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CS4025: Semantics
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
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 2
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 3
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 4
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 5
Meaning
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 ” Computing Science, University of Aberdeen 6
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 7
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 8
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 Object John Agent Pass CS1001 Computing Science, University of Aberdeen 9
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 10
Logic as a Representation
Translate into (first-order) logic
John is a man
man(John)
John eats spinach John sold all of his stocks
( X)(stock(X) & own(John,X))
John sold Peter all of his stocks
eat(John,spinach) sell(John, X)) ( X)(stock(X) & own(John,X)) sell(John,X,Peter)) Computing Science, University of Aberdeen 11
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 12
Case Frames as a Representation
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 Computing Science, University of Aberdeen 13
Example
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 Computing Science, University of Aberdeen 14
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 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 Computing Science, University of Aberdeen 15
Conceptual Dependency
Primitives » ATRANS - abstract transfer » PTRANS - physical transfer » MTRANS - mental transfer » PROPEL - apply force to an object » INGEST - eat, drink, etc » CON - conceptualise » etc Computing Science, University of Aberdeen 16
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 17
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 18
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?
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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 20
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?
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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 22
Ex: List the books
S: imperative V: List NP: X mapped into Select X.
Semantic interpretation: Semantic grammar
An attempt to reduce the categories » FLIGHT-NP -> Flight NUMBER » FLIGHT-NP -> Flight to CITY » FLIGHT-VP -> depart » FLIGHT-VP -> leave “ distance » FLIGHT-NP -> TIME flight to CITY ” » TIMEQ-> When does FLIGHT-NP FLIGHT-VP between syntactic and semantic representations Grammar is defined in terms of semantic Computing Science, University of Aberdeen 24
Semantic Interpretation: Template spotting
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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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 26
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 27
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 28
Conclusion
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 semantically interpret general text » but we can parse general text ’ t Computing Science, University of Aberdeen 29