Natural Language Processing
•
•
•
•
•
•

Machine Translation
Predicate argument structures
Syntactic parses
Lexical Semant...
Machine Translation
• One of the first applications for computers
– bilingual dictionary > word-word translation

• Good t...
Example translation

CSE391 – 2005

3

NLP
Translation Issues:
Korean to English
- Word order
- Dropped arguments
- Lexical ambiguities
- Structure vs morphology

CS...
Common Thread
• Predicate-argument structure
– Basic constituents of the sentence and how
they are related to each other

...
Abstracting away from surface
structure

CSE391 – 2005

6

NLP
Transfer lexicons

CSE391 – 2005

7

NLP
Machine Translation Lexical Choice- Word
Sense Disambiguation

Iraq lost the battle.
Ilakuka centwey ciessta.
[Iraq ] [bat...
Natural Language Processing
• Syntax
– Grammars, parsers, parse trees,
dependency structures

• Semantics
– Subcategorizat...
Syntactic Categories
• Nouns, pronouns, Proper nouns
• Verbs, intransitive verbs, transitive verbs,
ditransitive verbs (su...
Syntactic Parsing
• The cat sat on the mat.
Det Noun Verb Prep Det Noun

• Time flies like an arrow.
Noun Verb

Prep Det N...
Parses
The cat sat on the mat

S
NP
Det
the

VP
N
cat

PP

V
sat
Prep
on

CSE391 – 2005

12

NP
Det
the

NLP

N
mat
Parses
Time flies like an arrow.

S
NP
VP
N
time

V
flies

PP
Prep
like

CSE391 – 2005

13

NP
Det
an

N
arrow
NLP
Parses
Time flies like an arrow.

S
NP
N
time

N
flies

VP
V
like

NP
Det
an

CSE391 – 2005

14

N
arrow

NLP
Recursive transition nets for CFGs
NP

S

S1

VP
S2

pp

det

NP

S4

S3
noun

noun

S5

adj

S6

pronoun

• s :- np,vp.
•...
Lexicon
noun(cat).

noun(flies).

noun(mat).

noun(time).

det(the).

noun(arrow).

det(a).

det(an).

verb(sat).

verb(fl...
Lexicon with Roots
noun(cat,cat).

noun(flies,fly).

noun(mat,mat).

noun(time,time).

det(the,the)

noun(arrow,arrow).

d...
Parses
The old can can hold the water.

S
NP

VP
det
the

adj
old

N
can

CSE391 – 2005

aux
can V
hold

18

NP
det
the

N...
Structural ambiguities
• That factory can can tuna.
• That factory cans cans of tuna and salmon.
• Have the students in cs...
Lexicon
The old can can hold the water.
Noun(can,can)

Verb(hold,hold)

Noun(cans,can)

Verb(holds,hold)

Noun(water,water...
Simple Context Free Grammar in BNF
notation
S
NP

→
→

NP VP
Pronoun | Noun | Det Adj Noun |NP PP

PP

→

Prep NP

V
VP

→...
Top-down parse in progress
[The, old, can, can, hold, the, water]

S → NP VP
NP → NP?
NP → Pronoun?
Pronoun? fail
NP → Nou...
Top-down parse in progress
[can, hold, the, water]

VP → VP?
V → Verb?
Verb? fail
V → Aux Verb?
Aux? can
Verb? hold
succee...
Top-down parse in progress
[can, hold, the, water]
VP → VP NP
V → Verb?
Verb? fail
V → Aux Verb?
Aux? can
Verb? hold
NP → ...
Lexicon
Noun(can,can)

Verb(hold,hold)

Noun(cans,can)

Verb(holds,hold)

Noun(water,water)

Aux(can,can)

Noun(hold,hold)...
Top-down parse in progress
[can, hold, the, water]
VP → V NP?
V → Verb?
Verb? fail
V → Aux Verb?
Aux? can
Verb? hold
NP → ...
Lexicon
Noun(can,can)

Verb(hold,hold)

Noun(cans,can)

Verb(holds,hold)

Noun(water,water)

Aux(can,can)

Noun(hold,hold)...
Top-down approach
• Start with goal of sentence
S → NP VP
S → Wh-word Aux NP VP

• Will try to find an NP 4 different ways...
Bottom-up approach
• Start with words in sentence.
• What structures do they correspond to?
• Once a structure is built, k...
Bottom-up parse in progress

det adj noun
The old
can
det noun

CSE391 – 2005

aux
can

verb det
hold the

noun.
water.

a...
Bottom-up parse in progress

det adj noun
The old
can
det noun

CSE391 – 2005

aux
can

verb det
hold the

noun.
water.

a...
Bottom-up parse in progress

det adj noun
The old
can
det noun

CSE391 – 2005

aux
can

verb det
hold the

noun.
water.

a...
Top-down vs. Bottom-up
• Helps with POS
ambiguities – only
consider relevant
POS
• Rebuilds the same
structure repeatedly
...
Hybrid approach
• Top-down with a chart
• Use look ahead and heuristics to pick
most likely sentence type
• Use probabilit...
Features
• C for Case, Subjective/Objective
– She visited her.

• P for Person agreement, (1st, 2nd, 3rd)
– I like him, Yo...
Example Lexicon Entries
Using Features:
Case, Number, Gender, Person
pronoun(subj, sing, fem, third, she, she).
pronoun(ob...
Language to Logic
How do we get there?
• John went to the book store.
∃ John ∃ store1, go(John, store1)

• John bought a b...
Lexical Semantics
Same event - different sentences
John broke the window with a hammer.
John broke the window with the cra...
Same event - different syntactic frames
John broke the window with a hammer.
SUBJ VERB

OBJ

MODIFIER

John broke the wind...
Semantics -predicate arguments
break(AGENT, INSTRUMENT, PATIENT)
AGENT
PATIENT
INSTRUMENT
John broke the window with a ham...
AGENT

PATIENT

INSTRUMENT

John broke the window with a hammer.
SUBJ

OBJ

INSTRUMENT

MODIFIER
PATIENT

The hammer broke...
Constraint Satisfaction
break (Agent: animate,
Instrument: tool,
Patient: physical-object)
Agent
<=> subj
Instrument <=> s...
Syntax/semantics interaction
• Parsers will produce syntactically valid
parses for semantically anomalous
sentences
• Lexi...
Constraint Satisfaction
give

(Agent: animate,
Patient: physical-object
Recipient: animate)

Agent
<=> subj
Patient
<=> ob...
Subcategorization Frequencies
• The women kept the dogs on the beach.
– Where keep? Keep on beach 95%
• NP XP 81%

– Which...
Reading times
• NP-bias (slower times to bold word)
The waiter confirmed the reservation was
made yesterday.
The defendant...
Reading times
• S-bias (no slower times to bold word)
The waiter insisted the reservation was
made yesterday.
The defendan...
Probabilistic Context Free
Grammars
• Adding probabilities
• Lexicalizing the probabilities

CSE391 – 2005

48

NLP
Simple Context Free Grammar in BNF
S
NP

→
→

PP
V

→
→

VP

→

CSE391 – 2005

NP VP
Pronoun
| Noun
| Det Adj Noun
|NP PP
...
Simple Probabilistic CFG
S
NP

→
→

PP
V

→
→

VP

→

CSE391 – 2005

NP VP
Pronoun
| Noun
| Det Adj Noun
|NP PP
Prep NP
Ve...
Simple Probabilistic Lexicalized CFG
S
NP

→
→

PP
V

→
→

VP

→

CSE391 – 2005

NP VP
Pronoun
| Noun
| Det Adj Noun
|NP P...
Simple Probabilistic Lexicalized CFG
VP

→

V
| V NP
| V NP NP
| V NP PP
| VP PP

[0.30]
[0.60] {break,split,crack..}
[0.0...
A TreeBanked Sentence
(S (NP-SBJ Analysts)
(VP have
(VP been
VP
(VP expecting
(NP (NP a GM-Jaguar pact)
have VP
(SBAR (WHN...
The same sentence, PropBanked
(S Arg0 (NP-SBJ Analysts)
(VP have
(VP been
Arg1
(VP expecting
Arg1 (NP (NP a GM-Jaguar pact...
Headlines
• Police Begin Campaign To Run Down Jaywalkers
• Iraqi Head Seeks Arms
• Teacher Strikes Idle Kids
• Miners Refu...
Events
• From KRR lecture

CSE391 – 2005

56

NLP
Context Sensitivity
• Programming languages are Context Free
• Natural languages are Context Sensitive?
– Movement
– Featu...
The Chomsky Grammar Hierarchy
• Regular grammars, aabbbb
S → aS | nil | bS

• Context free grammars, aaabbb
S →

aSb | nil...
Recursive transition nets for CFGs
NP

S

S1

VP
S2

pp

det

NP

S4

S3
noun

noun

S5

adj

S6

pronoun

• s :- np,vp.
•...
Most parsers are Turing Machines
• To give a more natural and
comprehensible treatment of movement
• For a more efficient ...
Nested Dependencies and
Crossing Dependencies
CF The dog chased the cat that bit the mouse that ran.

CF The mouse the cat...
Movement
What did John give to Mary?
*Where did John give to Mary?
John gave cookies to Mary.
John gave <what> to Mary.
CS...
Handling Movement:
Hold registers/Slash Categories
• S

:- Wh, S/NP

• S/NP
• S/NP

:- VP
:- NP VP/NP

• VP/NP :- Verb

CS...
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Nlp.set

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Nlp.set

  1. 1. Natural Language Processing • • • • • • Machine Translation Predicate argument structures Syntactic parses Lexical Semantics Probabilistic Parsing Ambiguities in sentence interpretation CSE391 – 2005 1 NLP
  2. 2. Machine Translation • One of the first applications for computers – bilingual dictionary > word-word translation • Good translation requires understanding! – War and Peace, The Sound and The Fury? • What can we do? Sublanguages. – technical domains, static vocabulary – Meteo in Canada, Caterpillar Tractor Manuals, Botanical descriptions, Military Messages CSE391 – 2005 2 NLP
  3. 3. Example translation CSE391 – 2005 3 NLP
  4. 4. Translation Issues: Korean to English - Word order - Dropped arguments - Lexical ambiguities - Structure vs morphology CSE391 – 2005 4 NLP
  5. 5. Common Thread • Predicate-argument structure – Basic constituents of the sentence and how they are related to each other • Constituents – John, Mary, the dog, pleasure, the store. • Relations – Loves, feeds, go, to, bring CSE391 – 2005 5 NLP
  6. 6. Abstracting away from surface structure CSE391 – 2005 6 NLP
  7. 7. Transfer lexicons CSE391 – 2005 7 NLP
  8. 8. Machine Translation Lexical Choice- Word Sense Disambiguation Iraq lost the battle. Ilakuka centwey ciessta. [Iraq ] [battle] [lost]. John lost his computer. John-i computer-lul ilepelyessta. [John] [computer] [misplaced]. CSE391 – 2005 8 NLP
  9. 9. Natural Language Processing • Syntax – Grammars, parsers, parse trees, dependency structures • Semantics – Subcategorization frames, semantic classes, ontologies, formal semantics • Pragmatics – Pronouns, reference resolution, discourse models CSE391 – 2005 9 NLP
  10. 10. Syntactic Categories • Nouns, pronouns, Proper nouns • Verbs, intransitive verbs, transitive verbs, ditransitive verbs (subcategorization frames) • Modifiers, Adjectives, Adverbs • Prepositions • Conjunctions CSE391 – 2005 10 NLP
  11. 11. Syntactic Parsing • The cat sat on the mat. Det Noun Verb Prep Det Noun • Time flies like an arrow. Noun Verb Prep Det Noun • Fruit flies like a banana. Noun Noun CSE391 – 2005 Verb Det Noun 11 NLP
  12. 12. Parses The cat sat on the mat S NP Det the VP N cat PP V sat Prep on CSE391 – 2005 12 NP Det the NLP N mat
  13. 13. Parses Time flies like an arrow. S NP VP N time V flies PP Prep like CSE391 – 2005 13 NP Det an N arrow NLP
  14. 14. Parses Time flies like an arrow. S NP N time N flies VP V like NP Det an CSE391 – 2005 14 N arrow NLP
  15. 15. Recursive transition nets for CFGs NP S S1 VP S2 pp det NP S4 S3 noun noun S5 adj S6 pronoun • s :- np,vp. • np:- pronoun; noun; det,adj, noun; np,pp. CSE391 – 2005 15 NLP
  16. 16. Lexicon noun(cat). noun(flies). noun(mat). noun(time). det(the). noun(arrow). det(a). det(an). verb(sat). verb(flies). prep(on). verb(time). prep(like). CSE391 – 2005 16 NLP
  17. 17. Lexicon with Roots noun(cat,cat). noun(flies,fly). noun(mat,mat). noun(time,time). det(the,the) noun(arrow,arrow). det(a,a). det(an,an). verb(sat,sit). verb(flies,fly). prep(on,on). verb(time,time). prep(like,like). CSE391 – 2005 17 NLP
  18. 18. Parses The old can can hold the water. S NP VP det the adj old N can CSE391 – 2005 aux can V hold 18 NP det the N water NLP
  19. 19. Structural ambiguities • That factory can can tuna. • That factory cans cans of tuna and salmon. • Have the students in cse91 finish the exam in 212. • Have the students in cse91 finished the exam in 212? CSE391 – 2005 19 NLP
  20. 20. Lexicon The old can can hold the water. Noun(can,can) Verb(hold,hold) Noun(cans,can) Verb(holds,hold) Noun(water,water) Aux(can,can) Noun(hold,hold) Adj(old,old) Noun(holds,hold) Det(the,the) CSE391 – 2005 20 NLP
  21. 21. Simple Context Free Grammar in BNF notation S NP → → NP VP Pronoun | Noun | Det Adj Noun |NP PP PP → Prep NP V VP → → Verb | Aux Verb V | V NP | V NP NP | V NP PP | VP PP CSE391 – 2005 21 NLP
  22. 22. Top-down parse in progress [The, old, can, can, hold, the, water] S → NP VP NP → NP? NP → Pronoun? Pronoun? fail NP → Noun? Noun? fail NP → Det Adj Noun? Det? the ADJ?old Noun? Can Succeed. Succeed. VP? CSE391 – 2005 22 NLP
  23. 23. Top-down parse in progress [can, hold, the, water] VP → VP? V → Verb? Verb? fail V → Aux Verb? Aux? can Verb? hold succeed succeed fail [the, water] CSE391 – 2005 23 NLP
  24. 24. Top-down parse in progress [can, hold, the, water] VP → VP NP V → Verb? Verb? fail V → Aux Verb? Aux? can Verb? hold NP → Pronoun? Pronoun? fail NP → Noun? Noun? fail NP → Det Adj Noun? Det? the ADJ? fail CSE391 – 2005 24 NLP
  25. 25. Lexicon Noun(can,can) Verb(hold,hold) Noun(cans,can) Verb(holds,hold) Noun(water,water) Aux(can,can) Noun(hold,hold) Adj(old,old) Noun(holds,hold) Adj( , ) Det(the,the) CSE391 – 2005 25 NLP
  26. 26. Top-down parse in progress [can, hold, the, water] VP → V NP? V → Verb? Verb? fail V → Aux Verb? Aux? can Verb? hold NP → Pronoun? Pronoun? fail NP → Noun? Noun? fail NP → Det Adj Noun? Det? the ADJ? Noun? water SUCCEED SUCCEED CSE391 – 2005 26 NLP
  27. 27. Lexicon Noun(can,can) Verb(hold,hold) Noun(cans,can) Verb(holds,hold) Noun(water,water) Aux(can,can) Noun(hold,hold) Adj(old,old) Noun(holds,hold) Adj( , ) Det(the,the) Noun(old,old) CSE391 – 2005 27 NLP
  28. 28. Top-down approach • Start with goal of sentence S → NP VP S → Wh-word Aux NP VP • Will try to find an NP 4 different ways before trying a parse where the verb comes first. • What does this remind you of? – search • What would be better? CSE391 – 2005 28 NLP
  29. 29. Bottom-up approach • Start with words in sentence. • What structures do they correspond to? • Once a structure is built, kept on a CHART. CSE391 – 2005 29 NLP
  30. 30. Bottom-up parse in progress det adj noun The old can det noun CSE391 – 2005 aux can verb det hold the noun. water. aux/verb noun/verb noun det noun. 30 NLP
  31. 31. Bottom-up parse in progress det adj noun The old can det noun CSE391 – 2005 aux can verb det hold the noun. water. aux/verb noun/verb noun det noun. 31 NLP
  32. 32. Bottom-up parse in progress det adj noun The old can det noun CSE391 – 2005 aux can verb det hold the noun. water. aux/verb noun/verb noun det noun. 32 NLP
  33. 33. Top-down vs. Bottom-up • Helps with POS ambiguities – only consider relevant POS • Rebuilds the same structure repeatedly • Spends a lot of time on impossible parses CSE391 – 2005 • Has to consider every POS • Builds each structure once • Spends a lot of time on useless structures What would be better? 33 NLP
  34. 34. Hybrid approach • Top-down with a chart • Use look ahead and heuristics to pick most likely sentence type • Use probabilities for pos tagging, pp attachments, etc. CSE391 – 2005 34 NLP
  35. 35. Features • C for Case, Subjective/Objective – She visited her. • P for Person agreement, (1st, 2nd, 3rd) – I like him, You like him, He likes him, • N for Number agreement, Subject/Verb – He likes him, They like him. • G for Gender agreement, Subject/Verb – English, reflexive pronouns He washed himself. – Romance languages, det/noun • T for Tense, – auxiliaries, sentential complements, etc. – * will finished is bad CSE391 – 2005 35 NLP
  36. 36. Example Lexicon Entries Using Features: Case, Number, Gender, Person pronoun(subj, sing, fem, third, she, she). pronoun(obj, sing, fem, third, her, her). pronoun(obj, Num, Gender, second, you, you). pronoun(subj, sing, Gender, first, I, I). noun(Case, plural, Gender, third, flies,fly). CSE391 – 2005 36 NLP
  37. 37. Language to Logic How do we get there? • John went to the book store. ∃ John ∃ store1, go(John, store1) • John bought a book. buy(John,book1) • John gave the book to Mary. give(John,book1,Mary) • Mary put the book on the table. put(Mary,book1,table1) CSE391 – 2005 37 NLP
  38. 38. Lexical Semantics Same event - different sentences John broke the window with a hammer. John broke the window with the crack. The hammer broke the window. The window broke. CSE391 – 2005 38 NLP
  39. 39. Same event - different syntactic frames John broke the window with a hammer. SUBJ VERB OBJ MODIFIER John broke the window with the crack. SUBJ VERB OBJ MODIFIER The hammer broke the window. SUBJ VERB OBJ The window broke. SUBJ VERB CSE391 – 2005 39 NLP
  40. 40. Semantics -predicate arguments break(AGENT, INSTRUMENT, PATIENT) AGENT PATIENT INSTRUMENT John broke the window with a hammer. INSTRUMENT PATIENT The hammer broke the window. PATIENT The window broke. Fillmore 68 - The case for case CSE391 – 2005 40 NLP
  41. 41. AGENT PATIENT INSTRUMENT John broke the window with a hammer. SUBJ OBJ INSTRUMENT MODIFIER PATIENT The hammer broke the window. SUBJ OBJ PATIENT The window broke. SUBJ CSE391 – 2005 41 NLP
  42. 42. Constraint Satisfaction break (Agent: animate, Instrument: tool, Patient: physical-object) Agent <=> subj Instrument <=> subj, with-pp Patient <=> obj, subj ACL81,ACL85,ACL86,MT90,CUP90,AIJ93 CSE391 – 2005 42 NLP
  43. 43. Syntax/semantics interaction • Parsers will produce syntactically valid parses for semantically anomalous sentences • Lexical semantics can be used to rule them out CSE391 – 2005 43 NLP
  44. 44. Constraint Satisfaction give (Agent: animate, Patient: physical-object Recipient: animate) Agent <=> subj Patient <=> object Recipient <=> indirect-object, to-pp CSE391 – 2005 44 NLP
  45. 45. Subcategorization Frequencies • The women kept the dogs on the beach. – Where keep? Keep on beach 95% • NP XP 81% – Which dogs? Dogs on beach 5% • NP 19% • The women discussed the dogs on the beach. – Where discuss? Discuss on beach 10% • NP PP 24% – Which dogs? Dogs on beach 90% • NP 76% CSE391 – 2005 Ford, Bresnan, Kaplan 82, Jurafsky 98, Roland,Jurafsky 99 45 NLP
  46. 46. Reading times • NP-bias (slower times to bold word) The waiter confirmed the reservation was made yesterday. The defendant accepted the verdict would be decided soon. CSE391 – 2005 46 NLP
  47. 47. Reading times • S-bias (no slower times to bold word) The waiter insisted the reservation was made yesterday. The defendant wished the verdict would be decided soon. CSE391 – 2005 Trueswell, Tanenhaus and Kello, 93 47 Trueswell and Kim 98NLP
  48. 48. Probabilistic Context Free Grammars • Adding probabilities • Lexicalizing the probabilities CSE391 – 2005 48 NLP
  49. 49. Simple Context Free Grammar in BNF S NP → → PP V → → VP → CSE391 – 2005 NP VP Pronoun | Noun | Det Adj Noun |NP PP Prep NP Verb | Aux Verb V | V NP | V NP NP | V NP PP | VP PP 49 NLP
  50. 50. Simple Probabilistic CFG S NP → → PP V → → VP → CSE391 – 2005 NP VP Pronoun | Noun | Det Adj Noun |NP PP Prep NP Verb | Aux Verb V | V NP | V NP NP | V NP PP | VP PP 50 [0.10] [0.20] [0.50] [0.20] [1.00] [0.20] [0.20] [0.10] [0.40] [0.10] [0.20] [0.20] NLP
  51. 51. Simple Probabilistic Lexicalized CFG S NP → → PP V → → VP → CSE391 – 2005 NP VP Pronoun | Noun | Det Adj Noun |NP PP Prep NP Verb | Aux Verb V | V NP | V NP NP | V NP PP | VP PP 51 [0.10] [0.20] [0.50] [0.20] [1.00] [0.20] [0.20] [0.87] {sleep, cry, laugh} [0.03] [0.00] [0.00] [0.10] NLP
  52. 52. Simple Probabilistic Lexicalized CFG VP → V | V NP | V NP NP | V NP PP | VP PP [0.30] [0.60] {break,split,crack..} [0.00] [0.00] [0.10] VP → V | V NP | V NP NP | V NP PP | VP PP [0.10] [0.40] [0.10] [0.20] [0.20] CSE391 – 2005 52 what about leave? leave1, leave2? NLP
  53. 53. A TreeBanked Sentence (S (NP-SBJ Analysts) (VP have (VP been VP (VP expecting (NP (NP a GM-Jaguar pact) have VP (SBAR (WHNP-1 that) (S (NP-SBJ *T*-1) NP-SBJ been VP (VP would Analyst (VP give expectingNP s (NP the U.S. car maker) SBAR (NP (NP an eventual (ADJP 30 %) stake) NP S (PP-LOC in (NP the British company)))))))))))) a GM-Jaguar WHNP-1 VP pact that NP-SBJ VP *T*-1 would NP give PP-LOC NP Analysts have been expecting a GM-Jaguar NP the US car pact that would give the U.S. car maker an NP an eventual maker eventual 30% stake in the British company. in the British 30% stake company S CSE391 – 2005 53 NLP
  54. 54. The same sentence, PropBanked (S Arg0 (NP-SBJ Analysts) (VP have (VP been Arg1 (VP expecting Arg1 (NP (NP a GM-Jaguar pact) (SBAR (WHNP-1 that) (S Arg0 (NP-SBJ *T*-1) a GM-Jaguar (VP would pact (VP give Arg2 (NP the U.S. car maker) Arg1 (NP (NP an eventual (ADJP 30 %) stake) (PP-LOC in (NP the British company)))))))))))) have been expecting Arg0 Analyst s Arg0 *T*-1 that would give Arg2 Arg1 an eventual 30% stake in the British company the US car maker CSE391 – 2005 expect(Analysts, GM-J pact) give(GM-J pact, US car maker, 30% stake) 54 NLP
  55. 55. Headlines • Police Begin Campaign To Run Down Jaywalkers • Iraqi Head Seeks Arms • Teacher Strikes Idle Kids • Miners Refuse To Work After Death • Juvenile Court To Try Shooting Defendant CSE391 – 2005 55 NLP
  56. 56. Events • From KRR lecture CSE391 – 2005 56 NLP
  57. 57. Context Sensitivity • Programming languages are Context Free • Natural languages are Context Sensitive? – Movement – Features – respectively John, Mary and Bill ate peaches, pears and apples, respectively. CSE391 – 2005 57 NLP
  58. 58. 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 58 NLP
  59. 59. Recursive transition nets for CFGs NP S S1 VP S2 pp det NP S4 S3 noun noun S5 adj S6 pronoun • s :- np,vp. • np:- pronoun; noun; det,adj, noun; np,pp. CSE391 – 2005 59 NLP
  60. 60. 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 60 NLP
  61. 61. Nested Dependencies and Crossing Dependencies CF The dog chased the cat that bit the mouse that ran. CF The mouse the cat the dog chased bit ran. CS John, Mary and Bill ate peaches, pears and apples, respectively CSE391 – 2005 61 NLP
  62. 62. 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 62 NLP
  63. 63. Handling Movement: Hold registers/Slash Categories • S :- Wh, S/NP • S/NP • S/NP :- VP :- NP VP/NP • VP/NP :- Verb CSE391 – 2005 63 NLP

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