CS 388: Natural Language Processing: Semantic Role LabelingSemantic Role Labeling (SRL)Semantic RolesUse of Semantic RolesSRL and Syntactic CuesSelectional RestrictionsUse of Sectional RestrictionsSelectional Restrictions and Syntactic AmbiguitySelectional Restrictions and Word Sense DisambiguationEmpirical Methods for SRLSRL as Sequence LabelingSRL with Parse TreesSRL with Parse TreesSRL as Parse Node ClassificationFeatures for SRLParse Tree Path Feature: Example 1Parse Tree Path Feature: Example 2Slide 18Head Word Feature ExampleComplete SRL ExampleIssues in Parse Node ClassificationMore Issues in Parse Node ClassificationSRL DatasetsFrameNetFrameNet ResultsPropBankCONNL SRL Shared TaskCONLL-05 Learning ApproachesCONLL Experimental MethodBest Result from CONLL-05Issues in SRL11CS 388: Natural Language Processing:Semantic Role LabelingRaymond J. MooneyUniversity of Texas at Austin2Semantic Role Labeling(SRL)•For each clause, determine the semantic role played by each noun phrase that is an argument to the verb.agent patient source destination instrument–John drove Mary from Austin to Dallas in his Toyota Prius.–The hammer broke the window.•Also referred to a “case role analysis,” “thematic analysis,” and “shallow semantic parsing”3Semantic Roles•Origins in the linguistic notion of case (Fillmore, 1968)•A variety of semantic role labels have been proposed, common ones are:–Agent: Actor of an action–Patient: Entity affected by the action–Instrument: Tool used in performing action.–Beneficiary: Entity for whom action is performed–Source: Origin of the affected entity–Destination: Destination of the affected entity4Use of Semantic Roles•Semantic roles are useful for various tasks.•Question Answering–“Who” questions usually use Agents–“What” question usually use Patients–“How” and “with what” questions usually use Instruments–“Where” questions frequently use Sources and Destinations.–“For whom” questions usually use Beneficiaries–“To whom” questions usually use Destinations•Machine Translation Generation–Semantic roles are usually expressed using particular, distinct syntactic constructions in different languages.5SRL and Syntactic Cues •Frequently semantic role is indicated by a particular syntactic position (e.g. object of a particular preposition).–Agent: subject–Patient: direct object–Instrument: object of “with” PP–Beneficiary: object of “for” PP–Source: object of “from” PP–Destination: object of “to” PP•However, these are preferences at best:–The hammer hit the window.–The book was given to Mary by John.–John went to the movie with Mary.–John bought the car for $21K.–John went to work by bus.6Selectional Restrictions•Selectional restrictions are constraints that certain verbs place on the filler of certain semantic roles.–Agents should be animate–Beneficiaries should be animate–Instruments should be tools–Patients of “eat” should be edible–Sources and Destinations of “go” should be places.–Sources and Destinations of “give” should be animate.•Taxanomic abstraction hierarchies or ontologies (e.g. hypernym links in WordNet) can be used to determine if such constraints are met.–“John” is a “Human” which is a “Mammal” which is a “Vertebrate” which is an “Animate”7Use of Sectional Restrictions•Selectional restrictions can help rule in or out certain semantic role assignments.–“John bought the car for $21K”•Beneficiaries should be Animate•Instrument of a “buy” should be Money–“John went to the movie with Mary”•Instrument should be Inanimate–“John drove Mary to school in the van” “John drove the van to work with Mary.”•Instrument of a “drive” should be a Vehicle8Selectional Restrictions andSyntactic Ambiguity•Many syntactic ambiguities like PP attachment can be resolved using selectional restrictions.–“John ate the spaghetti with meatballs.” “John ate the spaghetti with chopsticks.”•Instruments should be tools•Patients of “eat” must be edible–“John hit the man with a dog.” “John hit the man with a hammer.” •Instruments should be tools9Selectional Restrictions andWord Sense Disambiguation•Many lexical ambiguities can be resolved using selectional restrictions.•Ambiguous nouns–“John wrote it with a pen.”•Instruments of “write” should be WritingImplements–“The bat ate the bug.”•Agents (particularly of “eat”) should be animate•Patients of “eat” should be edible•Ambiguous verbs–“John fired the secretary.” “John fired the rifle.”•Patients of DischargeWeapon should be Weapons•Patients of CeaseEmploment should be Human10Empirical Methods for SRL•Difficult to acquire all of the selectional restrictions and taxonomic knowledge needed for SRL.•Difficult to efficiently and effectively apply knowledge in an integrated fashion to simultaneously determine correct parse trees, word senses, and semantic roles.•Statistical/empirical methods can be used to automatically acquire and apply the knowledge needed for effective and efficient SRL.11SRL as Sequence Labeling•SRL can be treated as an sequence labeling problem.•For each verb, try to extract a value for each of the possible semantic roles for that verb.•Employ any of the standard sequence labeling methods–Token classification–HMMs–CRFs12SRL with Parse Trees•Parse trees help identify semantic roles through exploiting syntactic clues like “the agent is usually the subject of the verb”.•Parse tree is needed to identify the true subject.SNPsg VPsgDet N PPPrep NPplThe manby the store near the dogate the apple.“The man by the store near the dog ate an apple.”“The man” is the agent of “ate” not “the dog”.13SRL with Parse Trees •Assume that a syntactic parse is available.•For each predicate (verb), label each node in the parse tree as either not-a-role or one of the possible semantic roles.SNP VPNP PPThePrep NPwiththeV NPbitabigdoggirlboyDet A NDet A NεAdj AεDet A NεColor Code:not-a-roleagent patient source destination instrumentbeneficiary14SRL as Parse Node Classification•Treat problem as classifying parse-tree nodes.•Can use any machine-learning classification method.•Critical issue is engineering the right set