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Computing Science, University of Aberdeen 1
CS4025: Semantics
Representing meaning Semantic interpretation Word meaning
For more information: J&M, chap 14, 16 in 1st ed; 17, 19 in 2nd
Computing Science, University of Aberdeen 2
NL Understanding
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 3
Stages
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 4
Example
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
Computing Science, University of Aberdeen 5
Definition
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
Computing Science, University of Aberdeen 6
Meaning
How can we represent the meaning of an English sentence?
Programming languages: “meaning” is the equivalent machine code
a = b +cmeans load a
add bstore c
We could represent meaning as programs in some language, in which case NLU would be a kind of “compilation”
Computing Science, University of Aberdeen 7
Meaning Representation in NL
Many possibilities– executable programs– logical formulas– AI knowledge representation– nothing
No consensus on what is best - basic problem in philosophy and psychology
Computing Science, University of Aberdeen 8
Criteria for an ideal MRL
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
Computing Science, University of Aberdeen 9
Example: John passed CS1001
Different representations» Program: C (or SQL) code to add an appropriate
entry to a student database» Logic: pass(John, CS1001)» AI Semantic Net
Pass CS1001JohnAgent Object
Computing Science, University of Aberdeen 10
Program as representation
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
Computing Science, University of Aberdeen 11
Logic as a Representation
Translate into (first-order) logicJohn is a man man(John)John eats spinach eat(John,spinach)John sold all of his stocks(X)(stock(X) & own(John,X)) sell(John, X))John sold Peter all of his stocks(X)(stock(X) & own(John,X)) sell(John,X,Peter))
Computing Science, University of Aberdeen 12
Logic as Representation (2)
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?
Computing Science, University of Aberdeen 13
Case Frames as a Representation
Form of (AI) semantic network Assume verbs (and other words) are objects
with relationsAGENT - the person/thing actingTHEME - the person/thing acted uponBENEFICARY - [of action]AT-LOC - where action happened
Computing Science, University of Aberdeen 14
Example
John gave Peter the ballJohn gave the ball to PeterThe ball was given to Peter by John
are all interpreted asGIVE
agent = Johntheme = ballbeneficiary = Peter
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Meaning Primitives
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 actJohn bought the book from SamSam sold the book to John
Difficult to define small set of primitives» Conceptual Dependency was one serious attempt
Computing Science, University of Aberdeen 16
Primitives» ATRANS - abstract transfer» PTRANS - physical transfer» MTRANS - mental transfer» PROPEL - apply force to an object» INGEST - eat, drink, etc» CON - conceptualise» etc
Conceptual Dependency
Computing Science, University of Aberdeen 17
Example: "John bought a book from Mary."
(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))))
Computing Science, University of Aberdeen 18
Example: "Bob threw the ball to Bill."
(PTRANS (ACTOR BOB) (OBJECT BALL) (FROM BOB) (TO BILL) (TIME PAST) (INSTRUMENT (PROPEL (ACTOR BOB) (OBJECT BALL) (FROM BOB) (TO BILL) (TIME PAST)))
Computing Science, University of Aberdeen 19
Knowledge Bases
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?
Computing Science, University of Aberdeen 20
MRLs and Logic
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
Computing Science, University of Aberdeen 21
Choosing an MRL: What is the Task?
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?
Computing Science, University of Aberdeen 22
Semantic Interpretation
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
Computing Science, University of Aberdeen 23
Ex: List the books
S: imperativeV: ListNP: 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
Semantic interpretation: Semantic grammar
Computing Science, University of Aberdeen 25
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.
Semantic Interpretation: Template spotting
Computing Science, University of Aberdeen 26
Doctor-on-Board Problem
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
Computing Science, University of Aberdeen 27
Lexical (Word) Meaning
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”
Computing Science, University of Aberdeen 28
Word meaning for time-series data
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
Computing Science, University of Aberdeen 29
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
Conclusion