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Semantics and Pragmatics of NLP Lexical Semantics: Polysemy · Semantics and Pragmatics of NLP Lexical Semantics: Polysemy Alex Lascarides School of Informatics University of Edinburgh

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Page 1: Semantics and Pragmatics of NLP Lexical Semantics: Polysemy · Semantics and Pragmatics of NLP Lexical Semantics: Polysemy Alex Lascarides School of Informatics University of Edinburgh

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DataDifferent Kinds of Polysemy

How to Capture Lexical Generlisations

Semantics and Pragmatics of NLPLexical Semantics: Polysemy

Alex Lascarides

School of InformaticsUniversity of Edinburgh

Alex Lascarides SPNLP: Lexical Polysemy

Page 2: Semantics and Pragmatics of NLP Lexical Semantics: Polysemy · Semantics and Pragmatics of NLP Lexical Semantics: Polysemy Alex Lascarides School of Informatics University of Edinburgh

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DataDifferent Kinds of Polysemy

How to Capture Lexical Generlisations

Outline

1 Why A Dictionary Won’t Do: Polysemy!

2 Different Kinds of Polysemy

3 How to Capture Lexical Generlisations

Alex Lascarides SPNLP: Lexical Polysemy

Page 3: Semantics and Pragmatics of NLP Lexical Semantics: Polysemy · Semantics and Pragmatics of NLP Lexical Semantics: Polysemy Alex Lascarides School of Informatics University of Edinburgh

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DataDifferent Kinds of Polysemy

How to Capture Lexical Generlisations

Basic Lessons

The meanings of words sometimes predict aspects of theirsyntactic behaviour (regular polysemy).So lexical knowledge is the interface between worldknowledge and linguistic knowledge/processingDiscourse/ pragmatic processing interacts with lexicalsemanticsLexical semantic information can be modelled using atyped feature structure formalism, extended to handledefaults

Alex Lascarides SPNLP: Lexical Polysemy

Page 4: Semantics and Pragmatics of NLP Lexical Semantics: Polysemy · Semantics and Pragmatics of NLP Lexical Semantics: Polysemy Alex Lascarides School of Informatics University of Edinburgh

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DataDifferent Kinds of Polysemy

How to Capture Lexical Generlisations

Introducing bake

(1) a. Kim is bakingb. The potatoes are bakingc. Kim is baking a caked. Kim is baking a cake for Sandye. Kim is baking Sandy a cakef. The clay bakedg. The clay baked hardh. Sandy was baking, sitting in the hot sun

Alex Lascarides SPNLP: Lexical Polysemy

Page 5: Semantics and Pragmatics of NLP Lexical Semantics: Polysemy · Semantics and Pragmatics of NLP Lexical Semantics: Polysemy Alex Lascarides School of Informatics University of Edinburgh

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DataDifferent Kinds of Polysemy

How to Capture Lexical Generlisations

Sense enumeration: a strawman

(2) a. 〈bake, TakesNP, bake′1〉

b. 〈bake, TakesNP, bake′2〉

c. 〈bake, TakesNP.NP, bake′3〉

d. 〈bake, TakesNP.NP.PP, bake′4〉

e. 〈bake, TakesNP.NP.NP, bake′5〉

Senses connected by meaning postulates, such as:∀x , y [bake′

3(x , y) → bake′1(x) ∧ bake′

2(y)]

∀x , y [bake′4(x , y , z) → bake′

3(x , y)]

Alex Lascarides SPNLP: Lexical Polysemy

Page 6: Semantics and Pragmatics of NLP Lexical Semantics: Polysemy · Semantics and Pragmatics of NLP Lexical Semantics: Polysemy Alex Lascarides School of Informatics University of Edinburgh

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DataDifferent Kinds of Polysemy

How to Capture Lexical Generlisations

Problems

Doesn’t capture generalisations (cf cook, paint)No distinction between homonyms (accidental polysemy)and related sensesMeaning postulates are unrestrictedCannot list all potential usages.

Alex Lascarides SPNLP: Lexical Polysemy

Page 7: Semantics and Pragmatics of NLP Lexical Semantics: Polysemy · Semantics and Pragmatics of NLP Lexical Semantics: Polysemy Alex Lascarides School of Informatics University of Edinburgh

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DataDifferent Kinds of Polysemy

How to Capture Lexical Generlisations

Terminology

homonymy mogul :mound of snow vs. mogul :chinese emperorunpredictable polysemy bank :tilt of plane vs. bank :moundregular polysemy bank :building used by bank :institution

nonce not recorded in the lexicon, but interpretable viagenerative devices;The ham sandwich is getting impatient.

institutionalised recorded in the lexicon as derived by a regularprocess; teacher

lexicalised idiosyncratically augment or override regularlyderived information; in hospital.

established covers institutionalised and lexicalised

Alex Lascarides SPNLP: Lexical Polysemy

Page 8: Semantics and Pragmatics of NLP Lexical Semantics: Polysemy · Semantics and Pragmatics of NLP Lexical Semantics: Polysemy Alex Lascarides School of Informatics University of Edinburgh

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DataDifferent Kinds of Polysemy

How to Capture Lexical Generlisations

More Terminology

constructional polysemy a single sense assigned to a lexicalentry is contextually specialised

(3) a. That book is 500 pages longb. That book introduces syntax.c. That book is 500 pages long and

introduces syntax.

sense extension separate lexical entries are generated

(4) a. That chicken is healthy.b. That chicken is tasty.c.??That chicken is healthy and tasty.

Distinction between constructional polysemy and senseextension is not always clear cut.

Alex Lascarides SPNLP: Lexical Polysemy

Page 9: Semantics and Pragmatics of NLP Lexical Semantics: Polysemy · Semantics and Pragmatics of NLP Lexical Semantics: Polysemy Alex Lascarides School of Informatics University of Edinburgh

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DataDifferent Kinds of Polysemy

How to Capture Lexical Generlisations

Back to the lexical entry for transitive bake

transitive-verbORTH : bake

SYN :

CAT : v

SUBJ :

phraseSYN:CAT : nSEM:INDEX : x

SUBCAT :

⟨ phraseSYN:CAT : nSEM:INDEX : y

SEM :

INDEX : e

LISZT :

⟨_bake3_relEVENT : eARG1 : xARG2 : y

HPSGish syntax and MRS — neutral wrt various approaches tolexical semantics

Alex Lascarides SPNLP: Lexical Polysemy

Page 10: Semantics and Pragmatics of NLP Lexical Semantics: Polysemy · Semantics and Pragmatics of NLP Lexical Semantics: Polysemy Alex Lascarides School of Informatics University of Edinburgh

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DataDifferent Kinds of Polysemy

How to Capture Lexical Generlisations

bread (NP)

phraseORTH : bread

SYN :

[CAT : nSUBCAT : 〈〉

]

SEM :

INDEX : w

LISZT :

⟨[_bread_relINST : w

]⟩

LISZTs are appended when signs are combined LISZT :

⟨_bake3_relEVENT : eARG1 : xARG2 : y

,

[_bread_relINST : y

]⟩ BAKE3(e, x , y) ∧ BREAD(y)

Alex Lascarides SPNLP: Lexical Polysemy

Page 11: Semantics and Pragmatics of NLP Lexical Semantics: Polysemy · Semantics and Pragmatics of NLP Lexical Semantics: Polysemy Alex Lascarides School of Informatics University of Edinburgh

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DataDifferent Kinds of Polysemy

How to Capture Lexical Generlisations

Abbreviated description

transitive-verbORTH : bake

SYN :

CAT : vSUBJ : NP xSUBCAT : 〈NP y 〉

SEM :

INDEX : e

LISZT :

⟨_bake3_relEVENT : eARG1 : xARG2 : y

Alex Lascarides SPNLP: Lexical Polysemy

Page 12: Semantics and Pragmatics of NLP Lexical Semantics: Polysemy · Semantics and Pragmatics of NLP Lexical Semantics: Polysemy Alex Lascarides School of Informatics University of Edinburgh

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DataDifferent Kinds of Polysemy

How to Capture Lexical Generlisations

Type for simple transitive verbs

transitive-verbORTH : string

SYN :

CAT : vSUBJ : NP xSUBCAT : 〈NP y 〉

SEM :

INDEX : e

LISZT :

⟨relEVENT : eARG1 : xARG2 : y

Generalisation: agents realised as subjects and patients asobjects.

Alex Lascarides SPNLP: Lexical Polysemy

Page 13: Semantics and Pragmatics of NLP Lexical Semantics: Polysemy · Semantics and Pragmatics of NLP Lexical Semantics: Polysemy Alex Lascarides School of Informatics University of Edinburgh

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DataDifferent Kinds of Polysemy

How to Capture Lexical Generlisations

Type hierarchies>

���HHHsign

���HHH

word���

HHHverb noun

transitive-verb

bake3

phrase

relaaaaaaanoun_rel

���HHHphys_rel

���art_rel���

HHHphys_art_rel

verb_rel

tverb_rel

_bake3_rel

Information on higher types is inherited by lower types (andlexical entries, such as bake3)Multiple inheritance is possible

Alex Lascarides SPNLP: Lexical Polysemy

Page 14: Semantics and Pragmatics of NLP Lexical Semantics: Polysemy · Semantics and Pragmatics of NLP Lexical Semantics: Polysemy Alex Lascarides School of Informatics University of Edinburgh

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DataDifferent Kinds of Polysemy

How to Capture Lexical Generlisations

Constructional polysemy: Logical Metonymy

(5) a. Sandy enjoyed the film/beer/hamburger.b. Sandy enjoyed the book.c. Sandy enjoyed reading the bookd. The goat really enjoyed your book.

Don’t want to enumerate a million senses of enjoy!It’s not a purely pragmatic phenomenon:??John enjoyed the doorstop. ??John enjoyed the tunnel.Generalisation: When NP is an artifact,enjoy NP means enjoy V-ing NP, where V is its purpose.But this can be overridden in sufficiently rich discoursecontexts.

Alex Lascarides SPNLP: Lexical Polysemy

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DataDifferent Kinds of Polysemy

How to Capture Lexical Generlisations

Capturing the Generalisation (Simplified):enjoy the book

book: enjoy: inherited info; begin, finish etc.2664bookSEM : book(y)

QUALIA :

"CONST : pagesTELIC : readAGENTIVE : write

#3775

266664coercing

CAT SUBCAT :

*24 npSEM : n [Q(y)]

QUALIA TELIC : act-on-pred P

35+

SEM : [e][enjoy(e, x, e′) ∧ act-on-pred/ P (e′, x, y) ∧ n ]

377775

enjoy the book:coercing

CAT SUBCAT :

⟨ npSEM : n book(y)

QUALIA TELIC : P read

⟩SEM : [e][enjoy(e, x , e′) ∧ / P read(e′, x , y) ∧ n book(y)]

Alex Lascarides SPNLP: Lexical Polysemy

Page 16: Semantics and Pragmatics of NLP Lexical Semantics: Polysemy · Semantics and Pragmatics of NLP Lexical Semantics: Polysemy Alex Lascarides School of Informatics University of Edinburgh

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DataDifferent Kinds of Polysemy

How to Capture Lexical Generlisations

Constructional Polysemy: Sense Broadening

(6) a. There are clouds in the sky — it’s going to rain.b. There was a cloud of flies round the cow.c. The flies were pestering the horse — it swished its

tail at the buzzing cloud.

lex-count-nounORTH : cloudCAT : noun-catSEM : obj-noun-formula

QUALIA :

phys_obj /natural_obj

FORM :

nomformRELATIVE : indivABS. : amorph

CONSTITUENCY : phys_cum /water-vapour

Alex Lascarides SPNLP: Lexical Polysemy

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DataDifferent Kinds of Polysemy

How to Capture Lexical Generlisations

Sense extension

Grammatical effects: count -> mass, verbingEstablished and non-established sensesConventional nature of process

(7) a. Sandy drank a bottle of whisky.container -> contents

b. Sandy drank a bottleful of whisky.

(8) a. Kim ate some chicken.animal -> meat

b. That restaurant serves ostrich.

Alex Lascarides SPNLP: Lexical Polysemy

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DataDifferent Kinds of Polysemy

How to Capture Lexical Generlisations

A particular kind of Sense Extension: ReferenceTransfer

(9) a. The ham sandwich has paid his check.physical object -> associated person

b. *The dark haired guy is in the microwave.

(10) Chester serves not just country folk, but farming,suburban and city folk too. You’ll see Armani driftinginto the Grosvenor Hotel’s exclusive (but exquisite)Arkle Restaurant and C+A giggling out of its streetfrontbrasserie next door. (Guardian Weekly)manufacturer -> product + clothes -> wearer

Alex Lascarides SPNLP: Lexical Polysemy

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DataDifferent Kinds of Polysemy

How to Capture Lexical Generlisations

“Lexical Rules”

grinding < lexical-rulelex-count-nounORTH : 0SYN : noun-catSEM PRED : 3QUALIA : physical

lex-uncount-nounORTH : 0SYN : noun-catSEM PRED : grinding′( 3 )QUALIA : physical

meat-grinding < grinding[QUALIA : animal

]→

[QUALIA : edible_substance

]

Alex Lascarides SPNLP: Lexical Polysemy

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DataDifferent Kinds of Polysemy

How to Capture Lexical Generlisations

Verb classes: Levin (1993)

Large-scale descriptive account of (some) English verbs,pushing the idea that syntactic behaviour is (partly)semantically determined

alternationsThe ways in which arguments to verbs can be realiseddifferently.semantically coherent classes which exhibit the samealternationssome extended senses (eg whistle as a movement verb)

Alex Lascarides SPNLP: Lexical Polysemy

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DataDifferent Kinds of Polysemy

How to Capture Lexical Generlisations

26 Verbs of Creation and Transformation

26.1 Build verbs Class members include: bake, cook

(11) Material/Product Alternation (transitive):a. Martha baked a loaf out of wholewheat flourb. Martha baked some wholewheat flour into a loaf

(12) Unspecified Object Alternation:a. Martha bakes breadb. Martha bakes

(13) Benefactive alternationa. Martha baked a loaf (out of wholewheat flour) for

her auntb. Martha baked her aunt a loaf (out of wholewheat

flour)

Alex Lascarides SPNLP: Lexical Polysemy

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DataDifferent Kinds of Polysemy

How to Capture Lexical Generlisations

26.3 Verbs of Preparing

Class members include: bake, cook, fix, fry

(14) *Material/Product Alternation (transitive):a. ?Donna fixed a sandwich from last night’s leftoversb. *Donna fixed last night’s leftovers into a sandwich

(15) Benefactive alternationa. Donna fixed a sandwich for meb. Donna fixed me a sandwich

(16) *Causative alternationsa. Donna fixed a sandwichb. *a sandwich fixed

Alex Lascarides SPNLP: Lexical Polysemy

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DataDifferent Kinds of Polysemy

How to Capture Lexical Generlisations

45 Verbs of change of state

45.3 Cooking verbs Class members include: bake, cook, fry

(17) Causative/Inchoative Alternationa. Jennifer baked the potatoesb. The potatoes baked

(18) Middle Alternationa. Jennifer baked Idaho potatoesb. Idaho potatoes bake beautifully

(19) Instrument Subject Alternationa. Jennifer baked the potatoes in the ovenb. This oven bakes potatoes well

Alex Lascarides SPNLP: Lexical Polysemy

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DataDifferent Kinds of Polysemy

How to Capture Lexical Generlisations

Representing alternations: Lexical rules2666666666666666664

creation-tverbORTH : 1

SYN :

2664CAT : vSUBJ : NP

xSUBCAT : 〈NP

y〉

3775

SEM :

2666664INDEX : e

LISZT :

*2

26664relEVENT : e

ARG1 : x

ARG2 : y

37775+

3777775

3777777777777777775

2666666666666666664

benef-PP-verbORTH : 1

SYN :

2664CAT : vSUBJ : NP

xSUBCAT : 〈NP

y, PP(for)

z〉

3775

SEM :

2666664INDEX : e

LISZT :

*2

26664relEVENT : e

ARG1 : x

ARG2 : y

37775,

24 benef_relEVENT : e

ARG1 : z

35+3777775

3777777777777777775

John baked a cake → John baked a cake for MaryAlex Lascarides SPNLP: Lexical Polysemy

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DataDifferent Kinds of Polysemy

How to Capture Lexical Generlisations

Semi-productivity

??pig meaning meat, ??cow meaning meat,??John donated Oxfam a covenant etc.Avoid obscurity: use the form which has highest probability.

Estimating Probabilities: via Prob(lexical-entry | word-form)Seen lexical entries: Use frequencies in a very large corpus

(marked with senses!!)badger count noun, deer count noun.

Unseen lexical entries: Estimate the productivity of theappropriate lexical rule.badger meaning meat, deer meaning meat.

Alex Lascarides SPNLP: Lexical Polysemy

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DataDifferent Kinds of Polysemy

How to Capture Lexical Generlisations

Probabilities of Unseen Lexical Entries

1 Estimate the degree of productivity of a lexical rule lr bycomparing the number of attested outputs Mlr with thenumber of attested inputs Nlr seen in the corpus:

Prod(lr) =Mlr

Nlr

2 Use the Prod(lr)s to smooth over unseen data.

unseen-pr-mass(wf) =number-of-unattested-entries(wf)

freq(wf) + number-of-unattested-entries(wf)

est-freq(lei |wf) = unseen-pr-mass(wf)× Prod(lri)

ΣProd(lr1), . . . , Prod(lrn)

Alex Lascarides SPNLP: Lexical Polysemy

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DataDifferent Kinds of Polysemy

How to Capture Lexical Generlisations

Blocking

Blocking is an automatic consequence of avoid obscurity:

Prob(DEER-MEAT|venison) > Prob(DEER-MEAT|deer)

therefore generation of blocked forms is marked.

(20) a. That restaurant serves venison/?deer.b. There were five thousand extremely loud people

on the floor eager to tear into roast cow with bothhands and wash it down with bourbon whiskey.(Terry Pratchett)

Blocked forms dispreferred, but interpretable if otherpossibilities fail.Need formal account of pragmatic effects of unblocking

Alex Lascarides SPNLP: Lexical Polysemy

Page 28: Semantics and Pragmatics of NLP Lexical Semantics: Polysemy · Semantics and Pragmatics of NLP Lexical Semantics: Polysemy Alex Lascarides School of Informatics University of Edinburgh

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DataDifferent Kinds of Polysemy

How to Capture Lexical Generlisations

Conclusions

Lexical semantics interacts in complex ways with syntaxand pragmatics.A dictionary model of the lexicon is too simplistic to do thisjustice.But manually constructing a lexical type hierarchy with richsemantic information is impractical.Can we use machine learning from corpora toautomatically acquire the lexical semantic information?

Next time: A case study—logical metonymy.

Alex Lascarides SPNLP: Lexical Polysemy