Speech & NLP (Fall 2014): Conceptual Dependency & Linguistic Relativity

Speech & NLP

Conceptual Dependency Theory

&

Linguistic Relativity

Vladimir Kulyukin

Outline

Background

Conceptual Dependency

Linguistic Relativity

Semantic Primitives of Conceptual Dependency

Conceptual Analysis of Natural Language

Mapping NL Inputs to Conceptual Dependency

Graphs

Background

Content Analysis

Language is a medium whose primary purpose is

communication

Primary focus of linguistics is to determine what

kinds of things can be communicated

Primary emphasis is on content, not form (this is

in direct opposition to formal language theory)

Emphasis on content is common in AI; emphasis

on form is more common in computational

linguistics

Is Pro-Content Anti-Syntax?

Primary focus on content does not mean that

the focus on anti-syntax

Syntax is very important but its role should be

secondary to the study of knowledge and

meaning

There should be no independent syntactic pass

over the input: syntactic & semantic processing

should go hand in hand

Conceptual Dependency Theory

Basic Axioms of CD Theory

For any two sentences that are identical in

meaning, regardless of language, there

should be only one representation

Any information in a sentence that is

implicit must be made explicit in the

representation of the meaning of that

sentence

Basic Definitions of CD Theory

The meaning propositions underlying language are called

conceptualizations

A conceptualization can be active or stative

An active conceptualization has the form:

<ACTOR, ACTION, OBJECT, DIRECTION, INSTRUMENT>

A stative conceptualization has the form:

IS_IN(OBJECT, STATE, VALUE) – OBJECT is in STATE whose

value is equal to VALUE

Event Representation

Every EVENT has:

ACTOR

ACTION performed by ACTOR

OBJECT that ACTION is performed on

DIRECTION in which ACTION is oriented

INSTRUMENT with which ACTOR does

ACTION

ACTOR & OBJECT

ACTOR is a concrete object (aka PICTURE

PRODUCER or PP)

ACTOR can decide to apply ACTION to another PP

called OBJECT

A rock is a PP but cannot be an ACTOR because it

cannot decide to apply ACTION to any other object

Honesty, justice, truth and other mass nouns are not

PPs

ACTION represent a physical action or a mental

action

Semantic Action & State Primitives

CD constructs the meaning of NL input from a

finite set of semantic primitives

Semantic primitives can be considered as an

interlingual vocabulary in terms of which one

can, in principle, represent the meaning of

every word in every language

To date, there is no universally accepted set of

semantic action & state primitives but the

search for this set continues

Linguistic Relativity

Sapir-Whorf Hypothesis: Alternative to Universal Semantic

Primitives

Edward Sapir, 1884 - 1939 Benjamin Lee Whorf, 1897 - 1941

Linguistic Relativity

Benjamin Lee Whorf (1897 – 1941) was an American linguist

& anthropologist whose mentor was Edward Sapir (1884 –

1939), another American linguist & anthropologist

Whorf formulated the Principle of Linguistic Relativity (aka

the Sapir-Whorf Hypothesis) that states that speakers of

different languages conceptualize and experience the world

differently due to linguistic differences in grammar and

usage

Linguistic Relativity Principle states that it is impossible to

find the universal set of semantic action and state primitives

Semantic Primitives

of

Conceptual Dependency

Verb Representation in CD

A verb is represented as a particular

combination of primitive actions (acts) and

states none of which are unique to that verb

but whose combination is entirely unique

R. Schank, R. Abelson. “Scripts, Plans, Goals, & Understanding:

An Inquiry into Human Knowledge Structures”

ATRANS

ATRANS – transfer of an abstract relationship (e.g., possession,

ownership, control)

Examples:

1) GIVE is an ATRANS of something to someone else

2) TAKE is an ATRANS of something to oneself

3) BUY is an ATRANS of something to oneself and another

ATRANS of money from oneself to the owner of something

The robot gave John a cup of coffee.

The robot took a cup of coffee from the coffee machine.

John bought a new car.

PTRANS

PTRANS – transfer of the physical location of an

object

Examples:

1) GO is an PTRANS of oneself to a place

2) PUT is an PTRANS of an object to a place

The robot went to the lab.

The robot put the block on the table.

PROPEL

PROPEL – application of a physical force to an object;

this primitive applies whenever any force is applied

Examples:

PUSH, PULL, KICK, THROW have the PROPEL primitive

The robot pushed the chair to the wall.

This is an instance of PROPEL by the robot to the chair

that caused a PTRANS of the chair from its current

location to the wall.

MOVE

MOVE – the movement of a body part of an

agent/animal by that agent/animal

Examples:

KICK, HAND have the MOVE primitive

The boy kicked the ball.

This is an instance of MOVE by the boy of his foot to

the ball that causes a PTRANS of the ball from its

current location to some unknown location.

GRASP

GRASP – the grasping of an object by an actor

Examples:

HOLD, GRAB have the GRASP primitive

The robot picked up the ball from the floor.

This is an instance of GRASP by the robot of the ball to

the ball that causes a PTRANS of the ball from the

floor into the robot’s gripper. This is also an instance of

MOVE by the robot of its gripper to the ball.

INGEST

INGEST – the taking of an object by an animal/agent to the inside of that

animal agent

Examples:

EAT, DRINK, SMOKE, BREATHE have the INGEST primitive

The robot charged.

John ate an apple.

These are instances of INGEST. The first sentence is an INGEST by the

robot of electricity inside the robot’s batter. The second sentence is an

instance of INGEST by John of the apple to John’s stomach.

EXPEL

EXPEL – the expulsion of an object from the body of an animal/agent to

the outside of the body

Examples:

SWEAT, CRY have the EXPEL primitive

Mary cried.

John spat on the floor.

Both sentences are instances of EXPEL. The object of the first instance of

EXPEL is tears. The object of the second instance of EXPEL is saliva.

MTRANS

MTRANS – the transfer of mental information within one animal/agent or between/among

animals/agents.

CD Theory partitions the agent’s memory into two components: CP (conscious processor

where current mental manipulation occurs) and LTM (long-term memory where things are

stored)

Examples:

TELL, INFORM, SEE, FORGET have the MTRANS primitive

Mary told the robot how to get to the lab.

The robot told Mary which rooms it had cleaned.

Both sentences are instances of MTRANS. Mary does an MTRANS of a route from some

location to the lab. The robot does an MTRANS of the rooms it had cleaned to Mary.

MBUILD

MBUILD – the construction of an agent/animal of new

information from old information.

Examples:

DECIDE, CONCLUDE, REMEMBER have the MBUILD primitive

The robot concluded that it is lost.

John remembered that he had promised Mary to take her to the

movies.

SPEAK

SPEAK – the production of sounds by an animal/agent.

Examples:

SHOUT, PURR, BEEP have the SPEAK primitive

The robotic car beeped twice.

Mary yelled at John.

ATTEND

ATTEND – the focusing of a sense organ by an animal/agent

toward a stimulus.

Examples:

ATTEND(EAR) – LISTEN

ATTEND(EYE) – SEE

ATTEND(NOSE) – SMELL

ATTEND(SKIN) – TOUCH

The robot detected a door.

John saw an exit.

Categorization of Primitive CD ACTs

Physical ACTs: 1) PROPEL, 2) MOVE, 3)

INGEST, 4) EXPEL, 5) GRASP

ACTs that cause state changes: 1)

PTRANS, 2) ATRANS

Instrumental ACTs: 1) SPEAK, 2)

ATTEND

Mental ACTs: 1) MTRANS, 2) MBUILD

CD Representation of States

States are presented as attribute-value pairs

The values come from arbitrary ranges

constructed by the knowledge engineer

For example, an agent’s health can be

represented on a scale from -10 to +10

CD has never formulated a coherent set of

state primitives comparable to its primitive acts

and adhered to by all its proponents

Conceptual Analysis

of

Natural Language

Conceptual Analyzer

Conceptual Analysis: CD Parsing

Natural Language Input

CD Graphs (aka CDs) Inference Engine

Modified and/or New CDs

LTM

CD Representation Rules

PP ACT

Picture Producer PP can perform some act ACT

CD Representation Rules

ACT PP

Some act ACT has some PP as its object

o

CD Representation Rules

ACT

Some act ACT is directed from PP2 to PP1

PP2

PP1 D

CD Representation Rules

ACT

Some act ACT receives something from PP2 and

gives it to PP1

PP2

PP1 R

CD Representation Rules

ACT1

Some act ACT1 is accomplished (instrumented) by another

act ACT2 done by some picture producer PP2

PP2

I

ACT2

CD Graph Examples

CD Graph Example 01

The robot went to the kitchen.

CD Graph Example 01

Robot

PTRANS O

Robot

Unknown

D

Kitchen

CD Graph Example 02

The robot went from the lab to the

kitchen.

CD Graph Example 02

Robot

PTRANS O

Robot

Lab

D

Kitchen

CD Graph Example 03

JOHN ATE AN APPLE.

D

I

D

CD Graph Example 03

John INGEST Apple

Unknown Mouth

MOVE John Hand O

Unknown Mouth

O

CD Graph Example 04

The robot saw a door.

D

I

D

CD Graph Example 04

Robot MTRANS Door

Camera CP

ATTEND Robot Camera O

Unknown Door

O

CD Graph Example 04

John saw an exit.

D

I

D

CD Graph Example 05

John MTRANS Exit

Eyes CP

ATTEND John Eyes O

Unknown Exit

O

CD Graph Example 06

The robot read a street sign.

D

I

D

CD Graph Example 06

Robot MTRANS Sign

Camera CP

ATTEND Robot Camera O

Unknown Sign

O

CD Graph Example 07

John promised to give Mary a book.

D

O

D

CD Graph Example 07

John MTRANS

LTM Unknown

ATRANS John Book O

John Mary

Mapping NL Inputs to CD Graphs

Syntactic Parsing vs. Conceptual Analysis

The objective of syntactic parsing is to

construct a parse tree (or multiple

parse trees) of the input

The objective of conceptual analysis

(CA) is to construct a conceptual

dependency graph representing the

meaning of the input

Expectations

Conceptual analysis (CA) is an

expectation-driven process

Syntactic parsers (e.g., Early parser)

also use expectations

In a syntactic parser, expectations

come from a grammar

in a conceptual analyzer, expectations

come from a library of CD structures

CD Database

Suppose that we have compiled a database of PP and CD

structures

For example, our database may have CD structures like

<INGEST :ACTOR NULL

:OBJECT NULL

:FROM NULL

:TO NULL

:INSTRUMENT NULL

:TIME NULL>

<PP :CLASS APPLE :REF NULL NUMBER: NULL>

<PP :CLASS BOOK :REF NULL NUMBER: NULL>

<PP :CLASS HUMAN :NAME JOHN :GENDER MALE>

CD Database

CD structures have slots and fillers

Consider this CD:

<INGEST :ACTOR <PP :CLASS HUMAN :NAME JOHN :GENDER MALE>

:OBJECT NULL

:FROM NULL

:TO NULL

:INSTRUMENT NULL

:TIME NULL>

In this CD, the slot :ACTOR has the filler <PP :CLASS HUMAN

:NAME JOHN :GENER MALE> while the slot :OBJECT has the

filler

Insight into CA Algorithm

The conceptual analyzer has access to the CD database, the

input, and a concept list (the list models a short-term

memory)

The basic operation of the conceptual analyzer is to read the

input, retrieve an CD (or a set of CDs), and fill the retrieved

CDs on the basis of the read input, and find a CD (or a set of

CDs) on the concept list that expect the newly constructed CD

as a filler of a slot

If such a CD is found on the concept list, the newly

constructed CD fills one of its slots

If no such CD is found, the new CD is placed on the concept

list

References & Reading Suggestions

R. Schank, C. Riesbeck W. A. (1981) Inside Computer

Understanding. Lawrence Erlbaum & Associates.

R. Schank, R. Abelson. (1977). Scripts, Plans, Goals,

and Understanding: An Inquiry Into Human Knowledge

Structures (Artificial Intelligence Series). Lawrence

Erlbaum & Associates.