1. 74.793 NLP and Speech 2004 Semantics I Semantics II
General Introduction
Types of Semantics
From Syntax to Semantics
Semantics II
Desiderata for Representation
Logic-based Semantics

2. Semantics I

3. Semantics Distinguish between
surface structure (syntactic structure) and
deep structure (semantic structure) of sentences.
Different forms of Semantic Representation
logic formalisms
ontology / semantic representation languages
Case Frame Structures (Filmore)
Conceptual Dependy Theory (Schank)
DL and similar KR languages
Ontologies

4. Semantic Representations
Semantic Representations based on some form of (formal) Representation Language.
Semantics Networks
Conceptual Dependency Graphs
Case Frames
Ontologies
DL and similar KR languages

5. Constructing a Semantic Representation
General:
Start with surface structure
Derived from parser.
Map surface structure to semantic structure
Use phrases as sub-structures.
Find concepts and representations for central phrases (e.g. VP, NP, then PP)
Assign phrases to appropriate roles around central concepts (e.g. bind PP into VP representation).

6. Ontology (Interlingua) approach
Ontology: a language-independent classification of objects, events, relations
A Semantic Lexicon, which connects lexical items to nodes (concepts) in the ontology
An analyzer that constructs Interlingua representations and selects (an?) appropriate one
(based on Steve Helmreich's 419 Class, Nov 2003)

7. 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)
Provides connections from the syntactic context to semantic roles and constraints on these roles

8. Deriving Basic Semantic Dependency
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

9. 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

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

11. 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

12. Semantic Dependency Component
The basic semantic dependency component of the TMR for John makes tools
manufacturing-activity-7
agent uman-3 theme set element tool cardinality > 1 …

13. 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

14. 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.

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

16. 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

17. Semantics II

18. Representation of Meaning
Representation of meaning for natural language sentences:
Semantic Representation Language (in most cases) = some kind of formal language + semantic primitives
For example: First Order Predicate Logic with specific set of predicates and functions

19. Semantic Representations
Semantic Representation based on some form of (formal) Representation Language.
Semantics Networks
Conceptual Dependency Graphs
Case Frames
Ontologies
DL and similar KR languages
First-Order Predicate Logic

20. Semantics - Connecting Words and Worlds
Semantic Representation
NL Input
Knowledge Representation
NL Output
World State
(KB: T-Box, A-Box)

21. Desiderata for a Semantic Representation
Verifiability – semantic representation must be compatible with knowledge (base) of the system.
Canonical Form - assign same representation to different surface expressions which have essentially the same meaning
Ambiguity and Vagueness – representation should (in relation to knowledge base or information system access etc.) be unambiguous and precise

22. Example - NL Database Access
Imagine a database access using natural language, i.e. questions to the DB posed in natural language.
Example: DB of courses in the CS department
Pose questions like:
Who is teaching Advanced AI in Fall 2004?
Is John Anderson teaching this term?
What is John Anderson teaching this term?
Who is teaching AI at the University of Winnipeg?
Who is teaching an AI related course this term?

23. Example Story: My car was stolen two weeks ago.
They found it last week.
direct representation of meaning
knowledge
inference

24. Example car (my_car) stolen (my_car, t1), found (police, my-car, t2)
t1<t2
stolen (x, t1) and
found (police, x, t2) implies
has (owner (x), x, t3) with t3>t2
What can you infer if you instantiate x with my_car?

25. Example stolen (x, t1) and found (police, x, t2) implies
has (owner (x), x, t3) with t3>t2
Express that if something is stolen, the owner does not have it!

26. Predicate-Argument Structure
Verb-centered approach
Thematic roles, case roles
Describe semantic structure based on verb and associated roles filled by other parts of the sentence (phrases).
Representation using e.g. logic:
Transform structured input sentence (syntax!) into expression in predicate logic.
Usually based on central predicate, the verb, or equivalent, like ‘be’+ adjective etc.
Other parts of the sentence directly related to the verb go into the central predicate.

27. Verb Subcategorization
Consider possible subcat frames of verbs.
Example: 3 different kinds of want:
NP want NP I want money.
want1(Speaker, money) or want1(I, money)
NP want Inf-VP He wants to go home.
want2(he, go home)
NP want NP Inf-VP I want him to go away.
want3(I, him, go_away)

28. Example - Restaurant 'Maharani'
Maharani serves vegetarian food.
Maharani is a vegetarian restaurant.
Maharani is close to ICSI.
Write down logical formulas representing the three different sentences.

29. Logic Formalisms
Lambda Calculus

30. Semantics - Lambda Calculus 1
Logic representations often involve Lambda-Calculus:
represent central phrases (e.g. verb) as -expressions
-expression is like a function which can be applied to terms
insert semantic representation of complement or modifier phrases etc. in place of variables
x, y: loves (x, y) FOPL sentence
xy loves (x, y) -expression, function
xy loves (x, y) (John)  y loves (John, y)

31. Semantics - Lambda Calculus 2
Transform sentence into lambda-expression:
“AI Caramba is close to ICSI.”
specific: close-to (AI Caramba, ICSI)
general: x,y: close-to (x, y)  x=AI Caramba  y=ICSI
Lambda Conversion:
-expr: xy: close-to (x, y) (AI Caramba)
Lambda Reduction:
y: close-to (AI Caramba, y)
close-to (AI Caramba, ICSI)

32. Semantics - Lambda Calculus 3
Lambda Expressions can be constructed from central expression, inserting semantic representations for complement phrases
Verb  serves
{xy e IS-A(e, Serving)  Server(e,y)  Served(e,x)}
represents general semantics for the verb 'serve
Fill in appropriate expressions for x, y, for example 'meat' for y derived from Noun in NP as complement to Verb.

33. References
Jurafsky, D. & J. H. Martin, Speech and Language Processing, Prentice-Hall, (Chapters 9 and 10)
Helmreich, S., From Syntax to Semantics, Presentation in the Course, November 2003.