l l l
Representing meaning
Semantic interpretation
Word meaning
For more information: J&M, chap 14, 16 in
1 st ed; 17, 19 in 2 nd
Computing Science, University of Aberdeen 1

Understanding written text
» Which books are bestsellers
» Who wrote them
For now, focus on “ AI ” approach
» explicit models of grammar, meaning, etc
Computing Science, University of Aberdeen 2

Morphology: analyse word inflection
Syntax: determine grammatical structure
Semantics: convert to a form that is meaningful to a computer
» eg, SQL query
Pragmatics: influence of context
» eg, what them refers to
Computing Science, University of Aberdeen 3

Original: Who wrote them morph: who write/past them
Grammar: [verb=write, subject=who, object=them] semantics: Select title, firstname, lastname from [X] pragmatics:
» Select title, firstname, lastname from books
» Where salesthisyear >10000
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Semantic interpretation rewrites a parse tree into a “ meaning representation ”
» Logic, SQL, knowledge base
Poorly understood compared to syntax
» apps that need complex semantics, like database front ends or high-quality MT, have had limited success in the past
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How can we represent the meaning of an English sentence?
Programming languages: “ meaning ” is the equivalent machine code a = b +c means load a add b store c
We could represent meaning as programs in some language, in which case NLU would be a kind of
“ compilation ”
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Many possibilities
– executable programs
– logical formulas
– AI knowledge representation
– nothing
No consensus on what is best - basic problem in philosophy and psychology
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Unambiguous
Able to express all necessary shades of meaning for the application domain
Verifiability – system can tell whether a statement is true according to a knowledge base
Canonical – different sentences with the same meaning are mapped to the same representation
Support of inference
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Different representations
» Program: C (or SQL) code to add an appropriate entry to a student database
» Logic: pass(John, CS1001)
» AI Semantic Net
John
Agent
Pass
Object
CS1001
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Translate English into SQL (C, ...)
» MS English Query / AccessELF
– “ List the bestsellers ” translated into “ Select titles from books where sales>10000 ”
» Usually need a different translator for each application
– Good authoring environments for semantic rules are essential
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Translate into (first-order) logic
John is a man
John eats spinach
John sold all of his stocks
(  X)(stock(X) & own(John,X))  sell(John, X))
John sold Peter all of his stocks man(John) eat(John,spinach)
(  X)(stock(X) & own(John,X))  sell(John,X,Peter))
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Good points
» Can represent any meaning (if you are inventive enough about predicates etc.)
» Good support for compositionality, arbitrarily complex statements
» Good support for quantifiers (all, some,...)
Bad points
» Doesn ’ t seem to really match the way people think.
– does  really mean some?
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Form of (AI) semantic network
Assume verbs (and other words) are objects with relations
AGENT - the person/thing acting
THEME - the person/thing acted upon
BENEFICARY - [of action]
AT-LOC - where action happened
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John gave Peter the ball
John gave the ball to Peter
The ball was given to Peter by John are all interpreted as
GIVE agent = John theme = ball beneficiary = Peter
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Meaning primitives are a fixed set of concepts/ roles etc. in terms of which any meaning can be expressed
Makes reasoning, e.g. about whether two meanings are the same, simpler.
Example: PURCHASE act
John bought the book from Sam
Sam sold the book to John
Difficult to define small set of primitives
» Conceptual Dependency was one serious attempt
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Primitives
» ATRANS - abstract transfer
» PTRANS - physical transfer
» MTRANS - mental transfer
» PROPEL - apply force to an object
» INGEST - eat, drink, etc
» CON - conceptualise
» etc
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(BI-CAUSE
(SOURCE (ATRANS (ACTOR MARY)
(OBJECT BOOK)
(FROM MARY)
(TO JOHN)
(TIME PAST)))
(TARGET (ATRANS (ACTOR JOHN)
(OBJECT MONEY)
(FROM JOHN)
(TO MARY)
(TIME PAST))))
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(PTRANS (ACTOR BOB)
(OBJECT BALL)
(FROM BOB)
(TO BILL)
(TIME PAST)
(INSTRUMENT (PROPEL (ACTOR BOB)
(OBJECT BALL)
(FROM BOB)
(TO BILL)
(TIME PAST)))
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Represent meaning using objects in a large AI knowledge base
» CYC project - 15-year project to build a knowledge base which holds the kind of general world knowledge that people have
» Use Cyc primitives and KR language to represent meaning?
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Most existing meaning representation languages
(frames, semantic nets, case frames etc). can be viewed as subsets of First Order Logic (where the expressive power is restricted or the set of predicates etc. is partially determined)
Main deficiencies of first order logic – inability to express default inferences and inferences based on partial information
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Why are we processing this sentence? This could influence the kind of meaning representation chosen
» database interface - perhaps use SQL rep?
» AI system which reasons about John ’ s problems perhaps use logic or AI KR?
» Information retrieval, speech dictation, grammar checking - don ’ t build any meaning representation?
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Rewriting the parse tree into the target representation
May be based on rewrite rules that insert a semantic structure X if the parse tree contains syntactic structure Y
For generality/coverage, needs to be
compositional, that is the meaning of the whole is some fixed function of the meanings of the parts
More on this in the next lecture
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S: imperative
V: List
NP: X mapped into
Select X.<name> from X
There are also cheaper/simpler approaches to semantic interpretation in use…
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An attempt to reduce the “ distance ” between syntactic and semantic representations
Grammar is defined in terms of semantic categories
» TIMEQ-> When does FLIGHT-NP FLIGHT-VP
» FLIGHT-NP -> Flight NUMBER
» FLIGHT-NP -> Flight to CITY
» FLIGHT-NP -> TIME flight to CITY
» FLIGHT-VP -> depart
» FLIGHT-VP -> leave
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Look for patterns (either in text or parse tree) which identify meaning fragments
» Example: How much is a ticket to London?
» How much specifies cost query
» a ticket specifies a single one-way ticket
» to London specifies destination
Must be in limited domain
Patterns looked for can be informed by knowledge about how words relate to underlying concepts and what syntactic properties words have.
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Simple rewriting may not be sufficient. Example:
– Is there a doctor within 200 miles of the Enterprise
» Database doesn ’ t have Doctor entities, instead it has
DoctorOnBoard attr for ships
» Need to rephrase this as
– Is there a ship within 200 miles of the Enterprise which has a doctor on board?
» Restructure query from human ’ s data model to database ’ s data model
Distance between syntactic and semantic structure significant in this example
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Logic (classical) model
» bachelor(X) = male(X) & adult(X) & ¬married(X)
– But: the pope? Divorcee? Muslim with 3 wives?
» Father(X) = male(X)&parent(X)
– Man who adopts a child?
– Sperm-bank donor?
– Unmarried partner to woman raising a child?
– Unmarried (gay) partner to man raising a child?
Prototype/exemplar models may be better when words don ’ t have formal “ definitions ”
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Weather reports
» Saturday will be yet another generally dull day with early morning mist or fog and mainly cloudy skies being prevalent. There will be the odd bright spell here and there, but it will feel rather damp with patches of mainly light rain to be found across many parts, especially the west and south.
Ongoing research project in CS Dept
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Converting sentences to a “ meaning representation ” is hard
» No agreement on best meaning-rep
» Word meaning is hard to pin down
Limited success in small domains, but we can ’ t semantically interpret general text
» but we can parse general text
Computing Science, University of Aberdeen 29