Investigating adjective denotation and collocation Ann Copestake Computer Laboratory, University of...
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Transcript of Investigating adjective denotation and collocation Ann Copestake Computer Laboratory, University of...
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