Static Semantic Properties

Hot and Odd Topics in Semantics

Static Semantic Properties

Katharina Stein

04.05.2018 Static Semantic Properties 1

Outline

• Classes of semantic properties of entities

• Actual researches


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



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.



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.



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



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.



Specificity

• A few languages explicitly differentiate specifics from nonspecifics

Bemba:

• Differentiation per prefixication:

• VCV prefixes indicate specificity

• CV prefixes indicate nonspecificity



Specificity in Bemba

• aàfwaaya icitabo.

• a- à- fwaaya icitabo.

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.



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



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



Animacy in Indonesian

• se- orang mahasiswa

one Human student

• se- ekor kuda

one Animal horse

• se- buah buku

one Inanimate book



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



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



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



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



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



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



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



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



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.



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



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



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)



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



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.



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



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



Boundedness

• Semantically bounded: inherently demarcated, already specified limits

• Semantically unbounded: inherently open, incircumscribed regions in conceptual space



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



The Count / Mass Distinction

• Bounded entities are countable -> count nouns

• Unbounded entities are not countable -> mass nouns


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


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


Issues of Mass and Count: Dealing with ‚Dual-Life‘ Nouns

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



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



Data

• Intersection of the nouns of the American National Corpus withWordNet

→ nouns with definitions



Annotation Task

• Six tests chosen for relevance to the study of the Mass/Count distinction



Test Outcomes


• Senses grouped by the pattern of their answers


Test Outcomes


• Pattern for „dual-life noun-senses“: <yes, ¬num, yes, ¬equiv, yes, yes>

• 162 noun senses with this pattern


„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



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



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



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



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



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



Conclusion

• Home of the +MASS and +COUNT is a noun sense

• Dual-Life noun senses → distinction is not exclusive



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


Distributional Modeling on a Diet: One-shot Word Learning from Text Only

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?



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



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



Data



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



Results



Results



Results


• 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


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



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


Building a shared world: Mapping distributional to model-theoretic semantic spaces

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



Motivation

• Ambiguity of the bare plural

• Kim writes books vs Kim likes books

• Formalisation of the systematic dependencies between lexical and set-theoretic levels



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



Data


• Quantified McRae dataset• Animal Data


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



Results



Results


• Spearman ρ: Degree to which predicted values foreach dimension in a model-theoretic space correlatewith the gold annotations


Results

• Category-specific training data yields high performance when tested on the same category

• System reaches human-performance on a subset of the data



Generating natural language quantifiers

• Attemption to map the set-theoretic vectors back to natural languagequantifiers

• Goal: a system that produces quantified statements



Generating natural language quantifiers


• Conservative: prefers few to some and most to all

• Accuracy of producing „true“ quantified sentences = 73%


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



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.


Thank you for your attention


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Static Semantic Properties