1. From Syntax to Semantics
How to get from Form to Meaning in Two different ways

2. What is meaning? Connection (grounding) in something outside itself
Mental concept (ideas)
Objects and events in the world (true/false)
Some combination of the above
Ultimately – the success of the program in which it is embedded

3. Principle of Compositionality
The meaning of the whole is derived from the meaning of the parts and the manner of their combination
{John, kiss, Sally}
John kissed Sally.
Sally kissed John.

4. Semantics -- For our purposes
Formal representational language that represents the “manner of combination”
Lexicon that connects lexical items with some externally grounded object, the “meaning of the parts”

5. Two approaches Logical Interlingual To some extent complementary
Language of formal logic
Model (set) theoretic grounding
Interlingual
Specially-developed InterLingual (IL) Representation
Ontology to represent word meaning
To some extent complementary

6. Logical approach Predicate calculus and model theory PLUS
Extra stuff to handle some of the complexities of natural language, such as
(Scope) Every man loves a woman.
(Generics) Dogs have four legs.
(Specificity) John wants to marry a Norwegian.
(Intension) What if all bald men are tall?
(Roles) The temperature is ninety and rising.

7. Logical approach – λ calculus
Key idea: semantic construction parallels syntactic construction
John = john’
sleep = sleep’
John is sleeping = sleep’(john’)
sleep = λx[sleep’(x)]
John is sleeping = λx[sleep’(x)](john’)
Lambda conversion = sleep’(john’)

8. Logical approach – possible worlds
Instead of one model – many models
Each model is a “possible world” – one is designated as “real”
Temporal logic
Modal logic
Intensional logic

9. IL approach Developed in the context of Machine Translation
Interested in word sense disambiguation
The pig is in the pen.
The ink is in the pen.
Non-literal language: metonymy/metaphor
“The White House reported today that …”
“The business opened its doors in 1928.”
Inferencing for translation mismatches

10. IL approach
An Ontology, a language-independent classification of objects, event, relations
A Semantic Lexicon, which connects lexical items to nodes (concepts) in the ontology
An analyzer that constructs IL representations and selects (an?) appropriate one

11. IL approach – Ontology
A classification tree in which mother node contains all below it, and daughter nodes are distinct (is-a links)
Complications: expandable to a lattice, with non-exclusive daughter nodes
Inheritable features and relations (now looks more like a dictionary)
“Instances” can hang from bottom nodes (providing grounding)

14. Semantic lexicon
Provides a syntactic context for the appearance of the lexical item
Provides a mapping for the lexical item to a node in the ontology
Or more complex associations
Also providing connections from syntactic context to semantic roles
And constraints on these roles

15. Deriving basic semantic dependency (a toy example)
Input: John makes tools
Syntactic Analysis:
cat verb tense present subject   root john cat noun-proper object   root     tool cat noun number plural

16. Relevant parts of the (appropriate sense of the lexical entry for make)
make-v1
syn-struc root make cat v subj root $var1 cat n object root $var2 cat n
sem-struc manufacturing-activity agent ^$var1 theme ^$var2

17. Relevant Extract from the Specification of the Ontological Concept Used to Describe the Appropriate Meaning of make:
manufacturing-activity agent human theme artifact …

18. Relevant parts of the (appropriate senses of the)
lexicon entries for John and tool
John-n1 syn-struc root john cat noun-proper sem-struc human name john gender male
tool-n1 syn-struc root tool cat n sem-struc tool

19. The basic semantic dependency component of the TMR for John makes tools is as follows:…
manufacturing-activity-7
agent human-3 theme set element tool cardinality > 1

20. try-v3
syn-struc
root try
cat v
subj root $var1
cat n
xcomp root $var2
form OR infinitive gerund
sem-struc
set-1 element-type refsem-1
cardinality >=1
refsem-1 sem event
agent ^$var1
effect refsem-2
modality
modality-type epiteuctic
modality-scope refsem-2
modality-value < 1
refsem-2 value ^$var2
sem event

21. Constructing an IL representation
For each syntactic analysis
Access all semantic mappings and contexts for each lexical item
Create all possible semantic representations
Test them for coherency of structure and content

22. “Why is Iraq developing weapons of mass destruction?”

23. Concluding question Is all this really necessary?
Do we need it to do – Machine Translation, IR, IE, Q/A, summarization?
Can we “ground” the symbols of language without a special representation of the “meaning”?

24. Word sense disambiguation
Constraint checking – making sure the constraints imposed on context are met
Graph traversal – is-a links are inexpensive
Other links are more expensive
The “cheapest” structure is the most coherent
Hunter-gatherer processing