Compositional Semantics CMSC 723 / LING 723 / INST 725 M ARINE C - - PowerPoint PPT Presentation

Compositional Semantics

CMSC 723 / LING 723 / INST 725 MARINE CARPUAT

marine@cs.umd.edu

• Words, bag of words
• Sequences
• Trees
• Meaning

Representing Meaning

• An important goal of NLP/AI: convert natural

language into a representation that supports semantic inferences

• Why? Many applications require semantic

understanding

• Question answering, translation, fact-checking, giving

instructions to a robot,…

• Challenge: how to bridge gap between linguistic

input to non-linguistic knowledge of the world

Representing Meaning

Challenges for mapping linguistic input to meaning

• different words/structure, same meaning

– She needed to make a quick decision in that situation. – The scenario required her to make a split-second judgment. – I saw the man. – The man was seen by me.

Representing Meaning

Challenges for mapping linguistic input to meaning

• same words, different meaning
• I walked by the bank
• … to deposit my check.
• … to take a look at the river.

– Everyone on the island speaks two languages. – Two languages are spoken by everyone on the island.

Representing Meaning

• create representations of linguistic inputs that

capture the meanings of those inputs.

• In most cases, they’re simultaneously descriptions

– of the meanings of utterances – and of some potential state of affairs in some world.

Desired Properties of Meaning Representations

• Goal: express propositions, while abstracting

away from ambiguity/vagueness of natural language

• Desired Properties

– Verifiability – No ambiguity – Expressiveness – Inference

Natural Language Inferences Examples

• All blips are foos.
• Blop is a blip.
• Blop is a foo.
• Mozart was born in

Salzburg.

• Mozart was born in

Vienna.

• No, that can’t be.

These are different cities.

We’ll cover different families of approaches

• Logical Semantics
• Shallow Representations and Lexical

Semantics

• Textual Inference

Constrasting 2 Strategies to Semantic Analysis

• Logical semantics

– Complete analysis – Create a First Order Logic representation that accounts for all the entities, roles and relations present in a sentence

• Information Extraction

– Superficial analysis – Pulls out only the entities, relations and roles that are of interest to the consuming application.

Information Extraction: Entity Recognition

PERSON ORGANIZATION

American Airlines, a unit of AMR, immediately matched the move, spokesman Tim Wagner said.

Information Extraction: Predicting Relations

PERSON ORGANIZATION Founder? Investor? Member? Employee? President?

American Airlines, a unit of AMR, immediately matched the move, spokesman Tim Wagner said.

Information Extraction Relations

ARTIFACT GENERAL AFFILIATION ORG AFFILIATION PART- WHOLE PERSON- SOCIAL PHYSICAL Located Near Business Family Lasting Personal Citizen- Resident- Ethnicity- Religion Org-Location- Origin Founder Employment Membership Ownership Student-Alum Investor User-Owner-Inventor- Manufacturer Geographical Subsidiary Sports-Affiliation

17 relations from 2008 “Relation Extraction Task” from Automated Content Extraction (ACE)

Information Extraction Relations

• UMLS: Unified Medical Language System

134 entity types, 54 relations

Injury disrupts Physiological Function Bodily Location location-of Biologic Function Anatomical Structure part-of Organism Pharmacologic Substance causes Pathological Function Pharmacologic Substance treats Pathologic Function

Building Blocks of Logical Representations of Meaning

Propositional Semantics

• Proposition symbols: P

, Q, …

• Boolean operators

– negation, conjunction, disjunction – Implication, equivalence

• Inference rules

– Can be defined using Boolean connectives P => Q

Building Blocks of Logical Representations of Meaning

Predicate Logic: extends our representation with

• Constants = elements that name entities in the

model

• Predicates = sets of objects or, equivalently,

functions from objects to truth values

• Functions = sets of pairs of objects, or eq.

functions from one object to another

Building Blocks of Logical Representations of Meaning

Predicate Logic: extends our representation further with

• Variables = let us refer to objects which are not

locally specified

• Quantifiers = used to bind variables

– Existential – Universal

A CFG specification of the syntax of First Order Logic Representations

From SLP2 Section 17.3

Representing a sentence in FOL

• Franco likes Frasca.

How can we represent the “Liking” predicate- argument template?

Predicate-Argument Structure in Natural Language

• Events, actions and relationships can be captured

with representations that consist of predicates and arguments to those predicates.

• Predicates

– Primarily Verbs, VPs, Sentences – Sometimes Nouns and NPs

• Arguments

– Primarily Nouns, Nominals, NPs, PPs – But also everything else, depends on the context

Example: representing predicate- argument structure…

• Mary gave a list to John.
• Giving(Mary, John, List)
• More precisely

– Gave conveys a three-argument predicate – The first argument is the subject – The second is the recipient, which is conveyed by the NP inside the PP – The third argument is the thing given, conveyed by the direct object

Example: representing predicate- argument structure

• Predicate-argument structures as templates

– We can think of the verb/VP providing a template like the following – The semantics of the NPs and the PPs in the sentence plug into the slots provided in the template

) , ( )^ , ( )^ , ( )^ ( , , , z e Givee y e Given x e Giver e zGiving y x e 

A CFG specification of the syntax of First Order Logic Representations

From SLP2 Section 17.3

Representing a sentence in FOL

• Franco likes Frasca.

“Liking” predicate-argument template

One More Building Block of Logical Representations of Meaning

• Lambda forms

– Take a FOL formula with variables in it that are to be bound. – Allow those variables to be bound by treating the lambda form as a function with formal arguments.

λx.P(x)

P(Franco) anco) λx.P(x)(Fr

Lambda Reductions

Logical Semantics Representations

• f Natural Language
• Building blocks

– Propositional Logic – Predicate Logic – Lambda Forms

• Given a sentence, how can we construct its

logical representation?

– One approach: compositional semantics

Compositional Analysis: use syntax to guide semantic analysis

Principle of Compositionality

• The meaning of a whole is derived from

the meanings of the parts

• What parts?

– The constituents of the syntactic parse of the input

• What could it mean for a part to have a

meaning?

Compositional Analysis: use syntax to guide semantic analysis

Augmented Rules

• We’ll accomplish this by attaching semantic

formation rules to our syntactic CFG rules

• Abstractly

– This should be read as: “the semantics we attach to A can be computed from some function applied to the semantics of A’s parts.”

)} .sem .sem,...α α ( { ...

n 1 1

f A

n

  

Example

• Easy parts…

– NP -> PropNoun – PropNoun -> Frasca – PropNoun -> Franco

• Attachments

{PropNoun.sem} {Frasca} {Franco}

Example

• S -> NP VP
• VP -> Verb NP
• Verb -> likes
• {VP

.sem(NP .sem)}

• {Verb.sem(NP

.sem)

• ???

Which approach can we use to… discover information about specific entities?

What approach can we use to… summarize text?

Which approach can we use to… query databases?

Which approach can we use to… instruct a robot?

Recap… Intro to Semantics

–Meaning representations

• motivated by semantic processing
• for specific applications

–2 approaches to semantic processing

• complete FOL representation
• vs. shallow information extraction