INF5830 Introduction to Semantic Role Labeling · INF5830 Introduction to Semantic Role Labeling...

INF5830Introduction to Semantic Role Labeling

Andrey Kutuzov

University of OsloLanguage Technology Group

With thanks to Lilja Øvrelid, Martha Palmer and Dan Jurafsky

INF5830 Introduction to Semantic Role Labeling 1(36)

Semantic Role Labeling


Semantic Role Labeling


Introduction

Contents

Introduction

Semantic roles in general

PropBank: Proto-roles

FrameNet: Frame Semantics

Summary


Introduction

Semantics

I Study of meaning, expressed in language;

I Morphemes, words, phrases, sentences;

I Lexical semantics;

I Sentence semantics;

I (Pragmatics: how the context affects meaning).


Introduction

Semantics







Introduction

Semantics







Introduction

Semantics







Introduction

Semantics







Introduction

SemanticsI Linguistic knowledge: meaning

I Meaningful or not:I Word – flick vs blickI Sentence – John swims vs John metaphorically every

I Several meanings (WSD):I Words – fishI Sentence – John saw the man with the binoculars

I Same meaning (semantic similarity):I Word – sofa vs couchI Sentence – John gave Hannah a gift vs John gave a gift to

HannahI Truth conditions:

I All kings are maleI Molybdenum conducts electricity

I Entailment:I Alfred murdered the librarianI The librarian is dead

I Participant roles: John is the ‘giver’, Hannah is the ‘receiver’


Introduction










Introduction










Introduction





Hannah

I Truth conditions:I All kings are maleI Molybdenum conducts electricity




Introduction










Introduction










Introduction










Introduction

Representing events

I We want to understand the event described by thesesentences:

1. IBM bought Spark2. IBM acquired Spark3. Spark was acquired by IBM4. The owners of Spark sold it to IBM

I Dependency parsing is insufficient. UDPipe will give us simplerelations between verbs and arguments:

1. (buy, nsubj, IBM), (buy, obj, Spark)

2. (acquire, nsubj, IBM), (acquire, obj, Spark)

3. (acquire, nsubj :pass, Spark), (acquire, obl, IBM)

4. (sold, nsubj, owners), (sold, obj, it), (sold,

obl, IBM), (owners, nmod, Spark), . . .

I For dialogue agents, question-answering system, machinetranslation etc. we often need deeper representations.


Introduction

Representing events











Introduction

Representing events











Introduction

Representing events











Introduction

Representing events











Introduction

Representing events











Introduction

Representing events











Introduction

Semantic rolesI Semantic roles: alternative sentence-level representation of

semantic content.

I Generalization over different surface forms of predicatearguments.

I Who did what to whom, where when and how?I Intermediate between parsing and full semantics.I Predicate of a clause determines the main event, e.g. ‘eat’,

‘break’, ‘kiss’.I Semantic roles describe participants in the event.

I AGENT (who eats?)I PATIENT (what is broken?)I etc.

I Semantic role labeling is the task of assigning these roles tosentence parts (for example, words).

I Often preceded by parsing.


Introduction


semantic content.I Generalization over different surface forms of predicate

arguments.

I Who did what to whom, where when and how?I Intermediate between parsing and full semantics.I Predicate of a clause determines the main event, e.g. ‘eat’,






Introduction



arguments.I Who did what to whom, where when and how?

I Intermediate between parsing and full semantics.I Predicate of a clause determines the main event, e.g. ‘eat’,






Introduction



arguments.I Who did what to whom, where when and how?I Intermediate between parsing and full semantics.

I Predicate of a clause determines the main event, e.g. ‘eat’,‘break’, ‘kiss’.

I Semantic roles describe participants in the event.I AGENT (who eats?)I PATIENT (what is broken?)I etc.




Introduction



arguments.I Who did what to whom, where when and how?I Intermediate between parsing and full semantics.I Predicate of a clause determines the main event, e.g. ‘eat’,

‘break’, ‘kiss’.

I Semantic roles describe participants in the event.I AGENT (who eats?)I PATIENT (what is broken?)I etc.




Introduction









Introduction





I AGENT (who eats?)

I PATIENT (what is broken?)I etc.




Introduction









Introduction









Introduction









Introduction

Argument structure

I Verbs differ in their argument structure: number and types ofarguments they can take:

I find, hit, chase (how many arguments?)

I dance, sleep (how many arguments?)

I Argument structure of a verb (thematic grid) is part of itsmeaning.

I Verbs also limit semantic properties of arguments (selectionalrestrictions)

I *Colorless green ideas sleep furiously


Introduction

Argument structure


I find, hit, chase (how many arguments?)I dance, sleep (how many arguments?)





Introduction

Argument structure







Introduction

Argument structure







Introduction

Argument structure

I Components of verb meaning influence the choice ofarguments

I John threw/tossed/kicked/flung the boy the ballI *John pushed/pulled/lifted/hauled the boy the ball

I Mary faxed/radioed/emailed/phoned Helen the newsI *Mary murmured/mumbled/muttered/shrieked Helen the news

I verbs of motion: single quick motion vs. extended use of force

I verbs of communications: external apparatus vs. type of voice


Introduction

Argument structure







Introduction

Argument structure







Introduction

Argument structure







Introduction

Mismatches between syntax and semantics

I Semantic structure does not directly mirror syntacticstructure.

I Many phenomena affect mapping of syntactic to semanticarguments.

I PassiveI The dog chased the catI The cat was chased by the dogI The cat was chased

I Impersonal passivesI Det ble danset hele natta (Norwegian)I Íî ñâèñòíóòî î÷åíü ñðåäíå (Russian)

I Dative shiftI John gave the book to MaryI John gave Mary the book

I ...


Introduction







I ...


Introduction




I PassiveI The dog chased the cat

I The cat was chased by the dogI The cat was chased



I ...


Introduction




I PassiveI The dog chased the catI The cat was chased by the dog

I The cat was chased



I ...


Introduction







I ...


Introduction





I Impersonal passives

I Det ble danset hele natta (Norwegian)I Íî ñâèñòíóòî î÷åíü ñðåäíå (Russian)


I ...


Introduction







I ...


Introduction






I Dative shiftI John gave the book to Mary

I John gave Mary the book

I ...


Introduction







I ...


Introduction


I Goal: to compute the meaning of a sentence.

I There are regularities in mapping between syntax andsemantics...

I ...but not a one-to-one correspondence between syntactic andsemantic arguments.

I So what are these semantic arguments?


Introduction







Introduction







Introduction








Contents

Introduction




Summary



Semantic (thematic) roles

I Introduced in generative grammar mid-1960s and early 70s[Fillmore 1968, Jackendoff 1972].

I Classify arguments of predicates into a set of participant types.I Describe the semantic relation between the arguments of the

verb and the situation described by the verb:I The boy threw the red ball to the girl

I The boy – the participant responsible for the action, the ‘doer ’I the red ball –the affected entity, ‘undergoer ’I the girl – endpoint in a change of location.





I Classify arguments of predicates into a set of participant types.

I Describe the semantic relation between the arguments of theverb and the situation described by the verb:

I The boy threw the red ball to the girlI The boy – the participant responsible for the action, the ‘doer ’I the red ball –the affected entity, ‘undergoer ’I the girl – endpoint in a change of location.






verb and the situation described by the verb:

I The boy threw the red ball to the girlI The boy – the participant responsible for the action, the ‘doer ’I the red ball –the affected entity, ‘undergoer ’I the girl – endpoint in a change of location.














I The boy – the participant responsible for the action, the ‘doer ’

I the red ball –the affected entity, ‘undergoer ’I the girl – endpoint in a change of location.







I The boy – the participant responsible for the action, the ‘doer ’I the red ball –the affected entity, ‘undergoer ’

I the girl – endpoint in a change of location.










Role types

I AGENT: the participant that initiates the action, capable ofacting with ‘volition’

I David cooked the meatI The fox jumped out of the ditch

I PATIENT: the entity undergoing the effect of some actionI Edna cut back these bushesI The sun melted the ice

I THEME: the (inanimate) entity which is moved by an action,or whose location is described

I David passed the ball wideI The book is in the library

I EXPERIENCER: the entity which is aware of the action orstate described by predicate, but which is not in control

I Edna felt illI David saw the smoke



Role types










Role types










Role types










Role types (continued)

I BENEFICIARY: the entity for whose benefit the action wasperformed

I David filled in the form for his grandmotherI Jane baked me a cake

I INSTRUMENT: the means by which an action is performed orsomething comes about

I She cleaned the wound with an antiseptic wipeI They signed the treaty with the same pen

I GOAL: the entity towards which something movesI Edna handed her licence to the policemanI Fia told the joke to her friends

I SOURCE: the entity from which something movesI The plane came back from KinshasaI We got the idea from a magazine































I The initial example:The boy threw the red ball to the girlAGENT THEME GOAL

I Tests for semantic rolesI AGENT: add on purpose

I Jon took the book on purpose

I THEME/PATIENTI What happened to Y was . . .I What X did to Y was . . .




I The initial example:The boy threw the red ball to the girlAGENT THEME GOAL

I Tests for semantic rolesI AGENT: add on purpose

I Jon took the book on purpose

I THEME/PATIENTI What happened to Y was . . .I What X did to Y was . . .



Quiz

I https://b.socrative.com/login/student/

I Room name: ’KUTUZOV’


https://b.socrative.com/login/student/


Problems for semantic roles

I Assumptions:I Small, fixed set of roles;

I Semantic roles are atomic;I Every argument position is assigned exactly one role;I Every semantic role is assigned to at most one argument

I Every assumption has been contested at some point.




I Assumptions:I Small, fixed set of roles;I Semantic roles are atomic;

I Every argument position is assigned exactly one role;I Every semantic role is assigned to at most one argument





I Assumptions:I Small, fixed set of roles;I Semantic roles are atomic;I Every argument position is assigned exactly one role;

I Every semantic role is assigned to at most one argument





I Assumptions:I Small, fixed set of roles;I Semantic roles are atomic;I Every argument position is assigned exactly one role;I Every semantic role is assigned to at most one argument





I Assumptions:I Small, fixed set of roles;I Semantic roles are atomic;I Every argument position is assigned exactly one role;I Every semantic role is assigned to at most one argument





I No real consensus about role inventory.

I Difficult to formulate formal definitions of role types.

I But we need semantic roles to do inference for practical tasks!

Two ‘responses’

1. ⇒ more generalized semantic roles [Dowty 1991]

I PROTO-AGENT, PROTO-PATIENTI PropBank lexical database project.

2. ⇒ more fine-grained semantic roles, specific to particularverbs [Fillmore 1968, Fillmore 1977]

I FrameNet lexical database project.

Let’s describe these two approaches (and resources) in more detail.







Two ‘responses’












Two ‘responses’












Two ‘responses’


I PROTO-AGENT, PROTO-PATIENT

I PropBank lexical database project.










Two ‘responses’












Two ‘responses’












Two ‘responses’








Contents

Introduction




Summary



Dowty’s Proto-roles

I An influential theoretical approach.

I Semantic role: ‘set of entailments of a group of predicateswith respect to one of the arguments of each’ [Dowty 1991]

I x murders y, x nominates y, x interrogates yI → x does a volitional act (¬ ‘kills’)I → x intends it to be this kind of act (¬ ‘convince’)I → x causes an event involving y (¬ ‘looks at’)I → x moves or changes externally (¬ ‘understands’)




I An influential theoretical approach.I Semantic role: ‘set of entailments of a group of predicates

with respect to one of the arguments of each’ [Dowty 1991]







I x murders y, x nominates y, x interrogates y

I → x does a volitional act (¬ ‘kills’)I → x intends it to be this kind of act (¬ ‘convince’)I → x causes an event involving y (¬ ‘looks at’)I → x moves or changes externally (¬ ‘understands’)






I x murders y, x nominates y, x interrogates yI → x does a volitional act (¬ ‘kills’)

I → x intends it to be this kind of act (¬ ‘convince’)I → x causes an event involving y (¬ ‘looks at’)I → x moves or changes externally (¬ ‘understands’)






I x murders y, x nominates y, x interrogates yI → x does a volitional act (¬ ‘kills’)I → x intends it to be this kind of act (¬ ‘convince’)

I → x causes an event involving y (¬ ‘looks at’)I → x moves or changes externally (¬ ‘understands’)






I x murders y, x nominates y, x interrogates yI → x does a volitional act (¬ ‘kills’)I → x intends it to be this kind of act (¬ ‘convince’)I → x causes an event involving y (¬ ‘looks at’)

I → x moves or changes externally (¬ ‘understands’)










I Only two ‘thematic-role-like concepts’ for verbal predicates:1. proto-agent role (Arg0)2. proto-patient role (Arg1).

I Individual arguments have different ‘degrees of membership’ inPROTO-AGENT and PROTO-PATIENT

I Proto-roles are cluster-concepts determined for each predicate:

I Properties (entailments) of Proto-agent:I volition;I sentience (and/or perception);I causes event;I movement.

I Properties (entailments) of Proto-patient:I change of state;I incremental theme;I causally affected by event;I stationary (relative to movement by agent).



























Proto-roles and linking

I Argument Selection Principle (ASP)

I The argument with the most PROTO-AGENT propertiesbecomes subject (Arg0);

I The argument with the most PROTO-PATIENT propertiesbecomes object (Arg1).

I If two compete, both will be possible (psychological verbs, forexample):

I Experiencer is sentient/perceiving;I Stimulus causes emotional reaction.

I x likes y / y pleases xI x fears y / y frightens x




I Argument Selection Principle (ASP)I The argument with the most PROTO-AGENT properties

becomes subject (Arg0);

I The argument with the most PROTO-PATIENT propertiesbecomes object (Arg1).








becomes subject (Arg0);I The argument with the most PROTO-PATIENT properties

becomes object (Arg1).

























Argument structure for ‘break ’:

I Frameset break.01 ‘break, cause to not be whole’:

I Arg0: breakerI Arg1: thing brokenI Arg2: instrumentI Arg3: pieces




Argument structure for ‘break ’:

I Frameset break.01 ‘break, cause to not be whole’:I Arg0: breakerI Arg1: thing brokenI Arg2: instrumentI Arg3: pieces



PropBank: semantic propositions corpus

I Sentences annotated with semantic roles [Bonial et al. 2014]

I Freely available at https://propbank.github.io/I Focuses on verbs, closely related to VerbNet [Kipper et al. 2000]

I Accessible with NLTK :I python3 -m nltk.downloader ’propbank’I from nltk.corpus import propbankI propbank.verbs()I instance = propbank.instances(100)I instance.rolesetI reach.01

I More details athttp://www.nltk.org/howto/propbank.html.

I PropBank is now developed in close conjunction withAbstract Meaning Representation (AMR) [Banarescu et al. 2013]

and OntoNotes projects.


https://propbank.github.io/

http://www.nltk.org/howto/propbank.html




I Freely available at https://propbank.github.io/

I Focuses on verbs, closely related to VerbNet [Kipper et al. 2000]























I Accessible with NLTK :

I python3 -m nltk.downloader ’propbank’I from nltk.corpus import propbankI propbank.verbs()I instance = propbank.instances(100)I instance.rolesetI reach.01










































I SemLink project tries to integrate PropBank and FrameNet(and Wordnet):

I http://verbs.colorado.edu/semlink/


http://verbs.colorado.edu/semlink/



I SemLink project tries to integrate PropBank and FrameNet(and Wordnet):

I http://verbs.colorado.edu/semlink/


http://verbs.colorado.edu/semlink/


Contents

Introduction




Summary



Frame Semantics

The frame approach is motivated by the work of CharlesFillmore

I ‘Meanings are relative to scenes’

I ‘The study of meaning is the study ofcognitive scenes that are created oractivated by utterances’

I ‘whenever we understand a linguisticexpression of whatever sort, we havesimultaneously a background scene and aperspective on that scene’

[Fillmore 1977]



Frame Semantics





[Fillmore 1977]



Frame Semantics





[Fillmore 1977]



Historical roots (60s)

I Fillmore’s case grammarI Fillmore was inspired by the works of Lucien Tesniere, the

father of the contemporary dependency grammar.

I case frame: small abstract scene identifying the participants ofthe scene and thus the arguments of predicates and sentencesdescribing the scene;

I frame can be described by different verbs describing the samesituation.

I Artificial Intelligence research (Minsky and others)I frame-based knowledge representations;I can be called models or scripts or schemata;I collection of information about objects and events.

I Words evoke frames.

I Semantic roles (actants, cases) are assigned to participants ofthe frame.





father of the contemporary dependency grammar.I case frame: small abstract scene identifying the participants of

the scene and thus the arguments of predicates and sentencesdescribing the scene;















































Frame Semantics

I Roles are relative to a frame.

I Meaning of a verb can be modeled by reference to its frame.I Arguments of a verb can be described by reference to relevant

participants and objects.I frame elements = semantic roles;I NB! these deep semantic roles are frame-specific.

I ExampleI commercial event (frame):I frame elements (core semantic roles): (buyer, seller, money,

goods)I Their relations:

I buyer, goods: sellI buyer, money: spendI etc.



Frame Semantics


I Meaning of a verb can be modeled by reference to its frame.

I Arguments of a verb can be described by reference to relevantparticipants and objects.

I frame elements = semantic roles;I NB! these deep semantic roles are frame-specific.






Frame Semantics









Frame Semantics





goods)

I Their relations:I buyer, goods: sellI buyer, money: spendI etc.



Frame Semantics






I buyer, goods: sell

I buyer, money: spendI etc.



Frame Semantics






I buyer, goods: sellI buyer, money: spend

I etc.



Frame Semantics









Commercial event frame (partial)

BUYER buy GOODS (SELLER) (PRICE)subject object from for

Alfred bought the book from Olivia for 10 dollarsAlfred bought them for 1 dollarAlfred bought a bicycle from Sarah



Commercial event frame (partial)

VERB BUYER GOODS SELLER MONEY PLACEbuy subject object from for atsell to object subject for atcost ind.obj subject object atspend subject on object at



FrameNet lexical database

I 1224 manually described frame descriptions.

I Freely available (for several languages) athttps://framenet.icsi.berkeley.edu

I Accessible with NLTK:I python3 -m nltk.downloader ’framenet v17’I from nltk.corpus import framenet as fnI fn.frames()I fn.frame(200).nameI fn.frame(200).definitionI fn.frame(200).FE

I See http://www.nltk.org/howto/framenet.html for moredetails.


https://framenet.icsi.berkeley.edu

http://www.nltk.org/howto/framenet.html






























Example of a text annotated in the FrameNet paradigm.






Quiz

I https://b.socrative.com/login/student/

I Room name: ’KUTUZOV’


https://b.socrative.com/login/student/

Summary

Contents

Introduction




Summary


Summary

Summary

I Semantic roles are the roles which arguments play in the eventdescribed by the predicate.

I PropBank uses a small number of numbered argument labels(PROTO-AGENT, PROTO-PATIENT) as semantic roles.

I FrameNet uses many frame-specific elements as semanticroles.

I These resources are complementary.

I Automatic semantic role labeling (SRL) using machinelearning: the next lecture.


Summary

Summary







Summary

Summary







Summary

Summary







Summary

Summary







Summary

What’s next in the class?

I Introduction to Semantic Role Labeling (today);

I Modern approaches to Semantic Role Labeling (Nov 14)

I Obligatory assignment 4 (Project B) released later this week;

I Project B written report due Nov 24.I Group sessions:

I Exercises with semantic role labeling (Nov 13)I Discussing and working on Project B (November 20).


Summary








Summary








Summary





I Project B written report due Nov 24.

I Group sessions:I Exercises with semantic role labeling (Nov 13)I Discussing and working on Project B (November 20).


Summary








Summary






I Exercises with semantic role labeling (Nov 13)

I Discussing and working on Project B (November 20).


Summary








References

References I

I Laura Banarescu, Claire Bonial, Shu Cai, Madalina Georgescu,Kira Griffitt, Ulf Hermjakob, Kevin Knight, Philipp Koehn,Martha Palmer, and Nathan Schneider. 2013.Abstract meaning representation for sembanking. In Proceedingsof the 7th Linguistic Annotation Workshop and Interoperabilitywith Discourse, pages 178–186. Association for ComputationalLinguistics.

I Claire Bonial, Julia Bonn, Kathryn Conger, Jena D. Hwang, andMartha Palmer. 2014.Propbank: Semantics of new predicate types. In Proceedings ofthe Ninth International Conference on Language Resources andEvaluation (LREC-2014). European Language ResourcesAssociation (ELRA).


References

References II

I David Dowty. 1991.Thematic proto-roles and argument selection. Language,67(3):547–619.

I Charles Fillmore. 1968.The case for case. In E. Bach and R. Harms, editors, Universalsin Linguistic Theory. Holt, Rinehard and Winston, New York.

I Charles Fillmore. 1977.The case for case reopened. In Syntax and Semantics, volume 8.

I Ray Jackendoff. 1972.Semantic interpretation in generative grammar. The MIT Press,Cambridge, MA.


References

References III

I Karin Kipper, Hoa Trang Dang, Martha Palmer, et al. 2000.Class-based construction of a verb lexicon. AAAI/IAAI, 691:696.


Date post:	01-Jan-2020
Category:	Documents
Upload:	others
View:	10 times
Download:	0 times

INF5830 Introduction to Semantic Role Labeling · INF5830 Introduction to Semantic Role Labeling...

Documents