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university-logo Data Different Kinds of Polysemy How to Capture Lexical Generlisations Semantics and Pragmatics of NLP Lexical Semantics: Polysemy Alex Lascarides School of Informatics University of Edinburgh Alex Lascarides SPNLP: Lexical Polysemy
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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

Aug 12, 2020

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  • university-logo

    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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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) =MlrNlr

    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

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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

    Why A Dictionary Won't Do: Polysemy!Different Kinds of PolysemyHow to Capture Lexical Generlisations