction of Syntactic Collocations and Acquisition ofSyntactic Patterns
Argument Generalization through WN
Argument Interpretation via FE’s
Compilation of Domain-specific Frames
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
The FOLIE approach
The processing cascade
LU classification
Geometrical modeling in the word-based LSA spaceClustering applied as a model of polymorphic framesSynonimy based distance
Classification (in a k-NN perspective)
Extraction of Syntactic Collocations and Acquisition ofSyntactic Patterns
Argument Generalization through WN
Argument Interpretation via FE’s
Compilation of Domain-specific Frames
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
The FOLIE approach
The processing cascade
LU classification
Geometrical modeling in the word-based LSA spaceClustering applied as a model of polymorphic framesSynonimy based distanceClassification (in a k-NN perspective)
Extraction of Syntactic Collocations and Acquisition ofSyntactic Patterns
Argument Generalization through WN
Argument Interpretation via FE’s
Compilation of Domain-specific Frames
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
The FOLIE approach
The processing cascade
LU classification
Geometrical modeling in the word-based LSA spaceClustering applied as a model of polymorphic framesSynonimy based distanceClassification (in a k-NN perspective)
Extraction of Syntactic Collocations and Acquisition ofSyntactic Patterns
Argument Generalization through WN
Argument Interpretation via FE’s
Compilation of Domain-specific Frames
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
The FOLIE approach
The processing cascade
LU classification
Geometrical modeling in the word-based LSA spaceClustering applied as a model of polymorphic framesSynonimy based distanceClassification (in a k-NN perspective)
Extraction of Syntactic Collocations and Acquisition ofSyntactic Patterns
Argument Generalization through WN
Argument Interpretation via FE’s
Compilation of Domain-specific Frames
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
The FOLIE approach
The processing cascade
LU classification
Geometrical modeling in the word-based LSA spaceClustering applied as a model of polymorphic framesSynonimy based distanceClassification (in a k-NN perspective)
Extraction of Syntactic Collocations and Acquisition ofSyntactic Patterns
Argument Generalization through WN
Argument Interpretation via FE’s
Compilation of Domain-specific Frames
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
The FOLIE approach
The processing cascade
LU classification
Geometrical modeling in the word-based LSA spaceClustering applied as a model of polymorphic framesSynonimy based distanceClassification (in a k-NN perspective)
Extraction of Syntactic Collocations and Acquisition ofSyntactic Patterns
Argument Generalization through WN
Argument Interpretation via FE’s
Compilation of Domain-specific Frames
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
The overall architecture
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Current experimental set-up
The Corpus
TREC 2005 vol. 2
# of docs: about 230,000
# of tokens: about 110,000,000 (more than 70,000 types)
Source Dimensionality: 230,000 × 49,000
LSA Dimensionality reduction: 7,700 × 100
Syntactic and Semantic Analysis
Parsing: Minpar (Lin,1998)
Synonimy, hyponimy info: Wordnet 1.7
Semantic Similarity Estimation: CD library (Basili et al„ 2004)
Framenet: 2.0 version
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Current experimental set-up
The Corpus
TREC 2005 vol. 2
# of docs: about 230,000
# of tokens: about 110,000,000 (more than 70,000 types)
Source Dimensionality: 230,000 × 49,000
LSA Dimensionality reduction: 7,700 × 100
Syntactic and Semantic Analysis
Parsing: Minpar (Lin,1998)
Synonimy, hyponimy info: Wordnet 1.7
Semantic Similarity Estimation: CD library (Basili et al„ 2004)
Framenet: 2.0 version
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
LU CLassification: the LOO Test
The experiment set-up
English Number of frames: 701Number of LUs: 8462(nouns: 3524) (verbs: 3591) (adjectives: 1347)Most likely frames: Self_Motion (p=0.015),Clothing (p=0.014)
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
LOO Test: Results
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
LU Classification Results
Outcomes
A geometrial model capturing topical similarity establish significantevidence for classyfing novel LU’s not yet covered by Framenet
The role of lexical synonimy helps in improving the distributionalmodel at the expense of a slight reduction in coverage
The flexiblity of the model allow to apply it also to non-EnglishFramenets, such as the Italian, as Wordnet covers more languages
Future Work
Test different distributional models, eg. syntactic ones (Pado&Lapata,2007),for more robust classification
Devise more complex linear transoformation methods, better suited forlocality properties (e.g LPP in (Basili et al., TIR 08))
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
The overall architecture
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Generalizing Arguments through Wordnet
Task Definition
Given a lexical unit lu ∈ F, and one of its syntactic relations rDetermine the suitable generalizations α in WN able to subsume most ofthe fillers Fr of r
Lexical Fillers for the Subj of killGeneralizations CD Score Cluster of Lexical Fillers
12718325 fire, flame, flaming 1,00 blaze, fire
6859884explosion, detonation,blowup 0,25 explosion, blast
3945064 rocket 0,13 rocket, missile
5921623grammatical category,syntactic category 0,05 agent, number
914938attack, onslaught, on-set, onrush 0,05 shelling, attack, fire
7701234military unit, militaryforce, military group,force
0,04force, troop, armyunit, troop
9770195policeman, police offi-cer, officer 0,04 police officer
9424359executive, executivedirector 0,04 president, minister
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Generalizing Arguments through Wordnet
SolutionFor each dependency relation r, and the corresponding set of lexical fillersFr, the semantic similarity in Fr is computed according to the conceptualdensity metric (Basili et al., 2004).
Given Fr, a synset α in Wordnet used to generalize n different nouns w ∈ Fr,the conceptual density, cdFr(α), of α with respect to Fr is defined as:
cdFr(α) = ∑hi=0 µ i
area(α)
where h is the estimated depth of a tree able to generalize the n nouns, i.e.
h =blogµ nc iff µ 6= 1n otherwise
µ is the average branching factor in the Wordnet subhierarchy dominated byα , area(α) is the number of nodes in the α subhierarchy.
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
An example of CD estimation
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Argument interpretation
Task Definition
Given a lexical unit lu ∈ F, one of its syntactic relations r and ageneralization α in WN,determine the suitable frame element FE for (lu,r,α) in F.
SolutionFor each pair (r, α) in a pattern, its semantic similarity with respect to everyFE is computed.
The similarity is established between the set of the fillers F(r,α) AND thelexical representatives ΩFE of FE, such as its label, its semantic typeconstraint or the nominal head of its definition
For all FE’s, conceptual density scores over ΩFE are computed and theargmax is returned, i.e.
FE(r,α) = argmaxFEmaxl∈ΩFEcd(l∪F(r,α).
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Frames as Conceptual Patterns
An example: the killing frameFrame: KILLING
A KILLER or CAUSE causes the death of the VICTIM.
CAUSE The rockslide killed nearly half of the climbers.INSTRUMENT It’s difficult to suicide with only a pocketknife.KILLER John drowned Martha.MEANS The flood exterminated the rats by cutting off access
to food.MEDIUM John drowned Martha.
Fram
eE
lem
ents
Pred
icat
es
annihilate.v, annihilation.n, asphyxiate.v,assassin.n, assassinate.v,assassination.n, behead.v, beheading.n, blood-bath.n, butcher.v,butchery.n, carnage.n, crucifixion.n, crucify.v, deadly.a, decapi-tate.v, decapitation.n, destroy.v, dispatch.v, drown.v, eliminate.v,euthanasia.n, euthanize.v, . . .
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Argument interpretation: direct objects vs.VICTIM
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Argument interpretation: direct objects vs.CAUSE
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Acquisition of Semantic Patterns
The local detection of FE mappings for every dependency relation in apattern supports the induction of its global interpretation
Semantic Patterns Induction: a definition
Given a lexical unit lu ∈ F, a pattern made of all the syntactic relations r andcorresponding generalization α in WN, and FE’sDetermine the suitable frame structure, made of roles and semantic typeconstraints.
SolutionSolve the joint model over the preferences for dependencies (r), Wn synsets(α) and FE assignments (fα ) of a given syntactic pattern p, i.e.
SemPatt(lu,p) = argmax(r,α) ∏r∈p
σ1(α|r) ·σ2(fα |(r,α))
where σi are confidence measures over the individual inductive steps
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Acquisition of Semantic Patterns
The local detection of FE mappings for every dependency relation in apattern supports the induction of its global interpretation
Semantic Patterns Induction: a definition
Given a lexical unit lu ∈ F, a pattern made of all the syntactic relations r andcorresponding generalization α in WN, and FE’s
Determine the suitable frame structure, made of roles and semantic typeconstraints.
SolutionSolve the joint model over the preferences for dependencies (r), Wn synsets(α) and FE assignments (fα ) of a given syntactic pattern p, i.e.
SemPatt(lu,p) = argmax(r,α) ∏r∈p
σ1(α|r) ·σ2(fα |(r,α))
where σi are confidence measures over the individual inductive steps
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Acquisition of Semantic Patterns
The local detection of FE mappings for every dependency relation in apattern supports the induction of its global interpretation
Semantic Patterns Induction: a definition
Given a lexical unit lu ∈ F, a pattern made of all the syntactic relations r andcorresponding generalization α in WN, and FE’sDetermine the suitable frame structure, made of roles and semantic typeconstraints.
SolutionSolve the joint model over the preferences for dependencies (r), Wn synsets(α) and FE assignments (fα ) of a given syntactic pattern p, i.e.
SemPatt(lu,p) = argmax(r,α) ∏r∈p
σ1(α|r) ·σ2(fα |(r,α))
where σi are confidence measures over the individual inductive steps
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Acquisition of Semantic Patterns
The local detection of FE mappings for every dependency relation in apattern supports the induction of its global interpretation
Semantic Patterns Induction: a definition
Given a lexical unit lu ∈ F, a pattern made of all the syntactic relations r andcorresponding generalization α in WN, and FE’sDetermine the suitable frame structure, made of roles and semantic typeconstraints.
SolutionSolve the joint model over the preferences for dependencies (r), Wn synsets(α) and FE assignments (fα ) of a given syntactic pattern p, i.e.
SemPatt(lu,p) = argmax(r,α) ∏r∈p
σ1(α|r) ·σ2(fα |(r,α))
where σi are confidence measures over the individual inductive steps
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Acquisition of Semantic Patterns
The local detection of FE mappings for every dependency relation in apattern supports the induction of its global interpretation
Semantic Patterns Induction: a definition
Given a lexical unit lu ∈ F, a pattern made of all the syntactic relations r andcorresponding generalization α in WN, and FE’sDetermine the suitable frame structure, made of roles and semantic typeconstraints.
SolutionSolve the joint model over the preferences for dependencies (r), Wn synsets(α) and FE assignments (fα ) of a given syntactic pattern p, i.e.
SemPatt(lu,p) = argmax(r,α) ∏r∈p
σ1(α|r) ·σ2(fα |(r,α))
where σi are confidence measures over the individual inductive steps
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
An example of multiple semantic interpretations
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Compiling Domain-specific events in OWL
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
The FOLIE system on-line
... a quick tour in the demo
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Conclusions
FOLIE is a system for the acquisition of large scaleframenets from domain corpora
It embodies and makes usable several ideas from (a largebody of) machine learning and LA literature
Advanced kernels (i.e. Latent Semantic spaces) forpredicate detection and classificationSentence collection/retrieval through the duality propertiesof the LSA modelingLexical Pattern acquisition (dates back to(Grishman&Sterling,COLING92) or (Basili et al, ANLP92) )Unsupervised Semantic disambiguation over Wordnetthrough syntactic constraints and topological (n-ary)measures
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Conclusions
FOLIE is a system for the acquisition of large scaleframenets from domain corporaIt embodies and makes usable several ideas from (a largebody of) machine learning and LA literature
Advanced kernels (i.e. Latent Semantic spaces) forpredicate detection and classification
Sentence collection/retrieval through the duality propertiesof the LSA modelingLexical Pattern acquisition (dates back to(Grishman&Sterling,COLING92) or (Basili et al, ANLP92) )Unsupervised Semantic disambiguation over Wordnetthrough syntactic constraints and topological (n-ary)measures
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Conclusions
FOLIE is a system for the acquisition of large scaleframenets from domain corporaIt embodies and makes usable several ideas from (a largebody of) machine learning and LA literature
Advanced kernels (i.e. Latent Semantic spaces) forpredicate detection and classificationSentence collection/retrieval through the duality propertiesof the LSA modeling
Lexical Pattern acquisition (dates back to(Grishman&Sterling,COLING92) or (Basili et al, ANLP92) )Unsupervised Semantic disambiguation over Wordnetthrough syntactic constraints and topological (n-ary)measures
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Conclusions
FOLIE is a system for the acquisition of large scaleframenets from domain corporaIt embodies and makes usable several ideas from (a largebody of) machine learning and LA literature
Advanced kernels (i.e. Latent Semantic spaces) forpredicate detection and classificationSentence collection/retrieval through the duality propertiesof the LSA modelingLexical Pattern acquisition (dates back to(Grishman&Sterling,COLING92) or (Basili et al, ANLP92) )
Unsupervised Semantic disambiguation over Wordnetthrough syntactic constraints and topological (n-ary)measures
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Conclusions
FOLIE is a system for the acquisition of large scaleframenets from domain corporaIt embodies and makes usable several ideas from (a largebody of) machine learning and LA literature
Advanced kernels (i.e. Latent Semantic spaces) forpredicate detection and classificationSentence collection/retrieval through the duality propertiesof the LSA modelingLexical Pattern acquisition (dates back to(Grishman&Sterling,COLING92) or (Basili et al, ANLP92) )Unsupervised Semantic disambiguation over Wordnetthrough syntactic constraints and topological (n-ary)measures
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Conclusions
Novel Issues in FOLIELexical Semantics is used in FOLIE to drive the LUclassification task, as in Detour
Semantic disambiguation of syntactic arguments is drivenby the Wordnet topology, via the CD estimates (Basili et al., 2004)
Conceptual density is also used to suggest theinterpretations of predicate arguments in term ofsemantically similar Frame ElementsThe process thus relies on a combination of distributionaland paradigmatic information that tries to optimze theoverall evidence available without manual taggingLexicalized processes are more readable and usable by theuser
Missing thingsEvaluation: any suggestion?Application to Question AnsweringAdd some supervision through Kernel based classifiers
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Conclusions
Novel Issues in FOLIELexical Semantics is used in FOLIE to drive the LUclassification task, as in DetourSemantic disambiguation of syntactic arguments is drivenby the Wordnet topology, via the CD estimates (Basili et al., 2004)
Conceptual density is also used to suggest theinterpretations of predicate arguments in term ofsemantically similar Frame ElementsThe process thus relies on a combination of distributionaland paradigmatic information that tries to optimze theoverall evidence available without manual taggingLexicalized processes are more readable and usable by theuser
Missing thingsEvaluation: any suggestion?Application to Question AnsweringAdd some supervision through Kernel based classifiers
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Conclusions
Novel Issues in FOLIELexical Semantics is used in FOLIE to drive the LUclassification task, as in DetourSemantic disambiguation of syntactic arguments is drivenby the Wordnet topology, via the CD estimates (Basili et al., 2004)
Conceptual density is also used to suggest theinterpretations of predicate arguments in term ofsemantically similar Frame Elements
The process thus relies on a combination of distributionaland paradigmatic information that tries to optimze theoverall evidence available without manual taggingLexicalized processes are more readable and usable by theuser
Missing thingsEvaluation: any suggestion?Application to Question AnsweringAdd some supervision through Kernel based classifiers
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Conclusions
Novel Issues in FOLIELexical Semantics is used in FOLIE to drive the LUclassification task, as in DetourSemantic disambiguation of syntactic arguments is drivenby the Wordnet topology, via the CD estimates (Basili et al., 2004)
Conceptual density is also used to suggest theinterpretations of predicate arguments in term ofsemantically similar Frame ElementsThe process thus relies on a combination of distributionaland paradigmatic information that tries to optimze theoverall evidence available without manual taggingLexicalized processes are more readable and usable by theuser
Missing thingsEvaluation: any suggestion?
Application to Question AnsweringAdd some supervision through Kernel based classifiers
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Conclusions
Novel Issues in FOLIELexical Semantics is used in FOLIE to drive the LUclassification task, as in DetourSemantic disambiguation of syntactic arguments is drivenby the Wordnet topology, via the CD estimates (Basili et al., 2004)
Conceptual density is also used to suggest theinterpretations of predicate arguments in term ofsemantically similar Frame ElementsThe process thus relies on a combination of distributionaland paradigmatic information that tries to optimze theoverall evidence available without manual taggingLexicalized processes are more readable and usable by theuser
Missing thingsEvaluation: any suggestion?Application to Question Answering
Add some supervision through Kernel based classifiers
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Conclusions
Novel Issues in FOLIELexical Semantics is used in FOLIE to drive the LUclassification task, as in DetourSemantic disambiguation of syntactic arguments is drivenby the Wordnet topology, via the CD estimates (Basili et al., 2004)
Conceptual density is also used to suggest theinterpretations of predicate arguments in term ofsemantically similar Frame ElementsThe process thus relies on a combination of distributionaland paradigmatic information that tries to optimze theoverall evidence available without manual taggingLexicalized processes are more readable and usable by theuser
Missing thingsEvaluation: any suggestion?Application to Question AnsweringAdd some supervision through Kernel based classifiers
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Conclusions
Novel Issues in FOLIELexical Semantics is used in FOLIE to drive the LUclassification task, as in DetourSemantic disambiguation of syntactic arguments is drivenby the Wordnet topology, via the CD estimates (Basili et al., 2004)
Conceptual density is also used to suggest theinterpretations of predicate arguments in term ofsemantically similar Frame ElementsThe process thus relies on a combination of distributionaland paradigmatic information that tries to optimze theoverall evidence available without manual taggingLexicalized processes are more readable and usable by theuser
Missing thingsEvaluation: any suggestion?Application to Question AnsweringAdd some supervision through Kernel based classifiers
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Future Work
Modeling
Modeling predicate argument interpretation also via distibutionalmodels, as suggested in the ISP research of Hovy and colleagues onparaphrase patterns (see (Basili et al., RANLP 07)
Improving the ribustness of the model via probabilistic interpretationso fthe joint model for semantic patern acquisitionModel in OWL the grammatical knowledge acquired during theprocess as side effect of the acquisition process
Applications to non-English langauges
Apply FOLIE in the development of non-English FramenetsExploit the multilingual settings available for Wordnet to compilelarge scale LU repositoriesUse non-English corpora by parsing and inducing lexicalized domainspecific patterns (see (Basili et al, STEP 2008 forthcoming))
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
Future Work
Modeling
Modeling predicate argument interpretation also via distibutionalmodels, as suggested in the ISP research of Hovy and colleagues onparaphrase patterns (see (Basili et al., RANLP 07)
Improving the ribustness of the model via probabilistic interpretationso fthe joint model for semantic patern acquisitionModel in OWL the grammatical knowledge acquired during theprocess as side effect of the acquisition process
Applications to non-English langauges
Apply FOLIE in the development of non-English FramenetsExploit the multilingual settings available for Wordnet to compilelarge scale LU repositoriesUse non-English corpora by parsing and inducing lexicalized domainspecific patterns (see (Basili et al, STEP 2008 forthcoming))
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
References
Semantic Spaces
R. Basili, P. Marocco and D. Milizia, Semantically rich spaces fordocument clustering, Proceedings of the DEXA Workshop onText-based Information Retrieval, Torino, September 2008.Marco Pennacchiotti, Diego De Cao, Paolo Marocco, Roberto Basili,Towards a Vector Space Model for FrameNet-like Resources,Proceedings of the LREC Conference 2008, May 2008, Marrakesh,Morocco.Diego De Cao, Danilo Croce, Marco Pennacchiotti, Roberto Basili,Combining word sense and usage for modeling frame semantics,Proceedings of the Symposium On Semantics In Systems For TextProcessing (STEP 08), September 22-24, 2008 - Venice, Italy.Marco Pennacchiotti, Diego De Cao, Roberto Basili, Danilo Croce,Michael Roth, Automatic induction of FrameNet lexical units,Proceedings of the Int. Conference on EMNLP, Hawaii, USA,October, 2008.
Overview Motivation The OL approach The FOLIE system Conclusions and Future Works References
References (2)
Development of a Framenet-based Ontology
Roberto Basili, Cristina Giannone, Diego De Cao, Learningdomain-specific framenets from texts, Proceedings of the ECAI WS onOntology Learning and Population, Patras, Greece, 2008.Roberto Basili, Cristina Giannone, Diego De Cao, Language-drivenontology learning, (System Demo Paper) in Proceedings of EKAW,2008, Aci Trezza, Sicily, September 2008.
Kernel-based CoNLL
Basili R., Marco Cammisa, Alessandro Moschitti. (2006). A SemanticKernel to classify texts with very few training examples. Informatica.ISSN: 0350-5596.Alessandro Moschitti, Daniele Pighin and Roberto Basili. Tree Kernelsfor Semantic Role Labeling, Special Issue on Semantic Role Labeling,Computational Linguistics Journal. MIT Press for ACL. June 2008,Vol. 34, No. 2, Pages 145-159.
