Investigating adjective denotation and collocation

Ann CopestakeComputer Laboratory,University of Cambridge

Outline introduction: compositional semantics,

GL and semantic space models. denotation and collocation

distribution of `magnitude’ adjectives hypotheses about adjective denotation

and collocation semi-productivity

Themes semi-productivity: extending paper in

GL 2001 to phrases statistical and symbolic models

interacting generation as well as analysis computational account

Different branches of computational semantics compositional semantics: capture syntax, (some)

close-class words and (some) morphology every x [ dog’(x) -> bark’(x)] large coverage grammars as testbed for GL (constructions,

composition, underspecification) lexical semantics, e.g.,

GL (interacts with compositional semantics) WordNet meaning postulates etc

semantic space models, e.g., LSA Schütze (1995) Lin (multiple papers), Pado and Lapata (2003)

semantic spaces acquired from corpora generally, collect vectors of words

which co-occur with the target more sophisticated models incorporate

syntactic relationships

dog bark house cat

dog - 1 0 0

bark 1 - 0 0

Semantic space models and compositional semantics? do spaces correspond to predicates in compositional semantics?

e.g., bark’ attractions

automatic acquisition similarity metrics, priming fuzziness, meaning variation, sense clustering statistical approximation to real world knowledge? (but fallacy with

parse selection techniques) problems

classical lexical semantic relations (hyponymy etc) aren’t captured well

can’t do inference sensitivity to domain/corpus

role of collocation?

Denotation: assumptions Truth-conditional, logically formalisable (in

principle), refers to `real world’ (extension) Not necessarily decomposable: natural kinds (dog’

– canis familiaris), natural predicates Naive physics, biology, etc

Computationally: specification of meaning that interfaces with non-linguistic components

Selectional restrictions? bark’(x) -> dog’(x) or seal’(x) or ...

Collocation: assumptions Significant co-occurrences of words in

syntactically interesting relationships `syntactically interesting’: for this talk, attributive

adjectives and the nouns they immediately precede

`significant’: statistically significant (but on what assumptions about baseline?)

Compositional, no idiosyncratic syntax etc (as opposed to multiword expression)

About language rather than the real world

Collocation versus denotation Whether an unusually frequent word pair is a

collocation or not depends on assumptions about denotation: fix denotation to investigate collocation

Empirically: investigations using WordNet synsets (Pearce, 2001)

Anti-collocation: words that might be expected to go together and tend not to e.g., ? flawless behaviour (Cruse, 1986): big rain (unless

explained by denotation) e.g., buy house is predictable on basis of denotation,

shake fist is not

Collocation and denotation investigations can this notion of collocation be made

precise, empirically testable? assumptions about denotation determine

whether something is a collocation semantic space models will include

collocational effects initial, very preliminary, investigations with

magnitude adjectives attributive adjectives: can get corpus data without

parsing only one argument to consider

Distribution of `magnitude’ adjectives: summary some very frequent adjectives have magnitude-

related meanings (e.g., heavy, high, big, large) basic meaning with simple concrete entities extended meaning with abstract nouns, non-concrete

physical entities (high taxation, heavy rain) extended uses more common than basic not all magnitude adjectives – e.g. tall

nouns tend to occur with a limited subset of these extended adjectives

some apparent semantic groupings of nouns which go with particular adjectives, but not easily specified

Some adjective-noun frequencies in the BNC

number proportion quality problem part winds rain

large 1790 404 0 10 533 0 0

high 92 501 799 0 3 90 0

big 11 1 0 79 79 3 1

heavy 0 0 1 0 1 2 198

Grammaticality judgments

number proportion quality problem part winds rain

large * ? * *

high * ? *

big ? *

heavy ? * * *

More examplesimportance

success majority number proportion

quality role problem part winds support rain

great 310 360 382 172 9 11 3 44 71 0 22 0

large 1 1 112 1790 404 0 13 10 533 0 1 0

high 8 0 0 92 501 799 1 0 3 90 2 0

major 62 60 0 0 7 0 272 356 408 1 8 0

big 0 40 5 11 1 0 3 79 79 3 1 1

strong 0 0 2 0 0 1 8 0 3 132 147 0

heavy 0 0 1 0 0 1 0 0 1 2 4 198

Judgmentsimportance

success majority numberproportion

quality role problem part winds support rain

great ? *

large ? ? * ? * *

high * ? ? * ? *

major ? ? ?

big ? ?

strong ? ? * * * * ?

heavy ? * ? * * * *

Distribution Investigated the distribution of heavy, high, big,

large, strong, great, major with the most common co-occurring nouns in the BNC

Nouns tend to occur with up to three of these adjectives with high frequency and low or zero frequency with the rest

My intuitive grammaticality judgments correlate but allow for some unseen combinations and disallow a few observed but very infrequent ones

big, major and great are grammatical with many nouns (but not frequent with most), strong and heavy are ungrammatical with most nouns, high and large intermediate

heavy: groupings?magnitude: dew, rainstorm, downpour, rain, rainfall, snowfall, fall, snow, shower: frost, spindrift: clouds, mist, fog: flow, flooding, bleeding, period, traffic: demands, reliance, workload, responsibility, emphasis, dependence: irony, sarcasm, criticism: infestation, soiling: loss, price, cost, expenditure, taxation, fine, penalty, damages, investment: punishment, sentence: fire, bombardment, casualties, defeat, fighting: burden, load, weight, pressure: crop: advertising: use, drinking:magnitude of verb: drinker, smoker: magnitude related? odour, perfume, scent, smell, whiff: lunch: sea, surf, swell:

high: groupings?

magnitude: esteem, status, regard, reputation, standing, calibre, value, priority; grade, quality, level; proportion, degree, incidence, frequency, number, prevalence, percentage; volume, speed, voltage, pressure, concentration, density, performance, temperature, energy, resolution, dose, wind; risk, cost, price, rate, inflation, tax, taxation, mortality, turnover, wage, income, productivity, unemployment, demandmagnitude of verb: earner

heavy and high 50 nouns in BNC with the extended

magnitude use of heavy with frequency 10 or more

160 such nouns with high Only 9 such nouns with both adjectives:

price, pressure, investment, demand, rainfall, cost, costs, concentration, taxation

Basic adjective denotation

with simple concrete objects: high’(x) => zdim(x) > norm(zdim,type(x),c)heavy’(x) => wt(x) > norm(wt,type(x),c)

where zdim is distance on vertical, wt is weight (measure functions, MF)

norm(MF,class,context) is some standard for MF for class in context

(high’ also requires selectional restriction – not animate)

Metaphor Different metaphors for different nouns (cf., Lakoff et

al) `high’ nouns measured with an upright scale: e.g.,

temperature: temperature is rising `heavy’ nouns metaphorically like burden: e.g., workload:

her workload is weighing on her Empirical account of distribution?

predictability of noun classes? high volume? high and heavy taxation

adjective denotation for inference etc? via literal denotation? Discussed again at end of talk

Possible empirical accounts of distribution

1. Difference in denotation between `extended’ uses of adjectives

2. Grammaticized selectional restrictions/preferences

3. Lexical selection• stipulate Magn function with nouns (Meaning-

Text Theory)

4. Semi-productivity / collocation• plus semantic back-off

Computational semantics perspective Require workable account of

denotation: not too difficult to acquire, not over-specific

Require account of distribution for generation

Robustness and completeness Can’t assume pragmatics / real world

knowledge does the difficult bits!

Denotation account of distribution Denotation of adjective simply prevents it being possible with

the noun. heavy and high have different denotationsheavy’(x) => MF(x) > norm(MF,type(x),c) & precipitation(x) or

cost(x) or flow(x) or consumption(x)...(where rain(x) -> precipitation(x) and so on)

But: messy disjunction or multiple senses, open-ended, unlikely to be tractable. e.g., heavy shower only for rain sense, not bathroom sense

Not falsifiable, but no motivation other than distribution. Dictionary definitions can be seen as doing this (informally), but

none account for observed distribution.

Selectional restrictions and distribution Assume the adjectives have the same denotation Distribution via features in the lexicon

e.g., literal high selects for [ANIMATE false ] approach used in the LinGO ERG for in/on in temporal

expressions grammaticized, so doesn’t need to be determined by

denotation (though assume consistency) can utilise qualia structure

Problem: can’t find a reasonable set of cross-cutting features!

Stipulative approach possible, but unattractive.

Lexical selection MTT approach noun specifies its Magn adjective

in Mel’čuk and Polguère (1987), Magn is a function, but could modify to make it a set, or vary meanings

stipulative: if we’re going to do this, why not use a corpus directly?

Collocational account of distribution all the adjectives share a denotation corresponding

to magnitude (more details later), distribution differences due to collocation, soft rather than hard constraints

linguistically: adjective-noun combination is semi-productive denotation and syntax allow heavy esteem etc, but speakers

are sensitive to frequencies, prefer more frequent phrases with same meaning

cf morphology and sense extension: Briscoe and Copestake (1999)

blocking (but weaker than with morphology) anti-collocations as reflection of semi-productivity

Collocational account of distribution computationally, fits with some current

practice: filter adjective-noun realisations according

to n-grams (statistical generation – e.g., Langkilde and Knight)

use of co-occurrences in WSD back-off techniques

Collocational vs denotational differences

Collocation difference

Denotationdifference

high

low

heavy

Back-off and analogy back-off: decision for infrequent noun with no corpus

evidence for specific magnitude adjective based on productivity of adjective: number of nouns

it occurs with default to big

back-off also sensitive to word clusters e.g., heavy spindrift because spindrift is semantically similar

to snow semantic space models: i.e., group according to distribution

with other words hence, adjective has some correlation with semantics of the

noun

Metaphor again extended metaphor idea is consistent

with idea that clusters for backoff are based on semantic space

words cluster according to how they co-occur e.g., high words cluster with rise words?

but this doesn’t require that we interpret high literally and then coerce

More details: denotation of extended adjective uses mass: e.g., rain, and some plural e.g.,

casualties cf much, many

inherent measure: e.g., grade, percentage, fine

other: e.g., rainstorm, defeat, bombardment attribute in qualia has Magn – heavy rainstorm

equivalent to storm with heavy rain also heavy drinker etc

More details Different uses cross-cut adjective distinction

and domain categories Want to have single extended sense and

some form of co-composition Further complications: nouns with temporal

duration heavy rain – not the same as persistent rain heavy fighting but heavy drinking how much of this do we have to encode

specifically?

Connotation heavy often has negative connotations

heavy fine but not ? heavy reward etc heavy taxation versus high taxation

consistent with the semantic cluster / extended metaphor idea

Necessary experiments None of this is tested yet! Specify denotation, check for accuracy Implement semi-productivity model with

back-off Determine predictability of adjective based on

noun alone Extension to other adjectives? Magnitude

adjectives may be more lexical than others.

Conclusions Testing collocational account of

distribution requires fixing denotation Magnitude adjectives: assume same

denotation more complex denotations would need

different experiments Semi-productivity at the phrasal level

Back-off account is crucial

Some final comments denotation, selectional restriction,

collocation: choice between mechanisms?

ngrams for language models for speech recognition

variants of semantic space models that are less sensitive to collocation effects? can we `remove’ collocation?