Semantics Going beyond syntax 1 Semantics Relationship between

  • Slides: 19
Download presentation
Semantics Going beyond syntax 1

Semantics Going beyond syntax 1

Semantics • Relationship between surface form and meaning • What is meaning? • Lexical

Semantics • Relationship between surface form and meaning • What is meaning? • Lexical semantics • Syntax and semantics 2

What is meaning? • Reference to “worlds” – Objects, relationships, events, characteristics – Meaning

What is meaning? • Reference to “worlds” – Objects, relationships, events, characteristics – Meaning as truth • Understanding – Inference, implication – Modelling beliefs • Meaning as action – Understanding activates procedures 3

Lexical semantics • Meanings of individual words – Sense and Reference – What do

Lexical semantics • Meanings of individual words – Sense and Reference – What do we understand by the word lion ? – Is a toy lion a lion? Is a toy gun a gun? Is a fake gun a gun? • Grammatical meaning – What do we understand by the lion, lions, the lions, … as in The lion is a dangerous animal The lion was about to attack 4

Lexical relations • Lexical meanings can be defined in terms of other words –

Lexical relations • Lexical meanings can be defined in terms of other words – Synonyms, antonyms, broader/narrower terms – synsets – Part-whole relationships (often reflect realworld relationships) – Linguistic usage (style, register) also a factor 5

Semantic features • Meanings can be defined (to a certain extent) in terms of

Semantic features • Meanings can be defined (to a certain extent) in terms of distinctive features – e. g. man = adult, male, human • Meanings can be defined (to a certain extent) in terms of distinctive features 6

Types of representation 1. Syntactic relations The man shot an elephant with his gun

Types of representation 1. Syntactic relations The man shot an elephant with his gun shot subj obj man elephant adv gun det mod the an his 7

Types of representation 2. Deep syntax The man shot an elephant with his gun

Types of representation 2. Deep syntax The man shot an elephant with his gun An elephant was shot by the man with his gun shot dsubj dobj instr man elephant gun qtf poss the qtf an his 8

Types of representation 3. Semantic roles, deep cases The man shot an elephant with

Types of representation 3. Semantic roles, deep cases The man shot an elephant with his gun An elephant was shot by the man with his gun shot agent patient The man used his gun to shoot an elephant instr man elephant gun qtf poss the qtf an his 9

Types of representation 4. Event representation, semantic network The man shot an elephant with

Types of representation 4. Event representation, semantic network The man shot an elephant with his gun An elephant was shot by the man with his gun shooting The man used his gun to shoot an elephant shooter shot- instr thing man elephant gun qtf the qtf poss man 10

Types of representation 5. Predicate calculus The man shot an elephant with his gun

Types of representation 5. Predicate calculus The man shot an elephant with his gun An elephant was shot by the man with his gun The man used his gun to shoot an elephant The man owned the gun which he used to shoot an elephant The man used the gun which he owned to shoot an elephant event(e) & time(e, past) & pred(e, shoot) & man(A) & the(A) & (B) & dog(B) & shoot(A, B) & (C) & gun(C) & own(A, C) & use(A, C, e) 11

Types of representation 6. Conceptual dependency (Schank) John punched Mary 12

Types of representation 6. Conceptual dependency (Schank) John punched Mary 12

Types of representation 7. Semantic formulae (Wilks) ((THIS((PLANT STUFF)SOUR)) ((((((THRU PART)OBJE) (NOTUSE *ANI))GOAL) ((MAN

Types of representation 7. Semantic formulae (Wilks) ((THIS((PLANT STUFF)SOUR)) ((((((THRU PART)OBJE) (NOTUSE *ANI))GOAL) ((MAN USE) (OBJE THING) ))) door 13

Uses for semantic representations • As a linguistic artefact (because it’s there) • To

Uses for semantic representations • As a linguistic artefact (because it’s there) • To capture the text meaning relationship • Identifying paraphrases, equivalences (e. g. summarizing a text, searching a text for information) • Understanding and making inferences (e. g. so as to understand a sequence of events) • Interpreting questions (so as to find the answer), commands (so as to carry them out), statements (so as to update data) 14 • Translating

Uses for semantic representations • Different levels of understanding/meaning • Textual meaning may be

Uses for semantic representations • Different levels of understanding/meaning • Textual meaning may be little more than disambiguating – Attachment ambiguities – Word-senses – Anaphora (pronoun reference, coreference) • Conceptual meaning may be much deeper • Somewhere in between – a good example is Wilks’ preference semantics: especially good for metaphor 15

Linguistic issues • Words and Concepts – Objects, properties, actions n, adj, v –

Linguistic issues • Words and Concepts – Objects, properties, actions n, adj, v – Language allows us to be vague (e. g. toy gun) • Semantic primitives – what are they? • Meaning equivalence – when do two things mean the same? • Grammatical meaning – Tense vs. time – Topic and focus – Quantifiers, plurals, etc. 16

Linguistic issues • There are many other similarly tricky linguistic phenomena – Modality (could,

Linguistic issues • There are many other similarly tricky linguistic phenomena – Modality (could, should, would, must, may) – Aspect (completed, ongoing, resulting) – Determination (the, a, some, all, none) – Fuzzy sets (often, some, many, usually) 17

Lexical semantics • Lexical relations (familiar to linguists) have an impact on NLP systems

Lexical semantics • Lexical relations (familiar to linguists) have an impact on NLP systems – Homonymy –word-sense selection; homophones in speech-based systems – Polysemy – understanding narrow senses – Synonymy – lexical equivalence – Ontology – structure vocabulary, holds much of the “knowledge” used by clever systems 18

Word. Net • Began as a psycholinguistic “theory” of how the brain organizes its

Word. Net • Began as a psycholinguistic “theory” of how the brain organizes its vocabulary (Miller) • Organizes vocabulary into “synsets”, hierarchically arranged together with other relations (hyp[er|o]nymy, isa, member, antonyms, entailments) • Turns out to be very useful for many applications • Has been replicated for many languages (sometimes just translated!) 19