From Syntax to From Syntax to SemanticsSemantics

How to get from Form to Meaning How to get from Form to Meaning in Two different waysin Two different ways

What is meaning?What is meaning?

Connection (grounding) in something Connection (grounding) in something outside itselfoutside itself

Mental concept (ideas)Mental concept (ideas) Objects and events in the world Objects and events in the world

(true/false)(true/false) Some combination of the aboveSome combination of the above Ultimately – the success of the program in Ultimately – the success of the program in

which it is embeddedwhich it is embedded

Principle of CompositionalityPrinciple of Compositionality

The meaning of the whole is derived from The meaning of the whole is derived from the meaning of the parts and the manner the meaning of the parts and the manner of their combinationof their combination

{John, kiss, Sally} {John, kiss, Sally} John kissed Sally.John kissed Sally. Sally kissed John.Sally kissed John.

Semantics -- For our purposesSemantics -- For our purposes

Formal representational language that Formal representational language that represents the “manner of combination”represents the “manner of combination”

Lexicon that connects lexical items with Lexicon that connects lexical items with some externally grounded object, the some externally grounded object, the “meaning of the parts”“meaning of the parts”

Two approachesTwo approaches

Logical Logical Language of formal logicLanguage of formal logic Model (set) theoretic groundingModel (set) theoretic grounding

InterlingualInterlingual Specially-developed InterLingual (IL) Specially-developed InterLingual (IL)

RepresentationRepresentation Ontology to represent word meaningOntology to represent word meaning

To some extent complementaryTo some extent complementary

Logical approachLogical approach

Predicate calculus and model theory PLUSPredicate calculus and model theory PLUS Extra stuff to handle some of the complexities of Extra stuff to handle some of the complexities of

natural language, such asnatural language, such as (Scope) Every man loves a woman.(Scope) Every man loves a woman. (Generics) Dogs have four legs.(Generics) Dogs have four legs. (Specificity) John wants to marry a Norwegian.(Specificity) John wants to marry a Norwegian. (Intension) What if all bald men are tall?(Intension) What if all bald men are tall? (Roles) The temperature is ninety and rising.(Roles) The temperature is ninety and rising.

Logical approach – Logical approach – λ calculusλ calculus

Key idea: semantic construction parallels Key idea: semantic construction parallels syntactic constructionsyntactic construction

John = John = john’john’ sleep = sleep = sleep’sleep’ John is sleeping = John is sleeping = sleep’(john’)sleep’(john’) sleep = sleep = λx[sleep’(x)]λx[sleep’(x)] John is sleeping = John is sleeping = λx[sleep’(x)](john’)λx[sleep’(x)](john’) Lambda conversion = Lambda conversion = sleep’(john’)sleep’(john’)

Logical approach – possible Logical approach – possible worldsworlds

Instead of one model – many modelsInstead of one model – many models Each model is a “possible world” – one is Each model is a “possible world” – one is

designated as “real”designated as “real” Temporal logicTemporal logic Modal logicModal logic Intensional logicIntensional logic

IL approachIL approach

Developed in the context of Machine TranslationDeveloped in the context of Machine Translation Interested in word sense disambiguationInterested in word sense disambiguation

The pig is in the pen.The pig is in the pen. The ink is in the pen.The ink is in the pen.

Non-literal language: metonymy/metaphorNon-literal language: metonymy/metaphor ““The White House reported today that …”The White House reported today that …” ““The business opened its doors in 1928.”The business opened its doors in 1928.”

Inferencing for translation mismatchesInferencing for translation mismatches

IL approach IL approach

An Ontology, a language-independent An Ontology, a language-independent classification of objects, event, relationsclassification of objects, event, relations

A Semantic Lexicon, which connects A Semantic Lexicon, which connects lexical items to nodes (concepts) in the lexical items to nodes (concepts) in the ontologyontology

An analyzer that constructs IL An analyzer that constructs IL representations and selects (an?) representations and selects (an?) appropriate oneappropriate one

IL approach – OntologyIL approach – Ontology

A classification tree in which mother node A classification tree in which mother node contains all below it, and daughter nodes are contains all below it, and daughter nodes are distinct (is-a links)distinct (is-a links)

Complications: expandable to a lattice, with non-Complications: expandable to a lattice, with non-exclusive daughter nodesexclusive daughter nodes

Inheritable features and relations (now looks Inheritable features and relations (now looks more like a dictionary)more like a dictionary)

““Instances” can hang from bottom nodes Instances” can hang from bottom nodes (providing grounding)(providing grounding)

Semantic lexiconSemantic lexicon

Provides a syntactic context for the Provides a syntactic context for the appearance of the lexical itemappearance of the lexical item

Provides a mapping for the lexical item to Provides a mapping for the lexical item to a node in the ontologya node in the ontology

Or more complex associationsOr more complex associations Also providing connections from syntactic Also providing connections from syntactic

context to semantic roles context to semantic roles And constraints on these rolesAnd constraints on these roles

Deriving basic semantic dependency (a toy example)

Input: John makes tools

Syntactic Analysis:cat verbtense presentsubject  

root johncat noun-proper

object  root     toolcat nounnumberplural

Relevant parts of the (appropriate sense of the lexical entry for make)

make-v1syn-struc

root makecat vsubj root $var1

cat nobject root $var2

cat nsem-struc

manufacturing-activityagent ^$var1theme ^$var2

Relevant Extract from the Specification of the Ontological Concept Used to Describe the Appropriate Meaning of make:

manufacturing-activity...

agent humantheme artifact

…

John-n1syn-struc

root johncat noun-proper

sem-struchuman

name johngender male

tool-n1syn-struc

root toolcat n

sem-structool

Relevant parts of the (appropriate senses of the)lexicon entries for John and tool

The basic semantic dependency component of the TMR forJohn makes tools is as follows:

…

manufacturing-activity-7

agent human-3theme set-1

element toolcardinality > 1

…

try-v3syn-struc

root trycat vsubj root $var1

cat nxcomp root $var2

cat vform OR infinitive gerund

sem-strucset-1 element-type refsem-1

cardinality >=1refsem-1 sem event

agent ^$var1effect refsem-2

modalitymodality-type epiteucticmodality-scope refsem-2modality-value < 1

refsem-2 value ^$var2sem event

Constructing an IL Constructing an IL representationrepresentation

For each syntactic analysisFor each syntactic analysis Access all semantic mappings and Access all semantic mappings and

contexts for each lexical itemcontexts for each lexical item Create all possible semantic Create all possible semantic

representationsrepresentations Test them for coherency of structure and Test them for coherency of structure and

contentcontent

REQUEST-INFO-130 THEME DEVELOP-2601.PURPOSE DEVELOP-2601.REASON TEXT-POINTER why INSTANCE-OF REQUEST-INFO

DEVELOP-2601THEME SET-2555AGENT NATION-97PHASE CONTINUOUS

TIME FIND-ANCHOR-TIME INSTANCE-OF DEVELOP

TEXT-POINTER developing

NATION-97HAS-NAME Iraq

INSTANCE-OF NATIONTEXT-POINTER Iraq

SET-2555 ELEMENT-TYPE WEAPONCARDINALITY > 1

INSTRUMENT-OF KILL-1864 THEME-OF DEVELOP-2601 INSTANCE-OF WEAPON

TEXT-POINTER weapons

KILL-1864 THEME SET-2556 INSTRUMENT SET-2555 INSTANCE-OF KILL

TEXT-POINTER destruction

SET-2556 THEME-OF KILL-1225 ELEMENT-TYPE HUMAN

CARDINALITY > 100 INSTANCE-OF HUMAN

TEXT-POINTER mass

“Why is Iraq developing weapons of mass destruction?”

Concluding questionConcluding question

Is all this really necessary?Is all this really necessary? Do we need it to do – Machine Do we need it to do – Machine

Translation, IR, IE, Q/A, summarization?Translation, IR, IE, Q/A, summarization? Can we “ground” the symbols of language Can we “ground” the symbols of language

without a special representation of the without a special representation of the “meaning”?“meaning”?

Word sense disambiguationWord sense disambiguation

Constraint checking – making sure the Constraint checking – making sure the constraints imposed on context are metconstraints imposed on context are met

Graph traversal – is-a links are Graph traversal – is-a links are inexpensiveinexpensive

Other links are more expensiveOther links are more expensive The “cheapest” structure is the most The “cheapest” structure is the most

coherentcoherent Hunter-gatherer processingHunter-gatherer processing