Abstract Meaning Representation. Computational Linguistics Emory University Jinho D. Choi

Transcription

1 Abstract Meaning Representation Computational Linguistics Emory University D.

2 Abstract Meaning Representation Introduced in The dog will eat the bone. The dog ate the bone. agent eat patient A dog is eating a bone. dog bone Revisited in 213. PropBank, named entities, coreferences, etc. 2

3 Pennman Notation A simple way of representing directed graphs. The dog is eating a bone. (e / eat-1 :ARG (d / dog) :ARG1 (b / bone)) Variables Relations e/eat-1 ARG ARG1 Concepts d/dog b/bone 3

4 Reentrancy The dog is eating a bone. ARG d/dog e/eat-1 ARG1 b/bone (e / eat-1 :ARG (d / dog) :ARG1 (b / bone)) The dog to eat a bone. ARG d/dog w/want-1 ARG ARG1 e/eat-1 ARG1 b/bone (w / want-1 :ARG (d / dog) :ARG1 (e / eat-1 :ARG d :ARG1 (b / bone))) 4

5 Inverse Relations and Focus The dog ate. Focus? The dog that ate. (e / eat-1 :ARG (d / dog)) (d / dog :ARG-of (r / ran-1)) The dog ate the bone that it found. ARG e/eat-1 ARG1 X ARG Y Y ARG-of X d/dog b/bone ARG f/find-1 ARG1 5

6 Inverse Relations and Focus The dog ate the bone that it found. ARG e/eat-1 ARG1 ARG e/eat-1 ARG1 d/dog b/bone d/dog b/bone ARG ARG1 ARG ARG1-OF f/find-1 f/find-1 (e / eat-1 :ARG (d / dog) :ARG1 (b / bone :ARG1-of (f / find-1 :ARG d))) 6

7 Constants Relations with no variable. Negations The dog did not eat the bone. (e /eat-1 :polarity - :ARG (d / dog) :ARG1 (b / bone)) Numbers The dog ate four bones. (e /eat-1 :ARG (d / dog) :ARG1 (b / bone :quant 4)) 7

8 AMR Notation The dog ate four bones that it found. Variable (e / eat-1 Concept :ARG (d / dog) :ARG1 (b / bone :quant 4 Relation :ARG1-of (f / find-1 :ARG d))) Inverse Relation Reentrancy 8

9 AMR Framesets Frames are generalized across similar concepts. My fear of snakes I am fearful of snakes I fear snakes fear-1 I m afraid of snakes Frames are not generalized across synonyms. I fear snakes I m terrified of snakes Snakes creep me out fear-1 teerify-1 creep-3 9

10 Stemming All concepts drop plurality, articles, and tenses. A dog dogs The dog The dogs Eats Eating Ate Will eat (d / dog) (e / eat-1) Consistent across cross-linguistically. 1

11 Abstraction The man described the mission as a disaster. The man s description of the mission: disaster. As the man described it, the mission was a disaster. The man described the mission as disastrous. (d / describe-1 :ARG (m / man) :ARG1 (m2 / mission) :ARG2 (d / disaster)) 11

12 Non-Core Role Inventory Arguments that are not numbered. :accompanier, :age :beneficiary :compared-to, :concession, :condition, :consist-of :degree, :destination, :direction, :domain, :duration :example, :extent :frequency :instrument :location :manner, :medium, :mod, :mode :name :ord :part, :path, :polarity, :poss, :purpose :quant :scale, :source, :subevent :time, :topic, :unit :value :wiki 12

13 More Inventory Apples and Bananas (a / and :op1 (a2 / apple) :op2 (b / banana)) Tuesday the 19th (d / date-entity :weekday (t / tuesday) :day 19) Barack Obama (p / person :name (n / name :op1 "Barack" :op2 Obama") :wiki Barack_Obama) Five bucks (m / monetary-quantity :unit dollar :quant 5) 13

14 Dependency Tree vs. AMR Graph to a in nsubj Dependency Tree xcomp AMR Graph want-1 A1 nn aux dobj prep A A -1 A1 location to det in pobj person name a op1 name op2 14

15 AMR Graph vs. Span Graph to a in AMR Graph person name want-1 A A A1-1 A1 location want-1[3:4] A Span Graph A1-1[5:6] A A1 location op1 name op2 person +name[1,3] [7:8] [9:1] 15

16 Transition-based AMR Parsing to a in σ β to a in Populate σ by post-traversing the dependency graph. Populate β with σ s children.? 16

17 to a in σ Transition-based AMR Parsing β jinho to a β = in ) NEXT NODE-l c ( 1, [],G) ) ( 1, = CH( 1,G ),G ) [! l c ] DELETE NODE ( 1, [],G) ) ( 1, = CH( 1,G ),G ) NONE 17

18 Transition-based AMR Parsing to a in σ β jinho to a in Merge σ and β ) (,,G) ) (,,G ) [] ) ) ( = ( 18

19 to a in σ Transition-based AMR Parsing β person+name to a β = in ) NEXT NODE-l c ( 1, [],G) ) ( 1, = CH( 1,G ),G ) [! l c ] DELETE NODE ( 1, [],G) ) ( 1, = CH( 1,G ),G ) NONE atlanta 19

20 Transition-based AMR Parsing person+name in σ in atlanta β Replace ) σ with β ) (,,G) ) (, = CH(,G ),G ) ( ) ) ( ) 2

21 Transition-based AMR Parsing person+name σ β β = atlanta? ) NEXT NODE-l c ( 1, [],G) ) ( 1, = CH( 1,G ),G ) [! l c ] DELETE NODE ( 1, [],G) ) ( 1, = CH( 1,G ),G ) NONE 21

22 Transition-based AMR Parsing person+name A1 location σ β ) (,,G) ) (,,G ) ) ) ( Label σ β atlanta [(, )! l r ] [( )! ] 22

23 Transition-based AMR Parsing person+name -1 A1 location σ β β = atlanta ) NEXT NODE-l c ( 1, [],G) ) ( 1, = CH( 1,G ),G ) [! l c ] DELETE NODE ( 1, [],G) ) ( 1, = CH( 1,G ),G ) NONE 23

24 Transition-based AMR Parsing person+name A -1 A1 location atlanta σ β ) ) (,,G) ) (,,G ) ( ) ) ( ) 24 Reenter k β! k is a single of β [(k, )! l r ]

25 Transition-based AMR Parsing A person+name A A1-1 A1 location atlanta σ β ) (,,G) ) (,,G ) ) ) ( Label σ β [(, )! l r ] [( )! ] 25

26 Transition-based AMR Parsing A person+name want-1 A A1-1 A1 location atlanta? σ β β = ) NEXT NODE-l c ( 1, [],G) ) ( 1, = CH( 1,G ),G ) [! l c ] DELETE NODE ( 1, [],G) ) ( 1, = CH( 1,G ),G ) NONE 26

27 Transition-based AMR Parsing Action Current state ) Result state Assign labels Precondition NEXT EDGE-l r (,,G) ) (,,G ) [(,! l ] ) r SWAP-l r (,,G) ) (, ),G [(, )! l r ] REATTACH k -l r (,,G) ) (,,G ) [(k, )! l r ] REPLACE HEAD (,,G) ) (, = CH(,G ),G ) NONE is not empty REENTRANCE k -l r (,,G) ) (,,G ) [(k, )! l r ] MERGE (,,G) ) (,,G ) NONE NEXT NODE-l c ( 1, [],G) ) ( 1, = CH( 1,G ),G ) [! l c ] DELETE NODE ( 1, [],G) ) ( 1, = CH( 1,G ),G ) NONE is empty like SWAP like REATTACH like dog and and and cat dog cat dog cat 27