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Semantic (thematic) Roles Semantic Role Labelling/Predicate-Argument Structure
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Seman&c Analysis in Language Technology http://stp.lingfil.uu.se/~santinim/sais/2014/sais_2014.htm
Seman&c Role Labelling
/ Predicate-‐Argument Structure
Marina San&ni
Department of Linguis&cs and Philology Uppsala University, Uppsala, Sweden
Autumn 2014
Lecture 3: SRL/PAS 1
Outline
• Seman&c (thema&c) Roles • Seman&c Role Labelling/Predicate-‐Argument Structure
Lecture 3: SRL/PAS 2
The seman&cs of events • Predicates in FOL have fixed arity: they take a fixed number of arguments – predicates have a fixed arity
Lecture 3: SRL/PAS 3
event variables à (neo) Davidsonian event representa&on
• No need to specify a fixed number of arguments • The event itself is a single argument. • Everything else is captured by addi&onal predica&on
!Ǝe eating(e) ∧ eater(e, speaker)∧ eaten(e,turkey sandwich) ∧ meal(e,lunch) ∧ location(e,desk)∧time(e,tuesday)!
Lecture 3: SRL/PAS 4
What is the seman&c similarity here?
• John broke the window Ǝe x,y, breaking(e) ∧ breaker(e, x) ∧ john(e,x)brokenThing(e,y) ∧ window(e,y)!
• Mary opend the door Ǝe x,y, opening(e) ∧ opener(e, x) ∧ mary(e,x) ∧ openThing(e,y) ∧ door(e,y)#
Lecture 3: SRL/PAS 5
Deep roles = agents
Examples: Thema&c Roles
• Thema&c roles refer to a par&cular model of seman&c roles
• Them roles try to capture the seman&c commonality betw breaker and eater à agents à voli&onal causa&on
• brokenThing and openedThing are inanimate objects that are affected by te ac&on à themes
Lecture 3: SRL/PAS 6
2 seman&c constraints on the arguments of event predicate
1. Seman&c Roles 2. Selec&onal Constraints
Lecture 3: SRL/PAS 7
I. Seman&c Roles
• Express the seman&c of the arguments and its rela&on to predicate
Lecture 3: SRL/PAS 8
Examples
• Some common roles
Lecture 3: SRL/PAS 9
Why are they useful?
• Help generalize over different surface realiza&ons of predicate arguments.
• Ex: Diathesis
Lecture 3: SRL/PAS 10
Problems • No standard set of roles • Some&mes, many fine-‐grained roles • Difficult to formalize
• Solu&on? – Generalized seman&c roles
• PROTO-‐AGENT, PROTO-‐PATIENT, etc. … the more an argument displays agent-‐like proper&es (voli&on, inten&onality etc), the greater the possibility that the argument can be labelled a proto-‐agent…
Lecture 3: SRL/PAS 11
Predicate-‐Argument Structure
The argument structure of a verb is the lexical informa&on about the arguments of a predicate and their seman&c and syntac&c proper&es. Argument structure is generally seen as intermediate between seman&c-‐role structure and syntac&c-‐func&on structure. See: h^p://www.glo^opedia.org/index.php/Argument_structure
Lecture 3: SRL/PAS 12
Ex Argument structure is what makes a lexical head induce argument posi&ons in syntac&c structure is called its argument structure. Example: the head open has an argument structure which induces obligatorily one argument posi&on (Theme), and op&onally two more (Agent and Instrument).
Lecture 3: SRL/PAS 13
PropBank • Resource of sentences annotated with seman&c roles. – The English PropBank: sentences from the PennTreeBank.
• Each sense of each verb has a specific set of roles: – Arg0 = proto-‐agent – Arg1 = proto-‐pa&ent – The seman&c of the other roles is specific to each verb sense…
Lecture 3: SRL/PAS 14
Ex • Same role, despite the differing surface forms: increase and Arg1
Lecture 3: SRL/PAS 15
FrameNet
• Project that a^empts to generalize seman&c roles on different verbs and also betw verbs and nouns
Lecture 3: SRL/PAS 16
Frame • A structure with seman&c roles includes frame elements: – Core roles – Non-‐core roles
Lecture 3: SRL/PAS 17
Each word evoke a frame
• Ex: change_posi&on_on_a_scale
Lecture 3: SRL/PAS 18
II. Selec&onal Restric&ons • Seman&c constraints on arguments
• Constraints that the verb imposes on the concepts that are allowed to fill its arguments roles.
– I want to eat home – I want to eat French food
How do we know that ”home” is not a argument of eat? Seman&cally, we can say that the theme of ”eat” is edible. edible becomes a selec&onal restric&on of the theme of eat.
Lecture 3: SRL/PAS 19
Selec&onal Restric&ons and FOL
• neo-‐Davidsonian representa&on of events:
Lecture 3: SRL/PAS 20
• Drawbacks (p. 662) – Using FOL for a simple task like this is overkill. Far too computa&onally expensive
– We would need a KB of facts and concepts that is very large…
A more prac&cal approach • State selec&onal restric&ons in terms f WordNet synsets rather than as logical concepts.
• Each predicate simply specifies a WordNet synset as the selec&onal restrictons on each of its arguments.
ex: eat (food, nutrient) Selec&onal restric&on o the theme role of eat to the sysets àfood, nutrient
Lecture 3: SRL/PAS 21
Seman&c Role Labelling
• Synonyms: – Thema&c role labelling – Case role assignment – Shallow seman&c parsing
• What is it? – The task of automa&cally finding the appropriate role for each predicate in a sentence
Lecture 3: SRL/PAS 22
Current Approaches
• Based on supervised machine learning – Adequate amounts of training and testng sets. – FrameNet and PropBank have been used for this purpose.
Lecture 3: SRL/PAS 23
Features suggested by Gildea and Jurafsky (2000, 2002)
Lecture 3: SRL/PAS 24
Vectors of Features
• SVM, Maximum Entropy and other classifiers
Lecture 3: SRL/PAS 25
The End
Lecture 3: SRL/PAS 26