Natural Language Processing
Unit 4
Meaning Representation: Capturing the meaning of of
linguistic utterances using formal notation.
Meaning Representation Languages: Frameworks that are
used to specify the syntax and semantics of these meaning
representations.
Semantic Analysis: Mapping the linguistic utterances to
these meaning representations.
Correct meaning representation should be selected for the
application.
For certain language tasks require some form of semantic
processing: – following a recipe – answering an essay
question in exam
Let us look at four frequently used meaning representation
languages.
– First Order Predicate Calculus
– Semantic Network
– Conceptual Dependency Diagram
– Frame-Based Representation
Meaning Representation Example - I have a car
What do We Expect from Meaning Representations?
To be computationally effective, we expect certain properties in meaning
representations:
– Verifiability -- Ability to determine the truth value of the
representation.
– Unambiguous Representations -- A representation must be
unambiguous.
– Canonical Form -- Utterances which means the same thing should map
to the same meaning representation.
– Inference and Variables -- Ability to draw valid conclusions based on
the meaning representations of inputs and the background knowledge.
– Expressiveness -- Ability to express wide range of subject matter.
Verifiability
Verifiability -- Ability to determine the truth value of the representation
by looking at the information available in the knowledge base.
• Example:
– Assume that we have the entry serve(Subway,VegetarianFood) in our
KB.
– Question: Does Subway serve vegetarian food?
– The question should be converted into a logical form (a meaning
representation).
– We should able to verify the truth value of the logical form of the
question against our KB.
Unambiguous Representations
Unambiguous Representations -- A meaning representation must be
unambiguous.
• Example:
– Assume that we are looking the representation of “I want to eat some
place near Bilkent”.
– There will be different meanings of this sentence, and we will prefer
one of them.
– But that chosen meaning representation CANNOT be ambiguous. •
Vagueness: Vagueness can make it difficult to determine meaning
representation, but it does not cause multiple representations. – I want to
eat Turkish food.
– Here Turkish food is vague, but it does not cause multiple
representations.
– Meaning representations should be able to maintain a certain level of
vagueness.
Canonical Form
Distinct inputs can map to the same meaning representation.
– Does Kirac have vegetarian food?
– Do they have vegetarian food at Kirac? – Are vegetarian dishes served
at Kirac?
• We shouldn’t map these sentences to different meaning representations.
• Canonical Form -- The notion that inputs that mean same thing should
have the same meaning representation.
• To able to map distinct inputs to the same meaning representation, we
should able to know that different phrases mean the same thing such as
vegetarian food and vegetarian dishes.
Inference and Variables
Inference -- Ability to draw valid conclusions based on the meaning
representations of inputs and the background knowledge.
• We should be able to find the truth value of propositions that are not
explicitly in KB - - inference.
• Example:
– I would like to find a restaurant that serves vegetarian food.
– This example is complex and we should use variables in its
representation.
– serves(x, VegetarianFood) -
- a part of our meaning representation
– If there is a restaurant serves vegetarian food, our inference mechanism
should be able to find it by binding the variable x to that restaurant.
Expressiveness
Expressiveness -
- Ability to express wide range of subject matter.
• The ideal situation: a single meaning representation language that could
adequately represent the meaning of any sensible natural language
utterance.
• Although this ideal situation may not be possible, but the first order
predicate calculus (FOPC) is expressive enough to handle a lot of things.
• In fact, it is claimed that anything can be representable with other three
representation language, it can be also representable with FOPC.
• We will concentrate on FOPC, but other representation languages are
also used.
– For example, Text Meaning Representation (TMR) used in the machine
translation system of NMSU is a frame based representation.
Predicate-Argument Structure
All natural languages have a form of predicate-argument arrangement at
the core of their semantic structure.
• Specific relations hold among the constituent words and phrases of the
sentence. (predicate and its arguments)
• Our meaning representation should support the predicate-argument
structure induced by the language.
• In fact, there is a relation between syntactic frames and semantic
frames.
We will try to find these relations between syntactic frames and semantic
frames.
• Example: – Want(somebody,something) -- Want is predicate with two
arguments
Syntactic Structures:
– I want Turkish food. NP want NP
– I want to spend less than five dollars. NP want InfVP
– I want it to be close by here. NP want NP InfVP
• Verb sub-categorization rules allow the linking of the arguments of
syntactic structures with the semantic roles of these arguments in the
semantic representation of that sentence.
– The study of semantic roles associated with verbs is known as thematic
role.
• In syntactic structures, there are restrictions on the categories of their
arguments.
• Similarly, there are also semantic restrictions on the arguments of the
predicates.
• The selectional restrictions specify semantic restrictions on the
arguments of verbs
Other objects (other than verbs) in natural languages may have predicate-
argument structure.
A Turkish restaurant under fifteen dollars. Under(TurkishRestaurant,$15)
• meaning representation is associated with the preposition under.
• The preposition under can be characterized by a two-argument predicate.
Make a reservation for this evening for a table for two persons at 8.
Reservation(Hearer,Today,8PM,2)
• meaning representation is associated with the noun reservation (not
with make).
• Our meaning representation should support :
– variable arity predicate-argument structures
– the semantic labeling of arguments to predicates
– semantic constraints on the fillers of argument roles.
First Order Predicate Calculus (FOPC)
First Order Predicate Calculus (FOPC) is a flexible, well-understood, and
computationally tractable approach.
• So, FOPC satisfies the most of the things that we expect from a meaning
representation language.
• FOPC provides a sound computational basis for verifiability, inference,
and expressiveness requirements.
• The most attractive feature of FOPC is that it makes very few specific
commitments for how things should be represented.
Inference
Ability to determine the truth value of a formula not explicitly contained
in a KB.
• We should have inference rules to infer new formulas from formulas
available in a KB.
For example, modes ponens is a inference rule.
We may use forward chaining or backward chaining in the
implementations of inference rules.
• Implementation of certain inference rules for FOPC is not
computationally effective.
• Resolution is a computationally effective inference rule.
– Prolog uses resolution and backward chaining. • Inference rules must be
sound and complete.
– Sound -- If a formula is derivable using inference rules, it must be valid
– Complete -- If a formula is valid, it must be derivable.
Inference -- Prolog Example
