Computational lexicology morphology and syntax
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Computational Lexicology, Morphology and Syntax. Diana Trandab ăț 2013-2014. NLP with Regular Expressions. Transcript with Eliza, simulation of a Rogerian psychotherapist (Weizenbaum, 1966). User: Men are all alike ELIZA: IN WHAT WAY

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Computational lexicology morphology and syntax

Computational Lexicology, Morphology and Syntax

Diana Trandabăț

2013-2014


Nlp with regular expressions

NLP with Regular Expressions

Transcript with Eliza, simulation of a Rogerian psychotherapist

(Weizenbaum, 1966)

User: Men are all alike

ELIZA: IN WHAT WAY

User: They’re always bugging us about something or other

ELIZA: CAN YOU THINK OF A SPECIFIC EXAMPLE?

User: Well, my boyfriend made me come here

ELIZA: YOUR BOYFRIEND MADE YOU COME HERE

User: He says I’m depressed much of the time

ELIZA: I AM SORRY TO HEAR THAT YOU ARE DEPRESSED


How did it work

How did it work?

  • .* all .*

    →IN WHAT WAY

  • .* always .*

    →CAN YOU THINK OF A SPECIFIC EXAMPLE

  • .* I’m (depressed|sad) .*

    → I AM SORRY TO HEAR YOU ARE \1

  • .* I’m (depressed|sad) .*

    →WHY DO YOU THINK YOU ARE \1?


Aside

Aside…

  • What is intelligence?

  • What does Eliza tell us about intelligence?


The sentence as a string of words e g i saw the lady with the binoculars string a b c d e b f

The sentence as a string of wordsE.g I saw the lady with the binocularsstring = a b c d e b f


Computational lexicology morphology and syntax

The relations of parts of a string to each other may be different

I saw the lady with the binoculars

is stucturally ambiguous

Who has the binoculars?


Computational lexicology morphology and syntax

[I] saw the lady [ with the binoculars]= [a] b c d [e b f]I saw[ the lady with the binoculars]= a b [c d e b f]


Computational lexicology morphology and syntax

How can we represent the difference?

By assigning them different structures.

We can represent structures with 'trees'.

I

read

thebook


Computational lexicology morphology and syntax

a. I saw the lady with the binoculars SNPVPVNPNPPP

Isaw the ladywith the binocularsI saw [the lady with the binoculars]


Computational lexicology morphology and syntax

b. I saw the lady with the binoculars SNPVPVPPP

Isaw the ladywith the binocularsI[ saw the lady ] with the binoculars


Computational lexicology morphology and syntax

birdsfly

S

NPVP

NV

birdsfly

S →NPVP

NP → N

VP → V

Syntactic rules


Computational lexicology morphology and syntax

S

NPVP

birdsfly

ab

ab = string


Computational lexicology morphology and syntax

S

A B

a b

ab

S → A B

A → a

B → b


Computational lexicology morphology and syntax

Rules

Assumption:

natural language grammars are a rule-based systems

What kind of grammars describe natural language phenomena?

What are the formal properties of grammatical rules?


The chomsky hierarchy

The Chomsky Hierarchy


Computational lexicology morphology and syntax

Chomsky (1957) Syntactic Structures. The Hague: Mouton

Chomsky, N. and G.A. Miller (1958) Finite-state languages Information and Control 1, 99-112

Chomsky (1959) On certain formal properties of languages. Information and Control 2, 137-167


Computational lexicology morphology and syntax

Rules in Linguistics1.PHONOLOGY/s/ → [θ]  V ___VRewrite /s/ as [θ] when /s/ occurs in context V ____ VWith:V =auxiliary nodes, θ=terminal nodes


Computational lexicology morphology and syntax

Rules in Linguistics2.SYNTAXS →NP VPVP→VNP→NRewrite S as NP VP in any contextWith:S, NP, VP=auxiliary nodesV, N=terminal node


Computational lexicology morphology and syntax

SYNTAX (phrase/sentence formation)

sentence:

The boykissed the girl

Subjectpredicate

noun phraseverb phrase

art + nounverb + noun phrase

S→NPVP

VP→VNP

NP→ARTN


Computational lexicology morphology and syntax

Chomsky Hierarchy

0.Type 0 (recursively enumerable) languages

Only restrictionon rules: left-hand side cannot be the empty string (* Ø …….)

1. Context-Sensitive languages - Context-Sensitive (CS) rules

2. Context-Free languages - Context-Free (CF) rules

3. Regular languages - Non-Context-Free (CF) rules

0 ⊇ 1⊇ 2 ⊇ 3

a⊇b meaning a properly includes b (aisasupersetofb),

i.e. b is a proper subset of a or b is in a


Computational lexicology morphology and syntax

Generative power

0.Type 0 (recursively enumerable) languages

  • only restriction on rules: left-hand side cannot

    be the empty string (* Ø  …….)

    - is the most powerful system

    3. Type 3(regularlanguage)

    - is the least powerful


Computational lexicology morphology and syntax

Superset/subset relation

S1S2

a c

b

d

f g

a

b

S1 is a subset of S2 ; S2 is a superset of S1


Computational lexicology morphology and syntax

Rule Type – 3

 Name: Regular

 Example:Finite State Automata (Markov-process Grammar)

Rule type:

a) right-linear

AxB or

A  x

with:

A, B = auxiliary nodes and

x = terminal node

b) or left-linear

ABx or

A  x

Generates: ambn with m,n  1

Cannot guarantee that there are as many a’s as b’s; no embedding


Computational lexicology morphology and syntax

A regular grammar for natural language sentences

S→theA

A→catB

A→mouseB

A→duckB

B→bitesC

B→seesC

B→eatsC

C→theD

D→boy

D→girl

D→monkey

the cat bites the boy

the mouse eats the monkey

the duck sees the girl


Computational lexicology morphology and syntax

Regular grammars

Grammar 1: Grammar 2:

A → aA → a

A → a BA → B a

B → b AB → A b

Grammar 3: Grammar 4:

A → aA → a

A → a BA → B a

B → bB → b

B → b AB → A b

Grammar 5:Grammar 6:

S→a AA → A a

S→b BA → B a

A→a SB → b

B→b b SB → A b

S→A → a


Computational lexicology morphology and syntax

Grammars: non-regular

Grammar 6:Grammar 7:

S→A BA → a

S→b BA → B a

A→a SB → b

B→b b SB → b A

S→


Computational lexicology morphology and syntax

Finite-State Automaton

articlenoun

NP NP1 NP2

adjective


Computational lexicology morphology and syntax

NP

article NP1

adjectiveNP1

nounNP2

NP → article NP1

NP1 →adjective NP1

NP1 → noun NP2


Computational lexicology morphology and syntax

A parse tree

S root node

NPVPnon-

terminal

N VNP nodes

DETN

terminal nodes


Computational lexicology morphology and syntax

Rule Type – 2

Name: Context Free

Example:

Phrase Structure Grammars/

Push-Down Automata

Rule type:

A

with:

A = auxiliary node

 = any number of terminal or auxiliary nodes

Recursiveness(centre embedding) allowed:

AA


Computational lexicology morphology and syntax

CF Grammar

 A Context Free grammar consists of:

a)a finite terminal vocabulary VT

b)a finite auxiliary vocabulary VA

c)an axiom S  VA

  • a finite number of context free rules of

    form A → γ,

    whereAVA

    and γ {VAVT}*

    In natural language syntax S is interpreted as the start symbol for sentence, as in S → NP VP


Computational lexicology morphology and syntax

Natural language

Is English regular or CF?

If centre embedding is required, then it cannot be regular

Centre Embedding:

1.[The cat][likes tuna fish]

ab

2.The cat the dog chased likes tuna fish

aa b b

3.The cat the dog the rat bit chased likes tuna fish

a a abb b

4.The cat the dog the rat the elephant admired bit chased likes tuna fish

a a a a bb b b

 ab

aabb

aaabbb

aaaabbbb


Computational lexicology morphology and syntax

[The cat][likes tuna fish]

ab

2.[The cat] [the dog] [chased] [likes ...]

aa bb


Computational lexicology morphology and syntax

Centre embedding

S

NPVP

thelikes

cattuna

ab

= ab


Computational lexicology morphology and syntax

S

NPVP

likes

NPStuna

theb

catNPVP

athechased

dogb

a

=aabb


Computational lexicology morphology and syntax

S

 NPVP

likes

NPStuna

theb

catNPVP

achased

NPSb

the

dog NPVP

athebit

ratb

a

=aaabbb


Computational lexicology morphology and syntax

Natural language 2

More Centre Embedding:

1.If S1, then S2

a a

2.Either S3, or S4

b b

Sentence with embedding:

If either the man is arriving today or the woman is arriving tomorrow, then the child is arriving the day after.

a =[if

b =[either the man is arriving today]

b =[or the woman is arriving tomorrow]]

a =[then the child is arriving the day after]

= abba


Computational lexicology morphology and syntax

CS languages

The following languages cannot be generated by a CF grammar (by pumping lemma):

anbmcndm

Swiss German:

A string of dative nouns (e.g. aa), followed by a string of accusative nouns (e.g. bbb), followed by a string of dative-taking verbs (cc), followed by a string of accusative-taking verbs (ddd)

= aabbbccddd

= anbmcndm


Computational lexicology morphology and syntax

Swiss German:

Jan sait das (Jan says that) …

merem HansesHuushälfedaastriiche

weHans/DATthe house/ACChelpedpaint

we helped Hans paint the house

abcd

NPdatNPdatNPaccNPaccVdatVdatVaccVacc

aabbccd d


Context free grammars cfgs

Context Free Grammars (CFGs)

Sets of rules expressing how symbols of the language fit together, e.g.S -> NP VPNP -> Det NDet -> theN -> dog


What does context free mean

What Does Context Free Mean?

  • LHS of rule is just one symbol.

  • Can haveNP -> Det N

  • Cannot haveX NP Y -> X Det N Y


Grammar symbols

Grammar Symbols

  • Non Terminal Symbols

  • Terminal Symbols

    • Words

    • Preterminals


Non terminal symbols

Non Terminal Symbols

  • Symbols which have definitions

  • Symbols which appear on the LHS of rulesS-> NP VPNP -> Det NDet -> theN-> dog


Non terminal symbols1

Non Terminal Symbols

  • Same Non Terminals can have several definitionsS-> NP VPNP -> Det N

    NP -> N Det -> theN-> dog


Terminal symbols

TerminalSymbols

  • Symbols which appear in final string

  • Correspond to words

  • Are not defined by the grammar

    S -> NP VPNP -> Det NDet -> theN -> dog


Parts of speech pos

Parts of Speech (POS)

  • NT Symbols which produce terminal symbols are sometimes called pre-terminals

    S -> NP VPNP -> Det NDet -> theN-> dog

  • Sometimes we are interested in the shape of sentences formed from pre-terminalsDet N VAux N V D N


Cfg formal definition

CFG - formal definition

A CFG is a tuple (N,,R,S)

  • N is a set of non-terminal symbols

  •  is a set of terminal symbols disjoint from N

  • R is a set of rules each of the form A   where A is non-terminal

  • S is a designated start-symbol


Cfg example

grammar:

S  NP VP

NP  N

VP  V NP

lexicon:

V  kicks

N  John

N  Bill

N = {S, NP, VP, N, V}

 = {kicks, John, Bill}

R = (see opposite)

S = “S”

CFG - Example


Exercise

Exercise

  • Write grammars that generate the following languages, for m > 0

    (ab)m

    anbm

    anbn

  • Which of these are Regular?

  • Which of these are Context Free?


Ab m for m 0

(ab)m for m > 0

S -> a b

S -> a b S


Ab m for m 01

(ab)m for m > 0

S -> a b

S -> a b S

S -> a X

X -> b Y

Y -> a b

Y -> S


A n b m

S -> A B

A -> a

A -> a A

B -> b

B -> b B

anbm


A n b m1

S -> A B

A -> a

A -> a A

B -> b

B -> b B

S -> a AB

AB -> a AB

AB -> B

B -> b

B -> b B

anbm


Grammar defines a structure

NP

VP

N

V

NP

N

John

kicks

Bill

Grammar Defines a Structure

grammar:

S  NP VP

NP  N

VP  V NP

lexicon:

V  kicks

N  John

N  Bill

S


Different grammar different stucture

NP

N

Bill

Different Grammar Different Stucture

grammar:

S  NP NP

NP  N V

NP  N

lexicon:

V  kicks

N  John

N  Bill

S

NP

N

V

John

kicks


Which grammar is best

Which Grammar is Best?

  • The structure assigned by the grammar should be appropriate.

  • The structure should

    • Be understandable

    • Allow us to make generalisations.

    • Reflect the underlying meaning of the sentence.


Ambiguity

Ambiguity

  • A grammar is ambiguous if it assigns two or more structures to the same sentence.

    NP  NP CONJ NP

    NP  N

    lexicon:

    CONJ  and

    N  John

    N  Bill

  • The grammar should not generate too many possible structures for the same sentence.


Criteria for evaluating grammars

Criteria for Evaluating Grammars

  • Does it undergenerate?

  • Does it overgenerate?

  • Does it assign appropriate structures to sentences it generates?

  • Is it simple to understand? How many rules are there?

  • Does it contain just a few generalisations or is it full of special cases?

  • How ambiguous is it? How many structures does it assign for a given sentence?


Patr ii

PATR-II

  • The PATR-II formalism can be viewed as a computer language for encoding linguistic information.

  • A PATR-II grammar consists of a set of rules and a lexicon.

  • The rules are CFG rules augmented with constaints.

  • The lexicon provides information about terminal symbols.


Example patr ii grammar and lexicon

Grammar (grammar.grm)

Rule

s -> np vp

Rule

np -> n

Rule

vp -> v

Lexicon (lexicon.lex)

\w uther

\c n

\w sleeps

\c v

Example PATR-IIGrammar and Lexicon


Probabilistic context free grammar pcfg

Probabilistic Context Free Grammar(PCFG)

  • A PCFG is a probabilistic version of a CFG where each production has a probability.

  • String generation is now probabilistic where production probabilities are used to non-deterministically select a production for rewriting a given non-terminal.


Characteristics of pcfgs

Characteristics of PCFGs

  • In a PCFG, the probability P(Aβ) expresses the likelihood that the non-terminal A will expand as β.

    • e.g. the likelihood that S  NP VP

      • (as opposed to SVP, or S  NP VP PP, or… )

  • can be interpreted as a conditional probability:

    • probability of the expansion, given the LHS non-terminal

    • P(Aβ) = P(Aβ|A)

  • Therefore, for any non-terminal A, probabilities of every rule of the form A  β must sum to 1

    • If this is the case, we say the PCFG is consistent


Simple pcfg for english

Simple PCFG for English

Lexicon

Prob

Grammar

S → NP VP

S → Aux NP VP

S → VP

NP → Pronoun

NP → Proper-Noun

NP → Det Nominal

Nominal → Noun

Nominal → Nominal Noun

Nominal → Nominal PP

VP → Verb

VP → Verb NP

VP → VP PP

PP → Prep NP

0.8

0.1

0.1

0.2

0.2

0.6

0.3

0.2

0.5

0.2

0.5

0.3

1.0

Det → the | a | that | this

0.6 0.2 0.1 0.1

Noun → book | flight | meal | money

0.1 0.5 0.2 0.2

Verb → book | include | prefer

0.5 0.2 0.3

Pronoun → I | he | she | me

0.5 0.1 0.1 0.3

Proper-Noun → Houston | NWA

0.8 0.2

Aux → does

1.0

Prep → from | to | on | near | through

0.25 0.25 0.1 0.2 0.2

+

1.0

+

1.0

+

1.0

+

1.0


Parse tree and sentence probability

Parse tree and Sentence Probability

  • Assume productions for each node are chosen independently.

  • Probability of a parse tree (derivation) is the product of the probabilities of its productions.

  • Resolve ambiguity by picking most probable parse tree.

  • Probability of a sentence is the sum of the probabilities of all of its derivations.


Example pcfg rules probabilities

Example PCFG Rules & Probabilities

  • S  NP VP1.0

  • NP  DT NN0.5

  • NP  NNS0.3

  • NP  NP PP 0.2

  • PP  P NP1.0

  • VP  VP PP 0.6

  • VP  VBD NP0.4

  • DT  the1.0

  • NN  gunman0.5

  • NN  building0.5

  • VBD  sprayed 1.0

  • NNS  bullets1.0

  • P  with1.0


Example parse t 1

Example Parse t1`

  • The gunman sprayed the building with bullets.

S1.0

P (t1) = 1.0 * 0.5 * 1.0 * 0.5 * 0.6 * 0.4 * 1.0 * 0.5 * 1.0 * 0.5 * 1.0 * 1.0 * 0.3 * 1.0 = 0.00225

NP0.5

VP0.6

NN0.5

DT1.0

PP1.0

VP0.4

P1.0

NP0.3

NP0.5

VBD1.0

The

gunman

DT1.0

NN0.5

with

NNS1.0

sprayed

the

building

bullets


Another parse t 2

Another Parse t2

  • The gunman sprayed the building with bullets.

S1.0

P (t2) = 1.0 * 0.5 * 1.0 * 0.5 * 0.4 * 1.0 * 0.2 * 0.5 * 1.0 * 0.5 * 1.0 * 1.0 * 0.3 * 1.0 = 0.0015

NP0.5

VP0.4

NN0.5

DT1.0

VBD1.0

NP0.2

The

gunman

sprayed

NP0.5

PP1.0

P (sentence) = = P (t1) + P(t2) = = 0.00225 + 0.0015 = 0.00375

DT1.0

NN0.5

P1.0

NP0.3

NNS1.0

the

building

with

bullets


Computational lexicology morphology and syntax

Great!

See you next time!


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