Logic Representation and Inference Introduction to Semantics What

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Logic, Representation and Inference Introduction to Semantics • What is semantics for? • Role

Logic, Representation and Inference Introduction to Semantics • What is semantics for? • Role of FOL • Montague Approach February 2009 Introduction to Semantics 1

Semantics • Semantics is the study of the meaning of NL expressions • Expressions

Semantics • Semantics is the study of the meaning of NL expressions • Expressions include sentences, phrases, and sentences. • What is the goal of such study? – Provide a workable definition of meaning. – Explain semantic relations between expressions. February 2009 Introduction to Semantics 2

Examples of Semantic Relations • Synonymy – John killed Mary – John caused Mary

Examples of Semantic Relations • Synonymy – John killed Mary – John caused Mary to die • Entailment – John fed his cat – John has a cat • Consistency – John is very sick – John is not feeling well – John is very healthy February 2009 Introduction to Semantics 3

Different Kinds of Meaning X means Y • Meaning as definition: – a bachelor

Different Kinds of Meaning X means Y • Meaning as definition: – a bachelor means an unmarried man • Meaning as intention: – What did John mean by waving? • Meaning as reference: "Eiffel Tower " means February 2009 Introduction to Semantics 4

Workable Definition of Meaning • Restrict the scope of semantics. • Ignore irony, metaphor

Workable Definition of Meaning • Restrict the scope of semantics. • Ignore irony, metaphor etc. • Stick to the literal interpretations of expressions rather than metaphorical ones. (My car drinks petrol). • Assume that meaning is understood in terms of something concrete. February 2009 Introduction to Semantics 5

Concrete Semantics • Procedural semantics: the meaning of a phrase or sentence is a

Concrete Semantics • Procedural semantics: the meaning of a phrase or sentence is a procedure: “Pick up a big red block” (Winograd 1972) • Object–Oriented Semantics: meaning is an instance of a class. • Truth-Conditional Semantics February 2009 Introduction to Semantics 6

Truth Conditional Semantics • Key Claim: the meaning of a sentence is identical to

Truth Conditional Semantics • Key Claim: the meaning of a sentence is identical to the conditions under which it is true. • Know the meaning of "Ġianni ate fish for tea" = know exactly how to apply it to the real world and decide whether it is true or false. • On this view, one task of semantic theory is to provide a system for identifying the truth conditions of sentences. February 2009 Introduction to Semantics 7

TCS and Semantic Relations • TCS provides a precise account of semantic relations between

TCS and Semantic Relations • TCS provides a precise account of semantic relations between sentences. • Examples: – – S 1 is synonymous with S 2. S 1 entails S 2 S 1 is consistent with S 2. S 1 is inconsistent with S 2. • Just like logic! • Which logic? February 2009 Introduction to Semantics 8

NL Semantics: Two Basic Issues • How can we automate the process of associating

NL Semantics: Two Basic Issues • How can we automate the process of associating semantic representations with expressions of natural language? • How can we use semantic representations of NL expressions to automate the process of drawing inferences? • We will focus mainly on first issue. February 2009 Introduction to Semantics 9

Associating Semantic Representations Automatically • • • Design a semantic representation language. Figure out

Associating Semantic Representations Automatically • • • Design a semantic representation language. Figure out how to compute the semantic representation of sentences Link this computation to the grammar and lexicon. February 2009 Introduction to Semantics 10

Semantic Representation Language • Logical form (LF) is the name used by logicians (Russell,

Semantic Representation Language • Logical form (LF) is the name used by logicians (Russell, Carnap etc) to talk about the representation of contextindependent meaning. • Semantic representation language has to encode the LF. • One concrete representation for logical form is first order logic (FOL) February 2009 Introduction to Semantics 11

Why is FOL a good thing? • Has a precise, model-theoretic semantics. • If

Why is FOL a good thing? • Has a precise, model-theoretic semantics. • If we can translate a NL sentence S into a sentence of FOL, then we have a precise grasp on at least part of the meaning of S. • Important inference problems have been studied for FOL. Computational solutions exist for some of them. • Hence the strategy of translating into FOL also gives us a handle on inference. February 2009 Introduction to Semantics 12

Anatomy of FOL • Symbols of different types – – – – constant symbols:

Anatomy of FOL • Symbols of different types – – – – constant symbols: variable symbols: function symbols: predicate symbols: connectives: &, v, quantifiers: , punctuation: ), (, “, ” February 2009 a, b, c x, y, z f, g, h p, q, r Introduction to Semantics 13

Anatomy of FOL • Symbols of different types – – – – constant symbols:

Anatomy of FOL • Symbols of different types – – – – constant symbols: csa 3180, nlp, mike, alan, rachel, csai variable symbols: x, y, z function symbols: lecturer. Of, subject. Of predicate symbols: studies, likes connectives: &, v, quantifiers: , punctuation: ), (, “, ” February 2009 Introduction to Semantics 14

Anatomy of FOL With these symbols we can make expressions of different types –

Anatomy of FOL With these symbols we can make expressions of different types – Expressions for referring to things • constant: • variable: • term: alan, nlp x subject(csa 3180) – Expressions for stating facts • atomic formula: study(alan, csa 3180) • complex formula: study(alan, csa 3180) & teach(mike, csa 3180) • quantified expression: x y teaches(lecturer(x), x) & studies(y, subject(x)) x y likes(x, subject. Of(y)) studies(x, y) February 2009 Introduction to Semantics 15

Logical Form of Phrases word POS Logic Representation csai proper noun individual constant csai

Logical Form of Phrases word POS Logic Representation csai proper noun individual constant csai student common noun 1 place predicate student(x) easy adjective 1 place predicate easy(x) easy interesting course adj/noun 1 place predicate easy(x) & interesting(x) & course(x) snores intrans verb 1 place predicate snore (x) studies trans. verb 2 place predicate study(x, , y) gives February 2009 ditrans verb. Introduction 3 toplace pred Semantics give(x, y, z) 16

Logical Forms of Sentences • John kicks Fido: kick(john, fido) • Every student wrote

Logical Forms of Sentences • John kicks Fido: kick(john, fido) • Every student wrote a program x y( stud(x) prog(y) & write(x, y)) y x(stud(x) prog(y) & write(x, y)) • Semantic ambiguity related to quantifier scope February 2009 Introduction to Semantics 17

Building Logical Form Frege’s Principle of Compositionality • The POC states that the LF

Building Logical Form Frege’s Principle of Compositionality • The POC states that the LF of a complex phrase can be built out of the LFs of the constituent parts. • An everyday example of compositionality is the way in which the “meaning” of arithmetic expressions is computed (2+3) * (4/2) = (5 * 2) = 10 February 2009 Introduction to Semantics 18

Compositionality for NL • The LF of the whole sentence can be computed from

Compositionality for NL • The LF of the whole sentence can be computed from the LF of the subphrases, i. e. • Given the syntactic rule X Y Z. • Suppose [Y], [Z] are the LFs of Y, and Z respectively. • Then [X] = ([Y], [Z]) where is some function for semantic combination February 2009 Introduction to Semantics 19

Claims of Richard Montague: • Each syntax rule is associated with a semantic rule

Claims of Richard Montague: • Each syntax rule is associated with a semantic rule that describes how the LF of the LHS category is composed from the LF of its subconstituents • 1: 1 correspondence between syntax and semantics (rule-to-rule hypothesis) • Functional composition proposed for combining semantic forms. • Lambda calculus proposed as the mechanism for describing functions for semantic combination. February 2009 Introduction to Semantics 20

Sentence Rule • Syntactic Rule: S NP VP • Semantic Rule: [S] = [VP]([NP])

Sentence Rule • Syntactic Rule: S NP VP • Semantic Rule: [S] = [VP]([NP]) i. e. the LF of S is obtained by "applying" the LF of VP to the LF of NP. • For this to be possible [VP] must be a function, and [NP] the argument to the function. February 2009 Introduction to Semantics 21

Parse Tree with Logical Forms S write(bertrand, principia) VP y. write(y, principia) NP bertrand

Parse Tree with Logical Forms S write(bertrand, principia) VP y. write(y, principia) NP bertrand V x. y. write(y, x) writes February 2009 Introduction to Semantics NP principia 22