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CSE391 – 2005 NLP1
Events
• From KRR lecture
CSE391 – 2005 NLP2
Ask Jeeves – A Q/A, IR ex.
What do you call a successful movie?• Tips on Being a Successful Movie Vampire ... I shall
call the police.• Successful Casting Call & Shoot for ``Clash of
Empires'' ... thank everyone for their participation in the making of yesterday's movie.
• Demme's casting is also highly entertaining, although I wouldn't go so far as to call it successful. This movie's resemblance to its predecessor is pretty vague...
• VHS Movies: Successful Cold Call Selling: Over 100 New Ideas, Scripts, and Examples from the Nation's Foremost Sales Trainer.
Blockbuster
CSE391 – 2005 NLP3
Ask Jeeves – filtering w/ POS tag
What do you call a successful movie?• Tips on Being a Successful Movie Vampire ... I shall
call the police.• Successful Casting Call & Shoot for ``Clash of
Empires'' ... thank everyone for their participation in the making of yesterday's movie.
• Demme's casting is also highly entertaining, although I wouldn't go so far as to call it successful. This movie's resemblance to its predecessor is pretty vague...
• VHS Movies: Successful Cold Call Selling: Over 100 New Ideas, Scripts, and Examples from the Nation's Foremost Sales Trainer.
CSE391 – 2005 NLP4
Filtering out “call the police”
call(you,movie,what) ≠ call(you,police)
Different senses, - different syntax, - different participants
you movie what you police
CSE391 – 2005 NLP5
“Meaning”
• Shallow semantic annotation– Captures critical dependencies, – Highlights participants– Reflects clear sense distinctions
• Supports training of supervised automatic taggers
• Works for other languages
CSE391 – 2005 NLP6
Cornerstone: English lexical resource
• That provides sets of possible syntactic frames for verbs.
• And provides clear, replicable sense distinctions.
AskJeeves: Who do you call for a good electronic lexical database for English?
CSE391 – 2005 NLP7
WordNet – Princeton (Miller 1985, Fellbaum 1998)
• On-line lexical reference (dictionary)– Nouns, verbs, adjectives, and adverbs
grouped into synonym sets
– Other relations include hypernyms (ISA), antonyms, meronyms
CSE391 – 2005 NLP8
WordNet
• http://www.cogsci.princeton.edu/~wn/
CSE391 – 2005 NLP9
WordNet – call, 28 senses1. name, call -- (assign a specified, proper name to; "They named their son David"; …) -> LABEL2. call, telephone, call up, phone, ring -- (get or try to get
into communication (with someone) by telephone; "I tried to call you all night"; …)
->TELECOMMUNICATE3. call -- (ascribe a quality to or give a name of a common
noun that reflects a quality; "He called me a bastard"; …)
-> LABEL4. call, send for -- (order, request, or command to come;
She was called into the director's office"; "Call the police!")
-> ORDER
CSE391 – 2005 NLP10
WordNet – Princeton (Miller 1985, Fellbaum 1998)
• Limitations as a computational lexicon– Contains little syntactic information
• Comlex has syntax but no sense distinctions
– No explicit lists of participants– Sense distinctions very fine-grained, – Definitions often vague
• Causes problems with creating training data for supervised Machine Learning – SENSEVAL2
• Verbs > 16 senses (including call)• Inter-annotator Agreement ITA 73%, • Automatic Word Sense Disambiguation, WSD 60.2%
Dang & Palmer, SIGLEX02
CSE391 – 2005 NLP11
WordNet: - call, 28 senses
WN2 , WN13,WN28 WN15 WN26
WN3 WN19 WN4 WN 7 WN8 WN9
WN1 WN22
WN20 WN25
WN18 WN27
WN5 WN 16 WN6 WN23
WN12
WN17 , WN 11 WN10, WN14, WN21, WN24
CSE391 – 2005 NLP12
WordNet: - call, 28 senses, Senseval2 groups (engineering!)
WN5, WN16,WN12 WN15 WN26
WN3 WN19 WN4 WN 7 WN8 WN9
WN1 WN22
WN20 WN25
WN18 WN27
WN2 WN 13 WN6 WN23
WN28
WN17 , WN 11 WN10, WN14, WN21, WN24,
Loud cry
Label
Phone/radio
Bird or animal cry
Request
Call a loan/bond
Visit
Challenge
Bid
CSE391 – 2005 NLP13
Grouping improved scores: ITA 82%, MaxEnt WSD 69%
• Call: 31% of errors due to confusion between senses within same group 1:– name, call -- (assign a specified, proper name to; They
named their son David)– call -- (ascribe a quality to or give a name of a common
noun that reflects a quality; He called me a bastard)– call -- (consider or regard as being;I would not call her
beautiful)
– 75% with training and testing on grouped senses vs.– 43% with training and testing on fine-grained senses
Palmer, Dang, Fellbaum,, submitted, NLE
CSE391 – 2005 NLP14
Proposition Bank:From Sentences to Propositions
(Predicates!)
Powell met Zhu Rongji
Proposition: meet(Powell, Zhu Rongji)Powell met with Zhu Rongji
Powell and Zhu Rongji met
Powell and Zhu Rongji had a meeting
. . .When Powell met Zhu Rongji on Thursday they discussed the return of the spy plane.
meet(Powell, Zhu) discuss([Powell, Zhu], return(X, plane))
debate
consult
joinwrestle
battle
meet(Somebody1, Somebody2)
CSE391 – 2005 NLP15
Capturing semantic roles*
• Richard broke [ ARG1 the laser pointer.]
• [ARG1 The windows] were broken by the hurricane.
• [ARG1 The vase] broke into pieces when it toppled over.
SUBJ
SUBJ
SUBJ
*See also Framenet, http://www.icsi.berkeley.edu/~framenet/
CSE391 – 2005 NLP16
Word Senses in PropBank• Orders to ignore word sense not feasible for 700+ verbs
– Mary left the room– Mary left her daughter-in-law her pearls in her will
Frameset leave.01 "move away from":Arg0: entity leavingArg1: place left
Frameset leave.02 "give":Arg0: giver Arg1: thing givenArg2: beneficiary
How do these relate to traditional word senses in VerbNet and WordNet?
CSE391 – 2005 NLP17
WordNet: - call, 28 senses, groups
WN5, WN16,WN12 WN15 WN26
WN3 WN19 WN4 WN 7 WN8 WN9
WN1 WN22
WN20 WN25
WN18 WN27
WN2 WN 13 WN6 WN23
WN28
WN17 , WN 11 WN10, WN14, WN21, WN24,
Loud cry
Label
Phone/radio
Bird or animal cry
Request
Call a loan/bond
Visit
Challenge
Bid
CSE391 – 2005 NLP18
Overlap with PropBank Framesets
WN5, WN16,WN12 WN15 WN26
WN3 WN19 WN4 WN 7 WN8 WN9
WN1 WN22
WN20 WN25
WN18 WN27
WN2 WN 13 WN6 WN23
WN28
WN17 , WN 11 WN10, WN14, WN21, WN24,
Loud cry
Label
Phone/radio
Bird or animal cry
Request
Call a loan/bond
Visit
Challenge
Bid
CSE391 – 2005 NLP19
Overlap between Senseval2Groups and Framesets – 95%
WN1 WN2 WN3 WN4
WN6 WN7 WN8 WN5 WN 9 WN10
WN11 WN12 WN13 WN 14
WN19 WN20
Frameset1
Frameset2
developPalmer, Babko-Malaya,Dang SNLU 2004
CSE391 – 2005 NLP20
Sense Hierarchy • PropBank Framesets – coarse grained distinctions
20 Senseval 2 verbs w/ > 1 FramesetMaxent WSD system, 73.5% baseline, 90% accuracy
– Sense Groups (Senseval-2) intermediate level (includes Levin classes) – 69%
• WordNet – fine grained distinctions, 60.2%
CSE391 – 2005 NLP21
Maximum Entropy WSDHoa Dang, best performer on Verbs
• Maximum entropy framework, p(sense|context)
• Contextual Linguistic Features– Topical feature for W: +2.5%,
• keywords (determined automatically)
– Local syntactic features for W: +1.5 to +5%, • presence of subject, complements, passive?• words in subject, complement positions, particles, preps,
etc.
– Local semantic features for W: +6% • Semantic class info from WordNet (synsets, etc.)• Named Entity tag (PERSON, LOCATION,..) for proper
Ns• words within +/- 2 word window
CSE391 – 2005 NLP22
Context Sensitivity
• Programming languages are Context Free
• Natural languages are Context Sensitive?– Movement– Features– respectivelyJohn, Mary and Bill ate peaches, pears and apples,
respectively.
CSE391 – 2005 NLP23
The Chomsky Grammar Hierarchy
• Regular grammars, aabbbb S → aS | nil | bS
• Context free grammars, aaabbb S → aSb | nil
• Context sensitive grammars, aaabbbccc xSy → xby
• Turing Machines
CSE391 – 2005 NLP24
Recursive transition nets for CFGs
• s :- np,vp.• np:- pronoun; noun; det,adj, noun; np,pp.
S1 S2 S3
NP VP
S
S4 S5 S6
det noun
NPnoun
pronoun
pp
adj
CSE391 – 2005 NLP25
Most parsers are Turing Machines• To give a more natural and
comprehensible treatment of movement
• For a more efficient treatment of features
• Not because of respectively – most parsers can’t handle it.
CSE391 – 2005 NLP26
Nested Dependencies and Crossing Dependencies
John, Mary and Bill ate peaches, pears and apples, respectively
The dog chased the cat that bit the mouse that ran.
The mouse the cat the dog chased bit ran.
CF
CF
CS
CSE391 – 2005 NLP27
Movement
What did John give to Mary?
*Where did John give to Mary?
John gave cookies to Mary.
John gave <what> to Mary.
CSE391 – 2005 NLP28
Handling Movement:Hold registers/Slash Categories
• S :- Wh, S/NP
• S/NP :- VP
• S/NP :- NP VP/NP
• VP/NP :- Verb
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