cs626-449: speech, nlp and the web/topics in ai

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CS626-449: Speech, NLP and the Web/Topics in AI Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture 13: Deeper Adjective and PP Structure; Structural Ambiguity and Parsing

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CS626-449: Speech, NLP and the Web/Topics in AI. Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture 13: Deeper Adjective and PP Structure; Structural Ambiguity and Parsing. Types of Grammar. Prescriptive Grammar Taught in schools Emphasis is on usage Descriptive Grammar - PowerPoint PPT Presentation

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Page 1: CS626-449: Speech, NLP and the Web/Topics in AI

CS626-449: Speech, NLP and the Web/Topics in AI

Pushpak BhattacharyyaCSE Dept., IIT Bombay

Lecture 13: Deeper Adjective and PP Structure; Structural Ambiguity and

Parsing

Page 2: CS626-449: Speech, NLP and the Web/Topics in AI

Types of Grammar

Prescriptive Grammar Taught in schools Emphasis is on usage

Descriptive Grammar Also known as Linguistic Grammar Describes Language

Page 3: CS626-449: Speech, NLP and the Web/Topics in AI

Types of Languages SVO

Subject – Verb – Object English E.g. Ram likes music. S V O

SOV Subject- Object-Verb Indian Languages E.g. रा�म पा�नी� पा� राहा� हा� | S O V

Page 4: CS626-449: Speech, NLP and the Web/Topics in AI

More deeply embedded structureNP

PP

AP

big

The

of poems

with the blue cover

N’1

Nbook

PP

N’2

N’3

Page 5: CS626-449: Speech, NLP and the Web/Topics in AI

Bar-level projections Add intermediate structures

NP (D) N’ N’ (AP) N’ | N’ (PP) | N (PP)

() indicates optionality

Page 6: CS626-449: Speech, NLP and the Web/Topics in AI

New rules produce this treeNP

PP

AP

big

The

of poems

with the blue cover

N’1

Nbook

PP

N’2

N’3

N-bar

Page 7: CS626-449: Speech, NLP and the Web/Topics in AI

As opposed to this tree

NP

PPAP

big

The

of poems

with the blue coverbook

PP

Page 8: CS626-449: Speech, NLP and the Web/Topics in AI

V-bar

What is the element in verbs corresponding to one-replacement for nouns

do-so or did-so

Page 9: CS626-449: Speech, NLP and the Web/Topics in AI

I [eat beans with a fork]

VP

NP

beans

eat

with a fork

PP

No constituent that groups together V and NP and excludesPP

Page 10: CS626-449: Speech, NLP and the Web/Topics in AI

Need for intermediate constituents

I [eat beans] with a fork but Ram [does so] with a spoon

V2’

NP

beans

eat

with a fork

PP

VP

V1’

V

VPV’V’ V’ (PP)V’ V (NP)

Page 11: CS626-449: Speech, NLP and the Web/Topics in AI

How to target V1’

I [eat beans with a fork], and Ram [does so] too.

V2’

NP

beans

eat

with a fork

PP

VP

V1’

V

VPV’V’ V’ (PP)V’ V (NP)

Page 12: CS626-449: Speech, NLP and the Web/Topics in AI

Case of conjunction

V3’

NP

beans

eat

In the afternoon

PP

VP

V1’

V

V4’

NP

coffee

drink

V

V2’

Conjand

Page 13: CS626-449: Speech, NLP and the Web/Topics in AI

A-bar: adjectives

A3’

A4’

blue

Very

AP

bright

A5’

A6’

green

A1’

AP

A2’

Conjand

AP AP

dull

AP A’A’ (AP) A’A’ A (PP)

Page 14: CS626-449: Speech, NLP and the Web/Topics in AI

So-replacement for adjectives

Ram is very serious about studies , but less so than Shyam

Page 15: CS626-449: Speech, NLP and the Web/Topics in AI

P-bar: prepositions

P2’

NP

the table

right

AP

off

P3’

NP

the trash

A1’

AP

P1’

Conjand

P P

into

PP P’P’ P’ (PP)P’ P (NP)

PP

Page 16: CS626-449: Speech, NLP and the Web/Topics in AI

So-replacement for Prepositions

Ram is utterly in debt, but Shyam is only partly so.

Page 17: CS626-449: Speech, NLP and the Web/Topics in AI

Complements and Adjuncts orArguments and Adjuncts

Page 18: CS626-449: Speech, NLP and the Web/Topics in AI

Rules in bar notation: Noun

NP (D) N’ N’ (AP) N’ N’ N’ (PP) N’ N (PP)

Page 19: CS626-449: Speech, NLP and the Web/Topics in AI

Rules in bar notation: Verb

VP V’ V’ V’ (PP) V’ V (NP)

Page 20: CS626-449: Speech, NLP and the Web/Topics in AI

Rules in bar notation: Adjective

AP A’ A’ (AP) A’ A’ A (PP)

Page 21: CS626-449: Speech, NLP and the Web/Topics in AI

Rules in bar notation: Preposition

PP P’ P’ P’ (PP) P’ P (NP)

Page 22: CS626-449: Speech, NLP and the Web/Topics in AI

Introducing the “X factor”

Let X stand for any category N, V, A, P

Let XP stand for NP, VP, AP and PP Let X’ stand for N’, V’, A’ and P’

Page 23: CS626-449: Speech, NLP and the Web/Topics in AI

XP to X’

Collect the first level rules NP (D) N’ VP V’ AP A’ PP P’

And produce XP (YP) X’

Page 24: CS626-449: Speech, NLP and the Web/Topics in AI

X’ to X’

Collect the 2nd level rules N’ (AP) N’ or N’ (PP) V’ V’ (PP) A’ (AP) A’ P’ P’ (PP)

And produce X’ (ZP) X’ or X (ZP)

Page 25: CS626-449: Speech, NLP and the Web/Topics in AI

X’ to X

Collect the 3rd level rules N’ N (PP) V’ V (NP) A’ A (PP) P’ P (NP)

And produce X’ X (WP)

Page 26: CS626-449: Speech, NLP and the Web/Topics in AI

Basic observations about X and X’

X’ X (WP) X’ X’ (ZP) X is called Head Phrases must have Heads:

Headedness property Category of XP and X must match:

Endocentricity

Page 27: CS626-449: Speech, NLP and the Web/Topics in AI

Basic observations about X and X’

X’ X (WP) X’ X’ (ZP) Sisters of X are complements

Roughly correspond to objects Sisters of X’ are Adjuncts

PPs and Adjectives are typical adjuncts We have adjunct rules and

complement rules

Page 28: CS626-449: Speech, NLP and the Web/Topics in AI

Structural difference between complements and adjuncts

X’

WP

Complement

X’

X

ZP

XP

Adjunct

Page 29: CS626-449: Speech, NLP and the Web/Topics in AI

Complements and Adjuncts in NPs

N’

PP

of poems

N’

N

ZP

NP

with red cover

book

Page 30: CS626-449: Speech, NLP and the Web/Topics in AI

Any number of Adjuncts

N’

PP

of poems

N’

N

ZP

N’

with red cover

book

NP

from Oxford Press

Page 31: CS626-449: Speech, NLP and the Web/Topics in AI

Parsing Algorithm

Page 32: CS626-449: Speech, NLP and the Web/Topics in AI

A simplified grammar

S NP VP NP DT N | N VP V ADV | V

Page 33: CS626-449: Speech, NLP and the Web/Topics in AI

A segment of English Grammar

S’(C) S S{NP/S’} VP VP(AP+) (VAUX) V (AP+)

({NP/S’}) (AP+) (PP+) (AP+) NP(D) (AP+) N (PP+) PPP NP AP(AP) A

Page 34: CS626-449: Speech, NLP and the Web/Topics in AI

Example Sentence

People laugh1 2 3

Lexicon:People - N, V Laugh - N, V

These are positions

This indicate that both Noun and Verb is

possible for the word “People”

Page 35: CS626-449: Speech, NLP and the Web/Topics in AI

Top-Down Parsing

State Backup State Action

-----------------------------------------------------------------------------------------------------

1. ((S) 1) - -

2. ((NP VP)1) - -

3a. ((DT N VP)1) ((N VP) 1) -

3b. ((N VP)1) - -

4. ((VP)2) - Consume “People”

5a. ((V ADV)2) ((V)2) -

6. ((ADV)3) ((V)2) Consume “laugh”

5b. ((V)2) - -

6. ((.)3) - Consume “laugh”

Termination Condition : All inputs over. No symbols remaining.

Note: Input symbols can be pushed back.

Position of input pointer

Page 36: CS626-449: Speech, NLP and the Web/Topics in AI

Discussion for Top-Down Parsing This kind of searching is goal driven. Gives importance to textual precedence

(rule precedence). No regard for data, a priori (useless

expansions made).

Page 37: CS626-449: Speech, NLP and the Web/Topics in AI

Bottom-Up Parsing

Some conventions:N12

S1? -> NP12 ° VP2?

Represents positions

End position unknownWork on the LHS done, while the work on RHS remaining

Page 38: CS626-449: Speech, NLP and the Web/Topics in AI

Bottom-Up Parsing (pictorial representation)

S -> NP12 VP23 °

People Laugh 1 2 3

N12 N23

V12 V23

NP12 -> N12 ° NP23 -> N23 °

VP12 -> V12 ° VP23 -> V23 °

S1? -> NP12 ° VP2?

Page 39: CS626-449: Speech, NLP and the Web/Topics in AI

Problem with Top-Down Parsing

• Left Recursion• Suppose you have A-> AB rule. Then we will have the expansion as

follows:• ((A)K) -> ((AB)K) -> ((ABB)K) ……..

Page 40: CS626-449: Speech, NLP and the Web/Topics in AI

Combining top-down and bottom-up strategies

Page 41: CS626-449: Speech, NLP and the Web/Topics in AI

Top-Down Bottom-Up Chart Parsing

Combines advantages of top-down & bottom-up parsing.

Does not work in case of left recursion. e.g. – “People laugh”

People – noun, verb Laugh – noun, verb

Grammar – S NP VPNP DT N | N

VP V ADV | V

Page 42: CS626-449: Speech, NLP and the Web/Topics in AI

Transitive Closure

People laugh

1 2 3

S NP VP NP N VP V

NP DT N S NPVP S NP VP NP N VP V ADV success

VP V

Page 43: CS626-449: Speech, NLP and the Web/Topics in AI

Arcs in Parsing

Each arc represents a chart which records Completed work (left of ) Expected work (right of )

Page 44: CS626-449: Speech, NLP and the Web/Topics in AI

Example

People laugh loudly

1 2 3 4

S NP VP NP N VP V VP V ADVNP DT N S NPVP VP VADV S NP VPNP N VP V ADV S NP VP

VP V

Page 45: CS626-449: Speech, NLP and the Web/Topics in AI

Dealing With Structural Ambiguity

Multiple parses for a sentence The man saw the boy with a telescope. The man saw the mountain with a

telescope. The man saw the boy with the ponytail.

At the level of syntax, all these sentences are ambiguous. But semantics can disambiguate 2nd & 3rd sentence.

Page 46: CS626-449: Speech, NLP and the Web/Topics in AI

Prepositional Phrase (PP) Attachment Problem

V – NP1 – P – NP2

(Here P means preposition)NP2 attaches to NP1 ?

or NP2 attaches to V ?

Page 47: CS626-449: Speech, NLP and the Web/Topics in AI

Parse Trees for a Structurally Ambiguous Sentence

Let the grammar be – S NP VPNP DT N | DT N PPPP P NPVP V NP PP | V NPFor the sentence,“I saw a boy with a telescope”

Page 48: CS626-449: Speech, NLP and the Web/Topics in AI

Parse Tree - 1S

NP VP

N V NP

Det N PP

P NP

Det N

I saw

a boy

with

a telescope

Page 49: CS626-449: Speech, NLP and the Web/Topics in AI

Parse Tree -2S

NP VP

N V NP

Det N

PP

P NP

Det NI saw

a boy with

a telescope

Page 50: CS626-449: Speech, NLP and the Web/Topics in AI

Parsing Structural Ambiguity

Page 51: CS626-449: Speech, NLP and the Web/Topics in AI

Topics to be covered

Dealing with Structural Ambiguity Moving towards Dependency

Parsing

Page 52: CS626-449: Speech, NLP and the Web/Topics in AI

Parsing for Structurally Ambiguous Sentences Sentence “I saw a boy with a telescope” Grammar:

S NP VPNP ART N | ART N PP | PRONVP V NP PP | V NP

ART a | an | theN boy | telescopePRON IV saw

Page 53: CS626-449: Speech, NLP and the Web/Topics in AI

Ambiguous Parses Two possible parses:

PP attached with Verb (i.e. I used a telescope to see)

( S ( NP ( PRON “I” ) ) ( VP ( V “saw” ) ( NP ( (ART “a”) ( N “boy”))( PP (P “with”) (NP ( ART “a” ) ( N

“telescope”))))) PP attached with Noun (i.e. boy had a telescope) ( S ( NP ( PRON “I” ) ) ( VP ( V “saw” )

( NP ( (ART “a”) ( N “boy”) (PP (P “with”) (NP ( ART “a” ) ( N

“telescope”))))))

Page 54: CS626-449: Speech, NLP and the Web/Topics in AI

Top Down ParseState Backup State Action Comments

1 ( ( S ) 1 ) − − Use S NP VP

Page 55: CS626-449: Speech, NLP and the Web/Topics in AI

Top Down ParseState Backup State Action Comments

1 ( ( S ) 1 ) − − Use S NP VP

2 ( ( NP VP ) 1 ) − − Use NP ART N | ART N PP |

PRON

Page 56: CS626-449: Speech, NLP and the Web/Topics in AI

Top Down ParseState Backup State Action Comments

1 ( ( S ) 1 ) − − Use S NP VP

2 ( ( NP VP ) 1 ) − − Use NP ART N | ART N PP |

PRON

3 ( ( ART N VP ) 1 ) (a) ( ( ART N PP VP ) 1 )

(b) ( ( PRON VP ) 1)

− ART does not match “I”,

backup state (b) used

Page 57: CS626-449: Speech, NLP and the Web/Topics in AI

Top Down ParseState Backup State Action Comments

1 ( ( S ) 1 ) − − Use S NP VP

2 ( ( NP VP ) 1 ) − − Use NP ART N | ART N PP |

PRON

3 ( ( ART N VP ) 1 ) (a) ( ( ART N PP VP ) 1 )

(b) ( ( PRON VP ) 1)

− ART does not match “I”,

backup state (b) used

3B

( ( PRON VP ) 1 ) − −

Page 58: CS626-449: Speech, NLP and the Web/Topics in AI

Top Down ParseState Backup State Action Comments

1 ( ( S ) 1 ) − − Use S NP VP

2 ( ( NP VP ) 1 ) − − Use NP ART N | ART N PP |

PRON

3 ( ( ART N VP ) 1 ) (a) ( ( ART N PP VP ) 1 )

(b) ( ( PRON VP ) 1)

− ART does not match “I”,

backup state (b) used

3B

( ( PRON VP ) 1 ) − −

4 ( ( VP ) 2 ) − Consumed “I”

Page 59: CS626-449: Speech, NLP and the Web/Topics in AI

Top Down ParseState Backup State Action Comments

1 ( ( S ) 1 ) − − Use S NP VP

2 ( ( NP VP ) 1 ) − − Use NP ART N | ART N PP |

PRON

3 ( ( ART N VP ) 1 ) (a) ( ( ART N PP VP ) 1 )

(b) ( ( PRON VP ) 1)

− ART does not match “I”,

backup state (b) used

3B

( ( PRON VP ) 1 ) − −

4 ( ( VP ) 2 ) − Consumed “I”

5 ( ( V NP PP ) 2 ) ( ( V NP ) 2 ) − Verb Attachment Rule used

Page 60: CS626-449: Speech, NLP and the Web/Topics in AI

Top Down ParseState Backup State Action Comments

1 ( ( S ) 1 ) − − Use S NP VP

2 ( ( NP VP ) 1 ) − − Use NP ART N | ART N PP |

PRON

3 ( ( ART N VP ) 1 ) (a) ( ( ART N PP VP ) 1 )

(b) ( ( PRON VP ) 1)

− ART does not match “I”,

backup state (b) used

3B

( ( PRON VP ) 1 ) − −

4 ( ( VP ) 2 ) − Consumed “I”

5 ( ( V NP PP ) 2 ) ( ( V NP ) 2 ) − Verb Attachment Rule used

6 ( ( NP PP ) 3 ) − Consumed “saw”

Page 61: CS626-449: Speech, NLP and the Web/Topics in AI

Top Down ParseState Backup State Action Comments

1 ( ( S ) 1 ) − − Use S NP VP

2 ( ( NP VP ) 1 ) − − Use NP ART N | ART N PP |

PRON

3 ( ( ART N VP ) 1 ) (a) ( ( ART N PP VP ) 1 )

(b) ( ( PRON VP ) 1)

− ART does not match “I”,

backup state (b) used

3B

( ( PRON VP ) 1 ) − −

4 ( ( VP ) 2 ) − Consumed “I”

5 ( ( V NP PP ) 2 ) ( ( V NP ) 2 ) − Verb Attachment Rule used

6 ( ( NP PP ) 3 ) − Consumed “saw”

7 ( ( ART N PP ) 3 ) (a) ( ( ART N PP PP ) 3 )

(b) ( ( PRON PP ) 3 )

Page 62: CS626-449: Speech, NLP and the Web/Topics in AI

Top Down ParseState Backup State Action Comments

1 ( ( S ) 1 ) − − Use S NP VP

2 ( ( NP VP ) 1 ) − − Use NP ART N | ART N PP |

PRON

3 ( ( ART N VP ) 1 ) (a) ( ( ART N PP VP ) 1 )

(b) ( ( PRON VP ) 1)

− ART does not match “I”,

backup state (b) used

3B

( ( PRON VP ) 1 ) − −

4 ( ( VP ) 2 ) − Consumed “I”

5 ( ( V NP PP ) 2 ) ( ( V NP ) 2 ) − Verb Attachment Rule used

6 ( ( NP PP ) 3 ) − Consumed “saw”

7 ( ( ART N PP ) 3 ) (a) ( ( ART N PP PP ) 3 )

(b) ( ( PRON PP ) 3 )

8 ( ( N PP) 4 ) − Consumed “a”

Page 63: CS626-449: Speech, NLP and the Web/Topics in AI

Top Down ParseState Backup State Action Comments

1 ( ( S ) 1 ) − − Use S NP VP

2 ( ( NP VP ) 1 ) − − Use NP ART N | ART N PP |

PRON

3 ( ( ART N VP ) 1 ) (a) ( ( ART N PP VP ) 1 )

(b) ( ( PRON VP ) 1)

− ART does not match “I”,

backup state (b) used

3B

( ( PRON VP ) 1 ) − −

4 ( ( VP ) 2 ) − Consumed “I”

5 ( ( V NP PP ) 2 ) ( ( V NP ) 2 ) − Verb Attachment Rule used

6 ( ( NP PP ) 3 ) − Consumed “saw”

7 ( ( ART N PP ) 3 ) (a) ( ( ART N PP PP ) 3 )

(b) ( ( PRON PP ) 3 )

8 ( ( N PP) 4 ) − Consumed “a”

9 ( ( PP ) 5 ) − Consumed “boy”

Page 64: CS626-449: Speech, NLP and the Web/Topics in AI

Top Down ParseState Backup State Action Comments

1 ( ( S ) 1 ) − − Use S NP VP

2 ( ( NP VP ) 1 ) − − Use NP ART N | ART N PP |

PRON

3 ( ( ART N VP ) 1 ) (a) ( ( ART N PP VP ) 1 )

(b) ( ( PRON VP ) 1)

− ART does not match “I”,

backup state (b) used

3B

( ( PRON VP ) 1 ) − −

4 ( ( VP ) 2 ) − Consumed “I”

5 ( ( V NP PP ) 2 ) ( ( V NP ) 2 ) − Verb Attachment Rule used

6 ( ( NP PP ) 3 ) − Consumed “saw”

7 ( ( ART N PP ) 3 ) (a) ( ( ART N PP PP ) 3 )

(b) ( ( PRON PP ) 3 )

8 ( ( N PP) 4 ) − Consumed “a”

9 ( ( PP ) 5 ) − Consumed “boy”

10

( ( P NP ) ) − −

Page 65: CS626-449: Speech, NLP and the Web/Topics in AI

Top Down ParseState Backup State Action Comments

1 ( ( S ) 1 ) − − Use S NP VP

… … … … …

7 ( ( ART N PP ) 3 ) (a) ( ( ART N PP PP ) 3 )

(b) ( ( PRON PP ) 3 )

8 ( ( N PP) 4 ) − Consumed “a”

9 ( ( PP ) 5 ) − Consumed “boy”

10

( ( P NP ) 5 ) − −

11

( ( NP ) 6 ) − Consumed “with”

Page 66: CS626-449: Speech, NLP and the Web/Topics in AI

Top Down ParseState Backup State Action Comments

1 ( ( S ) 1 ) − − Use S NP VP

… … … … …

7 ( ( ART N PP ) 3 ) (a) ( ( ART N PP PP ) 3 )

(b) ( ( PRON PP ) 3 )

8 ( ( N PP) 4 ) − Consumed “a”

9 ( ( PP ) 5 ) − Consumed “boy”

10

( ( P NP ) 5 ) − −

11

( ( NP ) 6 ) − Consumed “with”

12

( ( ART N ) 6 ) (a) ( ( ART N PP ) 6 )

(b) ( ( PRON ) 6)

Page 67: CS626-449: Speech, NLP and the Web/Topics in AI

Top Down ParseState Backup State Action Comments

1 ( ( S ) 1 ) − − Use S NP VP

… … … … …

7 ( ( ART N PP ) 3 ) (a) ( ( ART N PP PP ) 3 )

(b) ( ( PRON PP ) 3 )

8 ( ( N PP) 4 ) − Consumed “a”

9 ( ( PP ) 5 ) − Consumed “boy”

10

( ( P NP ) 5 ) − −

11

( ( NP ) 6 ) − Consumed “with”

12

( ( ART N ) 6 ) (a) ( ( ART N PP ) 6 )

(b) ( ( PRON ) 6)

13

( ( N ) 7 ) − Consumed “a”

Page 68: CS626-449: Speech, NLP and the Web/Topics in AI

Top Down ParseState Backup State Action Comments

1 ( ( S ) 1 ) − − Use S NP VP

… … … … …

7 ( ( ART N PP ) 3 ) (a) ( ( ART N PP PP ) 3 )

(b) ( ( PRON PP ) 3 )

8 ( ( N PP) 4 ) − Consumed “a”

9 ( ( PP ) 5 ) − Consumed “boy”

10

( ( P NP ) 5 ) − −

11

( ( NP ) 6 ) − Consumed “with”

12

( ( ART N ) 6 ) (a) ( ( ART N PP ) 6 )

(b) ( ( PRON ) 6)

13

( ( N ) 7 ) − Consumed “a”

14

( ( − ) 8 ) − Consume “telescope”

Finish Parsing

Page 69: CS626-449: Speech, NLP and the Web/Topics in AI

Top Down Parsing - Observations

Top down parsing gave us the Verb Attachment Parse Tree (i.e., I used a telescope)

To obtain the alternate parse tree, the backup state in step 5 will have to be invoked

Is there an efficient way to obtain all parses ?

Page 70: CS626-449: Speech, NLP and the Web/Topics in AI

I saw a boy with a telescope1 2 3 4 5 6 7 8

Colour Scheme : Blue for Normal Parse Green for Verb Attachment Parse Purple for Noun Attachment Parse Red for Invalid Parse

Bottom Up Parse

Page 71: CS626-449: Speech, NLP and the Web/Topics in AI

I saw a boy with a telescope

1 2 3 4 5 6 7 8

Bottom Up Parse

NP12 PRON12

S1?NP12VP2?

Page 72: CS626-449: Speech, NLP and the Web/Topics in AI

I saw a boy with a telescope

1 2 3 4 5 6 7 8

Bottom Up Parse

NP12 PRON12

S1?NP12VP2? VP2?V23NP3?PP??

VP2?V23NP3?

Page 73: CS626-449: Speech, NLP and the Web/Topics in AI

I saw a boy with a telescope

1 2 3 4 5 6 7 8

Bottom Up Parse

NP12 PRON12

S1?NP12VP2? VP2?V23NP3?PP??

VP2?V23NP3? NP35 ART34N45

NP3?ART34N45PP5?

Page 74: CS626-449: Speech, NLP and the Web/Topics in AI

I saw a boy with a telescope

1 2 3 4 5 6 7 8

NP3?ART34N45PP5?

Bottom Up Parse

NP12 PRON12

S1?NP12VP2? VP2?V23NP3?PP??

VP2?V23NP3? NP35 ART34N45

NP3?ART34N45PP5?

NP35ART34N45

Page 75: CS626-449: Speech, NLP and the Web/Topics in AI

I saw a boy with a telescope

1 2 3 4 5 6 7 8

NP3?ART34N45PP5?

Bottom Up Parse

NP12 PRON12

S1?NP12VP2? VP2?V23NP3?PP??

VP2?V23NP3? NP35 ART34N45

NP3?ART34N45PP5?

NP35ART34N45

VP25V23NP35 S15NP12VP25

VP2?V23NP35PP5?

Page 76: CS626-449: Speech, NLP and the Web/Topics in AI

I saw a boy with a telescope

1 2 3 4 5 6 7 8

NP3?ART34N45PP5?

Bottom Up Parse

NP12 PRON12

S1?NP12VP2? VP2?V23NP3?PP??

VP2?V23NP3? NP35 ART34N45

NP3?ART34N45PP5?

PP5?P56NP6?NP35ART34N45

VP25V23NP35 S15NP12VP25

VP2?V23NP35PP5?

Page 77: CS626-449: Speech, NLP and the Web/Topics in AI

I saw a boy with a telescope

1 2 3 4 5 6 7 8

NP3?ART34N45PP5?

Bottom Up Parse

NP12 PRON12

S1?NP12VP2? VP2?V23NP3?PP??

VP2?V23NP3? NP35 ART34N45

NP3?ART34N45PP5?

PP5?P56NP6?NP35ART34N45 NP68ART67N7?

NP6?ART67N78PP8?

VP25V23NP35 S15NP12VP25

VP2?V23NP35PP5?

Page 78: CS626-449: Speech, NLP and the Web/Topics in AI

I saw a boy with a telescope

1 2 3 4 5 6 7 8

NP3?ART34N45PP5?

Bottom Up Parse

NP12 PRON12

S1?NP12VP2? VP2?V23NP3?PP??

VP2?V23NP3? NP35 ART34N45

NP3?ART34N45PP5?

PP5?P56NP6?NP35ART34N45 NP68ART67N7?

NP6?ART67N78PP8?

NP68ART67N78

VP25V23NP35 S15NP12VP25

VP2?V23NP35PP5?

Page 79: CS626-449: Speech, NLP and the Web/Topics in AI

I saw a boy with a telescope

1 2 3 4 5 6 7 8

NP3?ART34N45PP5?

Bottom Up Parse

NP12 PRON12

S1?NP12VP2? VP2?V23NP3?PP??

VP2?V23NP3? NP35 ART34N45

NP3?ART34N45PP5?

PP5?P56NP6?NP35ART34N45 NP68ART67N7?

NP6?ART67N78PP8?

NP68ART67N78

VP25V23NP35 S15NP12VP25

VP2?V23NP35PP5? PP58P56NP68

Page 80: CS626-449: Speech, NLP and the Web/Topics in AI

I saw a boy with a telescope

1 2 3 4 5 6 7 8

NP3?ART34N45PP5?

Bottom Up Parse

NP12 PRON12

S1?NP12VP2? VP2?V23NP3?PP??

VP2?V23NP3? NP35 ART34N45

NP3?ART34N45PP5?

PP5?P56NP6?NP35ART34N45 NP68ART67N7?

NP6?ART67N78PP8?

NP68ART67N78

VP25V23NP35 S15NP12VP25

VP2?V23NP35PP5? PP58P56NP68

NP38ART34N45PP58

Page 81: CS626-449: Speech, NLP and the Web/Topics in AI

I saw a boy with a telescope

1 2 3 4 5 6 7 8

NP3?ART34N45PP5?

Bottom Up Parse

NP12 PRON12

S1?NP12VP2? VP2?V23NP3?PP??

VP2?V23NP3? NP35 ART34N45

NP3?ART34N45PP5?

PP5?P56NP6?NP35ART34N45 NP68ART67N7?

NP6?ART67N78PP8?

NP68ART67N78

VP25V23NP35 S15NP12VP25

VP2?V23NP35PP5? PP58P56NP68

NP38ART34N45PP58

VP28V23NP38VP28V23NP35PP58

Page 82: CS626-449: Speech, NLP and the Web/Topics in AI

I saw a boy with a telescope

1 2 3 4 5 6 7 8

NP3?ART34N45PP5?

Bottom Up Parse

NP12 PRON12

S1?NP12VP2? VP2?V23NP3?PP??

VP2?V23NP3? NP35 ART34N45

NP3?ART34N45PP5?

PP5?P56NP6?NP35ART34N45 NP68ART67N7?

NP6?ART67N78PP8?

NP68ART67N78

VP25V23NP35 S15NP12VP25

VP2?V23NP35PP5? PP58P56NP68

NP38ART34N45PP58

VP28V23NP38VP28V23NP35PP58

S18NP12VP28

Page 83: CS626-449: Speech, NLP and the Web/Topics in AI

Bottom Up Parsing - Observations

Both Noun Attachment and Verb Attachment Parses obtained by simply systematically applying the rules

Numbers in subscript help in verifying the parse and getting chunks from the parse

Page 84: CS626-449: Speech, NLP and the Web/Topics in AI

Exercise

For the sentence,“The man saw the boy with a

telescope” & the grammar given previously, compare the performance of top-down, bottom-up & top-down chart parsing.

Page 85: CS626-449: Speech, NLP and the Web/Topics in AI

Verb Alternation (1/2) (ref: Natural Language Understanding, James Allan)

Verb ComplementStructure

Example

laugh Empty (in transitive) Ram laughed

find NP (transitive) Ram found the key

give NP+NP (di transitive) Ram gave Sita the paper

give NP+PP [to] Ram gave the paper to Sita

Reside Loc Phrase Ram resides in Mumbai

put NP+loc phrase Ram put the book inside the box

speak PP [with]+PP[about] Ram with Sita about floods

try VP[to] Ram tried to apologise

tell NP+VP[to] Ram told the man to go

Page 86: CS626-449: Speech, NLP and the Web/Topics in AI

Verb Alternation (1/2)Verb Complement

StructureExample

wish S [to] Ram wished for the man to go

keep VP [ing] Ram keeps hoping for the best

catch NP+VP [ing] Ram caught Shyam looking in his desk

Watch NP+VP [base] Ram watched Shyam eat the pizza

regret S [that] Ram regretted that he had eaten the whole thing

Tell NP+S [that] Ram told Sita that he was sorry

Seem ADJP Ram seems unhappy in his new job

Think NP+ADJP Ram thinks Sita is happy

Know S [wh] Ram knows where to go