static semantic properties
TRANSCRIPT
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Hot and Odd Topics in Semantics
Static Semantic Properties
Katharina Stein
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Outline
• Classes of semantic properties of entities
• Actual researches
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Semantic Properties of Entities
• Various ways to classify the properties of entities
• Eight (possible) classes of properties of entities:• Specificity• Animacy• Sex and gender• Kinship• Social status• Physical properties• Function• Boundedness
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Semantic Properties of Entities
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Specificity
• refers to the uniqueness or individuation of an entity in a mentally projected world
• I‘m looking for a man who speaks French.
• Two readings:
• Specific: I‘m looking for a particular man who speaks French.
• Nonspecific: I‘m looking for any man who speaks French.
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Semantic Properties of Entities
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Specificity
• refers to the uniqueness or individuation of an entity in a mentally projected world
• I‘m looking for a man who speaks French.
• Two readings:
• Specific: I‘m looking for a particular man who speaks French.
→ And I found him.
• Nonspecific: I‘m looking for any man who speaks French.
→ And I found one.
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Semantic Properties of Entities
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Specificity
• Often formal differentiation between specific and nonspecific entities
Spanish:
• Differentiation by the mood of the verb
• Specific: Busco a un hombre que habla francés → indicative mood
• Nonspecific : Busco a un hombre que hable francés → subjunctive mood
• specificity implies uniquely determined reference -> actual mood
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Semantic Properties of Entities
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Specificity: Factivity and Negation
• specificity is related to actual moods
• Expectation: specificity in factive context and nonspecificity in negative context
• I regretted reading a book. vs I didn‘t read a book.
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Semantic Properties of Entities
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Specificity
• A few languages explicitly differentiate specifics from nonspecifics
Bemba:
• Differentiation per prefixication:
• VCV prefixes indicate specificity
• CV prefixes indicate nonspecificity
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Semantic Properties of Entities
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Specificity in Bemba
• aàfwaaya icitabo.
• a- à- fwaaya ici- tabo.
he past want Spec book
He wanted the specific book.
• a- à fwaaya ci- tabo.
he past want Non-Spec book
He wanted some (any old) book.
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Semantic Properties of Entities
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Animacy
• Difference between biological and linguistic animacy
• Principle criteria for biological animacy: life and locomotion
• Principle criterion for linguistic animacy: influence of an entity over an execution of an event
• If an entity is more powerful or influential or valued it's more likely to be coded as animate → Animacy Hierarchy
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Semantic Properties of Entities
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Animacy
• Every language distinguishes between animates and inanimates
• Cultural reasons for a certain classification
Yagua:
• Animate: persons, spirits, animals, stars, the moon, months, mirrors, pictures, rocks, pineapples, brooms and fans
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Semantic Properties of Entities
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Animacy in Indonesian
• se- orang mahasiswa
one Human student
• se- ekor kuda
one Animal horse
• se- buah buku
one Inanimate book
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Semantic Properties of Entities
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Sex and Gender
• biological sex is needed to be differentiated from grammtical gender
• sex is semantic property
• gender is a formal or coding property
• sex and gender do not necessarily match
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Semantic Properties of Entities
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Sex and Gender
• in language only three sex distinctions are made:
• male, female, neuter
• Other not productive distinctions:
• e.g. entities with properties of both sexes
• e.g. castrated males
• different number of classes and different categorization in different languages
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Semantic Properties of Entities
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Sex and Gender in Dyirbal
• Four noun classes
• Class 1: certain animals, culturally valued objects and all human males
• Class 2: water, fire, fighting implements, certain animals and human females
• Class 3: nonflesh food
• Class 4: the rest
• nyalŋga → child, bayi → Class 1, balan → Class 2
• Bayi nyalŋga → boy, balan nyalŋga → girl
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Semantic Properties of Entities
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Kinship
• Familial relations among humans
• Relational System
• Focal point from which the rest of the system is seen = ego
• The other part of the relation = alter
• Three semantic properties: • Consanguinity: consanguineal vs affinal• Lineality: lineal vs collateral• Generation
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Semantic Properties of Entities
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Kinship in Seneca
• ha?nih defines a paternal, male, consanguineal and one relation
→ Father
→ uncle on the father‘s side
→ great grandfather‘s brother‘s son‘s son
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Semantic Properties of Entities
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Kinship in Mari‘ngar
• Quite different kin terms than in English• Tyan‘ angga → father‘s father / mother‘s mother
• Tamie → father‘s mother / mother‘s father
• Nea → man‘s child
• Mulugu → woman‘s daughter
• Magu → woman‘s son
• Wam:a → any third generation relative
• 18 basic kin terms at all
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Semantic Properties of Entities
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Social Status
• Encoding of nonfamilial, social relations
• Every language has a way to signal relative social rank
• Four features of social status: stable, narrowly structured, gradient, encoded in a variety of forms
• Honorific: grammatical or morphosyntactical encoding of social status
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Semantic Properties of Entities
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Social Status
• Social status = relation between two entities
• Relation between the speaker and
• The hearer → S/H System
• An entity spoken about → S/R System
• A situation → S/S System
• A bystander → S/B System
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Semantic Properties of Entities
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Social Status in Japanese
In the S/H System:
• Ame ga hur- masi- ta
rain Subj fall Hearer Status fall
Speaking from me to you, and you have higher status, it rained.
It rained.
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Semantic Properties of Entities
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Social Status in Japanese
In the S/R System:
• Yamada- sensei ga tegami o o- aki- ni nar- u.
Yamada teacher Subj letter Obj Hon write Subj High Pres
Respected teacher Yamada writes letters.
Teacher Yamada writes letters
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Semantic Properties of Entities
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Physical Properties
• Focus on inanimate entities
• Physical properties: characteristics as spatial object
• Four general properties: extendedness, interioricity, size, consistency
• A number of others like arrangement, aggregates, time, material
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Semantic Properties of Entities
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Physical Properties: Extendedness
• Two subcategories: dimensionality and shape
• Many languages encode dimensionality directly, some also shape
• Cree:
• Kinw- ēk- an
long two dimensions it is
It is long (i.e. like a piece of cloth)
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Semantic Properties of Entities
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Physical Properties: Direction
• Distinction between vertically and horizontally extended entities
• e.g. in American Sign Language
• Toba: • nkotragañi ra- wakalče
he spills Vert Ext milkHe is spilling milk downward
• nkotragañi ĵi- wakalčehe spills Horiz Ext milkHe is spilling milk across
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Semantic Properties of Entities
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Two Examples
• Yagua:
• Celina- jųy suuta- jááy- rà sújay mїї- jày.
Celina dual wash Near Past Inanimate cloth dirty Inan/2D
Celina (a married woman) washed the dirty cloth yesterday.
• Jacaltec:
• xul naj Pel b‘oj ya? Malin.
came Adult/Male/Non-kin Peter with Respect/Human Mary
An unrelated adult male, Peter, came with a respected human, Mary.
Peter came with Mary.
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Semantic Properties of Entities
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Function
• In many languages there are ways of marking the specific uses of entities or the kinds of actions that are performed on them
• Wide range of functions encoded in different languages
• Often function-based classes are language-specific and connected to culture
• Only few regularities
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Semantic Properties of Entities
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Function
• Common function properties:• Edibility
• Vehicular transport
• Speaking
• Cutting/piercing and the instruments for these actions
• Tzeltal: differentiates edibles by consistency, size, sweetness
• Yidiny: distinguishes between flesh and nonflesh food
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Semantic Properties of Entities
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Boundedness
• Semantically bounded: inherently demarcated, already specified limits
• Semantically unbounded: inherently open, incircumscribed regions in conceptual space
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Semantic Properties of Entities
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Boundedness: Features
• Features of the bound itself:• Boundedness
• depends on the universe of the discourse at the time of speech
• can be real or virtual
• is an inherent property of entities
• is fuzzy
• Features of internal structure:• Internal homogeneity
• Expansibility
• Replicability
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Semantic Properties of Entities
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The Count / Mass Distinction
• Bounded entities are countable -> count nouns
• Unbounded entities are not countable -> mass nouns
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Count nouns Mass nouns
• can be pluralized• occur with indefinite
determiner• quantifiers as each,
every
• numeral modifiers
• always occur in singular• with measure terms
(much)• nondistributive
quantifiers as most, all and some
Semantic Properties of Entities
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Issues of Mass and Count
• Standard presumptions:
• The locus of the distinction is the lexical noun
• The distinction is exhaustive and exclusive
• Account of the study: deny all these presumptions
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Issues of Mass and Count: Dealing with ‚Dual-Life‘ Nouns
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Home of the Mass/Count Distinction
• Usual assumption: lexical noun is the home
• Problem:
• Mary put a little chicken into the salad
• Ambiguity
• Change from +COUNT to +MASS
→ Assumption doesn‘t hold
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Issues of Mass and Count: Dealing with ‚Dual-Life‘ Nouns
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Goals of the Study
1. Investigate the possibility that the home of +MASS and +COUNT is not the lexical noun but a given sense of a noun
2. Display individual noun-senses that are simoultaneously +MASS and +COUNT
→ „Dual-Life“ noun-senses
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Issues of Mass and Count: Dealing with ‚Dual-Life‘ Nouns
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Data
• Intersection of the nouns of the American National Corpus withWordNet
→ nouns with definitions
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Issues of Mass and Count: Dealing with ‚Dual-Life‘ Nouns
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Annotation Task
• Six tests chosen for relevance to the study of the Mass/Count distinction
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Issues of Mass and Count: Dealing with ‚Dual-Life‘ Nouns
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Test Outcomes
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• Senses grouped by the pattern of their answers
Issues of Mass and Count: Dealing with ‚Dual-Life‘ Nouns
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Test Outcomes
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• Pattern for „dual-life noun-senses“: <yes, ¬num, yes, ¬equiv, yes, yes>
• 162 noun senses with this pattern
Issues of Mass and Count: Dealing with ‚Dual-Life‘ Nouns
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„Dual Life“ senses
• Senses of Nominally-Oriented nouns (57):
• Nouns on there own
• Nouns compounded from nouns and possibly other non-verb partsof speech
• Senses of Verbally-Oriented nouns (96)
• Nouns that rely on a sense of a verb
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Issues of Mass and Count: Dealing with ‚Dual-Life‘ Nouns
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Nominally-Oriented Nouns Senses
• Noun type associated with food
• Animal-designating meaning is count, flesh-designating meaning ismass
• „Fence-nouns“
• „-sides“ dual life group
• „-land“ dual life group
• Kind-Instance
• General meaning is mass and the more individual meaning is count
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Issues of Mass and Count: Dealing with ‚Dual-Life‘ Nouns
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Verbally-Oriented Noun Senses
• Event-Result:
• Ambiguity of „event nouns“
• One meaning that describes the acitivty, action, event or process
• One meaning that describes the result of the activity
• e.g. collection
• e.g. emission
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Issues of Mass and Count: Dealing with ‚Dual-Life‘ Nouns
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Verbally-Oriented Noun Senses
• Kind-Instance:
• Event-noun as a general term for a kind or type that has instances
• Event „meaning“ is seen as mass
• Instance „meaning“ is seen as count
• e.g. fantasy
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Issues of Mass and Count: Dealing with ‚Dual-Life‘ Nouns
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Borderline Cases
• A few cases which seem to be equally Verbally- and Nominally-Oriented:
• regret
• Often difficult to distinguish between Verbally-Oriented Event-Resultand Kind-Instance relationship
• No sharp distinction
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Issues of Mass and Count: Dealing with ‚Dual-Life‘ Nouns
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Event-Result vs Kind-Instance
• Event’s happening suggests a cause for the result → Event-Result "meaning"
• Event seems not to play any role in the formation, causation or extistence → Kind-Instance "meaning“
• Sometimes there are two possible perspectives on the relation
• e.g. imperfection#1: the state or an instance of being imperfect
• Such nouns manifest both types equally
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Issues of Mass and Count: Dealing with ‚Dual-Life‘ Nouns
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Conclusion
• Home of the +MASS and +COUNT is a noun sense
• Dual-Life noun senses → distinction is not exclusive
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Issues of Mass and Count: Dealing with ‚Dual-Life‘ Nouns
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One-shot Word Learning from Text only
• We found a cute, hairy wampimuk sleeping under a tree.
• Distributional models:
• Need hundreds of instances of word to derive a goodrepresentation of it
• Humans:
• Can infer a passable approximate meaning from only one sentence
→ fast mapping
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Distributional Modeling on a Diet: One-shot Word Learning from Text Only
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Goals of the Study
• Can distributional models do one-shot learning of definitionalproperties from textual context only?
• Explore a plausible probabilistic distributional model for fast mappinglearning
• What kinds of overarching structure in distributional context and in properties are helpful?
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Distributional Modeling on a Diet: One-shot Word Learning from Text Only
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Hypotheses
It is helpful to learn:
1. About similarities between context items
• e.g. eat-dobj and cook-dobj should prefer similar contexts
2. Co-occurrence patterns between properties
• e.g. from learning that an entity is mammal it‘s possible to inferthat it is four-legged
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Distributional Modeling on a Diet: One-shot Word Learning from Text Only
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Data
• Quantified McRae dataset:
• Mc Rae dataset: set of 7257 concept-feature pairs
• Natural language quantifier expressing the proportion of categorymembers that have a property
• all -> 1, most -> 0.95, some -> 0.35, few -> 0.05 and none -> 0
• Animal dataset
• British National Corpus
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Distributional Modeling on a Diet: One-shot Word Learning from Text Only
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Data
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Distributional Modeling on a Diet: One-shot Word Learning from Text Only
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Models
• Count-based Models:
• Count Independent and Count Multinomial
• Implement neither of the two hypotheses
• Bimodal Topic Model:
• Implements both hypotheses
• Bernoulli Mixtures:
• Count BernMix H1
• Count BernMix H2
• Bi-TM BernMix H2
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Distributional Modeling on a Diet: One-shot Word Learning from Text Only
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Results
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Distributional Modeling on a Diet: One-shot Word Learning from Text Only
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Results
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Distributional Modeling on a Diet: One-shot Word Learning from Text Only
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Results
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• bi-TM is much better→ confirms hypotheses
• AP varies widely across sentences• Average over all is close to
baseline• Most informative instances yield
excellent information aboutunknown context
Distributional Modeling on a Diet: One-shot Word Learning from Text Only
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Conclusion
• Learning word properties it‘s helpful to use
• Distributional context
• Co-occurences of properties
• Combination of both
• Some contexts are highly informative
• AvgCos achieves some success in predicting which contexts are mostuseful
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Mapping distributional to model-theoreticspaces
• Complementary of distributional semantics and formal semantics
• Would be desirable to have an overarching semantics whichintegrates distributional and formal aspects
→ Formal Distributional Semantics
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Motivation
• People can make complex inferences about statements even if theydo not have access to their real-world reference
• The kouprey is a mammal
• Systems can model entailment between quantifiers but rely on explicit representation of the quantifiers
• All koupreys are mammals → This kouprey is a mammal
• * Koupreys are mammals → This kouprey is a mammal
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Motivation
• Ambiguity of the bare plural
• Kim writes books vs Kim likes books
• Formalisation of the systematic dependencies between lexical and set-theoretic levels
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Goals of the Study
• Approach to automatically map a distributional semantic space onto a set-theoretical model
• Generation of high-quality vector representations
• Generation of natural language quantifiers from such vectors
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Data
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• Quantified McRae dataset• Animal Data
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Experimental Setup
• Function f: DS → MT
• Transforms a distributional semantic vector for a concept to itsmodel-theoretic equivalent
• Mapping learned as a linear relationship
• Estimation of the coefficients via partial least squares regression
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Results
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Results
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• Spearman ρ: Degree to which predicted values foreach dimension in a model-theoretic space correlatewith the gold annotations
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Results
• Category-specific training data yields high performance when tested on the same category
• System reaches human-performance on a subset of the data
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Generating natural language quantifiers
• Attemption to map the set-theoretic vectors back to natural languagequantifiers
• Goal: a system that produces quantified statements
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Generating natural language quantifiers
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• Conservative: prefers few to some and most to all
• Accuracy of producing „true“ quantified sentences = 73%
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Conclusion
• Given a reasonable amount of training data for a category we can proficiently generate model-theoretic representations for concept-feature pairs from a distributional space
• Reaching human performance on domain-specific test sets
• Generating of natural language quantifiers from vectorialrepresentations
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References
• Linguistic Semantics by William Frawley
• e.g. Kiss et al. (*SEM 2017). Issues of Mass and Count: Dealing with "Dual-Life" Nouns
• Wang, Roller, and Erk (IJCNLP 2017). Distributional modeling on a diet: one-shot learning from text only
• Herbelot and Vecchi (EMNLP 2015). Mapping distributional tomodel-theoretic semantic spaces.
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Thank you for your attention
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