augmenting wordnet for deep understanding of text
DESCRIPTION
Augmenting WordNet for Deep Understanding of Text. Peter Clark, Phil Harrison, Bill Murray, John Thompson (Boeing) Christiane Fellbaum (Princeton Univ) Jerry Hobbs (ISI/USC). “The soldier died” “The soldier was shot” “There was a fight” …. “A soldier was killed in a gun battle”. - PowerPoint PPT PresentationTRANSCRIPT
![Page 1: Augmenting WordNet for Deep Understanding of Text](https://reader030.vdocuments.net/reader030/viewer/2022032605/56812c34550346895d90b92e/html5/thumbnails/1.jpg)
Augmenting WordNet for Deep Understanding of Text
Peter Clark, Phil Harrison, Bill Murray, John Thompson (Boeing)Christiane Fellbaum (Princeton Univ)Jerry Hobbs (ISI/USC)
![Page 2: Augmenting WordNet for Deep Understanding of Text](https://reader030.vdocuments.net/reader030/viewer/2022032605/56812c34550346895d90b92e/html5/thumbnails/2.jpg)
“Deep Understanding”• Not (just) parsing + word senses• Construction of a coherent representation of the scene
the text describes• Challenge: much of that representation is not in the text
“A soldier was killed in a gun battle”
“The soldier died”“The soldier was shot”“There was a fight”…
![Page 3: Augmenting WordNet for Deep Understanding of Text](https://reader030.vdocuments.net/reader030/viewer/2022032605/56812c34550346895d90b92e/html5/thumbnails/3.jpg)
“Deep Understanding”
“A soldier was killed in a gun battle”
“The soldier died”“The soldier was shot”“There was a fight”…
BecauseA battle involves a fight.Soldiers use guns.Guns shoot.Guns can kill.If you are killed, you are dead.….
How do we get this knowledge into the machine?How do we exploit it?
![Page 4: Augmenting WordNet for Deep Understanding of Text](https://reader030.vdocuments.net/reader030/viewer/2022032605/56812c34550346895d90b92e/html5/thumbnails/4.jpg)
“Deep Understanding”
“A soldier was killed in a gun battle”
“The soldier died”“The soldier was shot”“There was a fight”…
BecauseA battle involves a fight.Soldiers use guns.Guns shoot.Guns can kill.If you are killed, you are dead.….
Several partially useful resources exist.WordNet is already used a lot…can we extend it?
![Page 5: Augmenting WordNet for Deep Understanding of Text](https://reader030.vdocuments.net/reader030/viewer/2022032605/56812c34550346895d90b92e/html5/thumbnails/5.jpg)
The Initial Vision• Our vision:
Rapidly expand WordNet to be more of a knowledge-base
Question-answering software to demonstrate its use
![Page 6: Augmenting WordNet for Deep Understanding of Text](https://reader030.vdocuments.net/reader030/viewer/2022032605/56812c34550346895d90b92e/html5/thumbnails/6.jpg)
The Evolution of WordNet
• v1.0 (1986)– synsets (concepts) + hypernym (isa) links
• v1.7 (2001)– add in additional relationships
• has-part• causes• member-of• entails-doing (“subevent”)
• v2.0 (2003)– introduce the instance/class distinction
• Paris isa Capital-City is-type-of City– add in some derivational links
• explode related-to explosion
• …• v10.0 (200?)
– ?????
lexicalresource
knowledgebase?
![Page 7: Augmenting WordNet for Deep Understanding of Text](https://reader030.vdocuments.net/reader030/viewer/2022032605/56812c34550346895d90b92e/html5/thumbnails/7.jpg)
Augmenting WordNet
• World Knowledge– Sense-disambiguate the glosses (by hand)– Convert the glosses to logic
• Similar to LCC’s Extended WordNet attempt– Axiomatize “core theories”
• WordNet links– Morphosemantic links– Purpose links
• Experiments
![Page 8: Augmenting WordNet for Deep Understanding of Text](https://reader030.vdocuments.net/reader030/viewer/2022032605/56812c34550346895d90b92e/html5/thumbnails/8.jpg)
Converting the Glosses to Logic
Convert gloss to form “word is gloss”
Parse (Charniak)
“ambition#n2: A strong drive for success”
LFToolkit: Generate logical form fragments
strong drive for success
strong(x1) & drive(x2) & for(x3,x4) & success(x5)
Lexical output rulesproduce logical form
fragments
![Page 9: Augmenting WordNet for Deep Understanding of Text](https://reader030.vdocuments.net/reader030/viewer/2022032605/56812c34550346895d90b92e/html5/thumbnails/9.jpg)
Converting the Glosses to Logic
Convert gloss to form “word is gloss”
Parse (Charniak)
LFToolkit: Generate logical form fragments
Identify equalities, add senses
“ambition#n2: A strong drive for success”
![Page 10: Augmenting WordNet for Deep Understanding of Text](https://reader030.vdocuments.net/reader030/viewer/2022032605/56812c34550346895d90b92e/html5/thumbnails/10.jpg)
Converting the Glosses to Logic
Identify equalities, add senses
A strong drive for success
strong(x1) & drive(x2) & for(x3,x4) & success(x5)
x2=x3
x1=x2
Lexical output rulesproduce logical form
fragments
Composition rulesidentify variables
x4=x5
![Page 11: Augmenting WordNet for Deep Understanding of Text](https://reader030.vdocuments.net/reader030/viewer/2022032605/56812c34550346895d90b92e/html5/thumbnails/11.jpg)
Converting the Glosses to Logic
Convert gloss to form “word is gloss”
Parse (Charniak)
LFToolkit: Generate logical form fragments
Identify equalities, add senses
“ambition#n2: A strong drive for success”
ambition#n2(x1) → a(x1) & strong#a1(x1) & drive#n2(x1) & for(x1,x2) & success#a3(x2)
![Page 12: Augmenting WordNet for Deep Understanding of Text](https://reader030.vdocuments.net/reader030/viewer/2022032605/56812c34550346895d90b92e/html5/thumbnails/12.jpg)
Converting the Glosses to Logic• Sometimes works well!• But often not. Primary problems:
1. Errors in the language processing2. Only capture definitional knowledge3. “flowery” language, many gaps, metonymy, ambiguity;
If logic closely follows syntax → “logico-babble”
“hammer#n2: tool used to deliver an impulsive force by striking”hammer#n2(x1) →
tool#n1(x1) & use#v1(e1,x2,x1) & to(e1,e2) & deliver#v2(e2,x3) & driving#a1(x3) & force#n1(x3) & by(e3,e4) & strike#v3(e4,x4).
→ Hammers hit things??
![Page 13: Augmenting WordNet for Deep Understanding of Text](https://reader030.vdocuments.net/reader030/viewer/2022032605/56812c34550346895d90b92e/html5/thumbnails/13.jpg)
Augmenting WordNet
• World Knowledge– Sense-disambiguate the glosses (by hand)– Convert the glosses to logic– Axiomatize “core theories”
• WordNet links– Morphosemantic links– Purpose links
• Experiments
![Page 14: Augmenting WordNet for Deep Understanding of Text](https://reader030.vdocuments.net/reader030/viewer/2022032605/56812c34550346895d90b92e/html5/thumbnails/14.jpg)
Core Theories
• Many domain-specific facts are instantiations of more general, “core” knowledge
• By encoding this core knowledge, get leverage• eg 517 “vehicle” noun (senses), 185 “cover” verb (senses)
• Approach:– Analysis and grouping of words in Core WordNet– Identification and encoding of underlying theories
![Page 15: Augmenting WordNet for Deep Understanding of Text](https://reader030.vdocuments.net/reader030/viewer/2022032605/56812c34550346895d90b92e/html5/thumbnails/15.jpg)
Composite Entities: perfect, empty, relative, secondary, similar, odd, ...Scales: step, degree, level, intensify, high, major, considerable, ...Events: constraint, secure, generate, fix, power, development, ...Space: grade, inside, lot, top, list, direction, turn, enlarge, long, ...Time: year, day, summer, recent, old, early, present, then, often, ...Cognition: imagination, horror, rely, remind, matter, estimate, idea, ...Communication: journal, poetry, announcement, gesture, charter, ...Persons and their Activities: leisure, childhood, glance, cousin, jump, ...Microsocial: virtue, separate, friendly, married, company, name, ...Material World: smoke, shell, stick, carbon, blue, burn, dry, tough, ... Geo: storm, moon, pole, world, peak, site, village, sea, island, ...Artifacts: bell, button, van, shelf, machine, film, floor, glass, chair, ...Food: cheese, potato, milk, break, cake, meat, beer, bake, spoil, ... Macrosocial: architecture, airport, headquarters, prosecution, ...Economic: import, money, policy, poverty, profit, venture, owe, ...
Core Theories
![Page 16: Augmenting WordNet for Deep Understanding of Text](https://reader030.vdocuments.net/reader030/viewer/2022032605/56812c34550346895d90b92e/html5/thumbnails/16.jpg)
Augmenting WordNet
• World Knowledge– Sense-disambiguate the glosses (by hand)– Convert the glosses to logic– Axiomatize “core theories”
• WordNet links– Morphosemantic links– Purpose links
• Experiments
![Page 17: Augmenting WordNet for Deep Understanding of Text](https://reader030.vdocuments.net/reader030/viewer/2022032605/56812c34550346895d90b92e/html5/thumbnails/17.jpg)
Morphosemantic Links• Often need to cross part-of-speech
T: A council worker cleans up after Tuesday's violence in Budapest.H: There were attacks in Budapest on Tuesday.
(“attack”) attack_v3 aggression_n4 (←“violence”)
“aggress”/“aggression”derivation link
• Can solve with WN’s derivation links:
![Page 18: Augmenting WordNet for Deep Understanding of Text](https://reader030.vdocuments.net/reader030/viewer/2022032605/56812c34550346895d90b92e/html5/thumbnails/18.jpg)
Morphosemantic Links• But can go wrong!
T: Paying was slowH1: The transaction was slowH2: *The person was slow [NOT entailed]
“pay”/“payment”payment_n1 (→ “transaction”)(“pay”) pay_v1
“pay”/“payer”payer_n1 (→ “person”)(“pay”) pay_v1
Problem: The type of relation matters for derivatives! (Event? Agent?..)
A pays B → The payment (event-noun) by A A is the payer (agent-noun) of B
etc.
![Page 19: Augmenting WordNet for Deep Understanding of Text](https://reader030.vdocuments.net/reader030/viewer/2022032605/56812c34550346895d90b92e/html5/thumbnails/19.jpg)
Morphosemantic Links• Task: Classify the 22,000 links in WordNet:
• Semi-automatic process– Exploit taxonomy and morphology
• 15 semantic types used– agent, undergoer, instrument, result, material, destination,
location, result, by-means-of, event, uses, state, property, body-part, vehicle.
Verb Synset Noun Synset Relationshiphammer_v1 hammer_n1 instrumentexecute_v1 execution_n1 event (equal)sign_v2 signatory_n1 agent
![Page 21: Augmenting WordNet for Deep Understanding of Text](https://reader030.vdocuments.net/reader030/viewer/2022032605/56812c34550346895d90b92e/html5/thumbnails/21.jpg)
Task: Recognizing Entailment• Experiment with WordNet, logical glosses, DIRT• Text interpretation to logic using Boeing’s NLP system
• Entailment: T → H if:– T is subsumed by H (“cat eats mouse” → “animal was eaten”)– An elaboration of T using inference rules is subsumed by H
• (“cat eats mouse” → “cat swallows mouse”)
• No statistical similarity metrics
“A soldier was killed in a gun battle”
“soldier”(soldier01),“kill”(…..object(kill01,soldier01),“in”(kill01,battle01),modifier(battle01,gun01).
isa(soldier01,soldier_n1), isa(……object(kill01,soldier01)during(kill01,battle01)instrument(battle01,gun01)
Initial Logic Final Logic
![Page 22: Augmenting WordNet for Deep Understanding of Text](https://reader030.vdocuments.net/reader030/viewer/2022032605/56812c34550346895d90b92e/html5/thumbnails/22.jpg)
Successful Examples with the Glosses• Good example
T: Britain puts curbs on immigrant labor from Bulgaria and Romania.H: Britain restricted workers from Bulgaria.
14.H4
![Page 23: Augmenting WordNet for Deep Understanding of Text](https://reader030.vdocuments.net/reader030/viewer/2022032605/56812c34550346895d90b92e/html5/thumbnails/23.jpg)
Successful Examples with the Glosses• Good example
T: Britain puts curbs on immigrant labor from Bulgaria and Romania.H: Britain restricted workers from Bulgaria.
WN: limit_v1:"restrict“: place limits on.
→ ENTAILED (correct)
14.H4
T: Britain puts curbs on immigrant labor from Bulgaria and Romania.
H: Britain placed limits on workers from Bulgaria.
![Page 24: Augmenting WordNet for Deep Understanding of Text](https://reader030.vdocuments.net/reader030/viewer/2022032605/56812c34550346895d90b92e/html5/thumbnails/24.jpg)
T: The administration managed to track down the perpetrators.H: The perpetrators were being chased by the administration.
56.H3
Successful Examples with the Glosses• Another (somewhat) good example
![Page 25: Augmenting WordNet for Deep Understanding of Text](https://reader030.vdocuments.net/reader030/viewer/2022032605/56812c34550346895d90b92e/html5/thumbnails/25.jpg)
T: The administration managed to track down the perpetrators.H: The perpetrators were being chased by the administration.
WN: hunt_v1 “hunt” “track down”: pursue for food or sport
→ ENTAILED (correct)
56.H3
T: The administration managed to pursue the perpetrators [for food or sport!].H: The perpetrators were being chased by the administration.
Successful Examples with the Glosses• Another (somewhat) good example
![Page 26: Augmenting WordNet for Deep Understanding of Text](https://reader030.vdocuments.net/reader030/viewer/2022032605/56812c34550346895d90b92e/html5/thumbnails/26.jpg)
Unsuccessful examples with the glosses• More common: Being “tantalizingly close”
T: Satomi Mitarai bled to death.H: His blood flowed out of his body.
16.H3
![Page 27: Augmenting WordNet for Deep Understanding of Text](https://reader030.vdocuments.net/reader030/viewer/2022032605/56812c34550346895d90b92e/html5/thumbnails/27.jpg)
Unsuccessful examples with the glosses• More common: Being “tantalizingly close”
T: Satomi Mitarai bled to death.H: His blood flowed out of his body.
16.H3
bleed_v1: "shed blood", "bleed", "hemorrhage": lose blood from one's body
WordNet:
So close!
Need to also know: “lose liquid from container” → “liquid flows out of container”
usually
![Page 28: Augmenting WordNet for Deep Understanding of Text](https://reader030.vdocuments.net/reader030/viewer/2022032605/56812c34550346895d90b92e/html5/thumbnails/28.jpg)
T: The National Philharmonic orchestra draws large crowds.H: Large crowds were drawn to listen to the orchestra.
20.H2
Unsuccessful examples with the glosses• More common: Being “tantalizingly close”
![Page 29: Augmenting WordNet for Deep Understanding of Text](https://reader030.vdocuments.net/reader030/viewer/2022032605/56812c34550346895d90b92e/html5/thumbnails/29.jpg)
T: The National Philharmonic orchestra draws large crowds.H: Large crowds were drawn to listen to the orchestra.
20.H2
WN: orchestra = collection of musicians WN: musician: plays musical instrument WN: music = sound produced by musical instruments WN: listen = hear = perceive sound
WordNet:
So close!
Unsuccessful examples with the glosses• More common: Being “tantalizingly close”
![Page 30: Augmenting WordNet for Deep Understanding of Text](https://reader030.vdocuments.net/reader030/viewer/2022032605/56812c34550346895d90b92e/html5/thumbnails/30.jpg)
Success with Morphosemantic Links• Good example
T: The Zoopraxiscope was invented by Mulbridge.H*: Mulbridge was the invention of the Zoopraxiscope. [NOT entailed]
66.H100
(“invent”) invent_v1 invention_n1 (“invention”)
“invent”/ “invention”derivation link
But need an agent (X verb Y -> X is agent-noun of Y)Got: result-noun (“invention” is result of “invent”)
So no entailment (correct!)
WordNet too permissive!:
![Page 31: Augmenting WordNet for Deep Understanding of Text](https://reader030.vdocuments.net/reader030/viewer/2022032605/56812c34550346895d90b92e/html5/thumbnails/31.jpg)
T: The president visited Iraq in September.H: The president traveled to Iraq.
54.H1
Successful Examples with DIRT• Good example
DIRT: IF Y is visited by X THEN X flocks to YWordNet: "flock" is a type of "travel"
Entailed [correct]
![Page 32: Augmenting WordNet for Deep Understanding of Text](https://reader030.vdocuments.net/reader030/viewer/2022032605/56812c34550346895d90b92e/html5/thumbnails/32.jpg)
T: The US troops stayed in Iraq although the war was over.H*: The US troops left Iraq when the war was over. [NOT entailed]
55.H100
Unsuccessful Examples with DIRT• Bad rule
DIRT: IF Y stays in X THEN Y leaves X
Entailed [incorrect]
![Page 33: Augmenting WordNet for Deep Understanding of Text](https://reader030.vdocuments.net/reader030/viewer/2022032605/56812c34550346895d90b92e/html5/thumbnails/33.jpg)
Overall Results• Note: Eschewing statistics!
• BPI test suite (61%):
Correct Incorrect
When H or ¬H is predicted by:
Simple syntax manipulation 11 3
WordNet taxonomy + morphosemantics 14 1
WordNet logicalized glosses 4 1
DIRT paraphrase rules 27 20
When H or ¬H is not predicted: 97 72
“Straight-Forward”
![Page 34: Augmenting WordNet for Deep Understanding of Text](https://reader030.vdocuments.net/reader030/viewer/2022032605/56812c34550346895d90b92e/html5/thumbnails/34.jpg)
Overall Results• Note: Eschewing statistics!
• BPI test suite (61%):
Correct Incorrect
When H or ¬H is predicted by:
Simple syntax manipulation 11 3
WordNet taxonomy + morphosemantics 14 1
WordNet logicalized glosses 4 1
DIRT paraphrase rules 27 20
When H or ¬H is not predicted: 97 72
Useful
![Page 35: Augmenting WordNet for Deep Understanding of Text](https://reader030.vdocuments.net/reader030/viewer/2022032605/56812c34550346895d90b92e/html5/thumbnails/35.jpg)
Overall Results• Note: Eschewing statistics!
• BPI test suite (61%):
Correct Incorrect
When H or ¬H is predicted by:
Simple syntax manipulation 11 3
WordNet taxonomy + morphosemantics 14 1
WordNet logicalized glosses 4 1
DIRT paraphrase rules 27 20
When H or ¬H is not predicted: 97 72
Occasionallyuseful
![Page 36: Augmenting WordNet for Deep Understanding of Text](https://reader030.vdocuments.net/reader030/viewer/2022032605/56812c34550346895d90b92e/html5/thumbnails/36.jpg)
Overall Results• Note: Eschewing statistics!
• BPI test suite (61%):
Correct Incorrect
When H or ¬H is predicted by:
Simple syntax manipulation 11 3
WordNet taxonomy + morphosemantics 14 1
WordNet logicalized glosses 4 1
DIRT paraphrase rules 27 20
When H or ¬H is not predicted: 97 72
Often useful but
unreliable
• RTE3: 55%
![Page 37: Augmenting WordNet for Deep Understanding of Text](https://reader030.vdocuments.net/reader030/viewer/2022032605/56812c34550346895d90b92e/html5/thumbnails/37.jpg)
Summary
• “Understanding”– Constructing a coherent model of the scene being described– Much is implicit in text → Need lots of world knowledge
• Augmenting WordNet– Made some steps forward:
• More connectivity
• Logicalized glosses
• But still need a lot more knowledge!
![Page 38: Augmenting WordNet for Deep Understanding of Text](https://reader030.vdocuments.net/reader030/viewer/2022032605/56812c34550346895d90b92e/html5/thumbnails/38.jpg)
Thank you!