surface realization

Surface Realization

Mahalingam.P.R

Semester III, M.Tech CSESIS, RSET

Agenda Introduction

Systemic Grammar

Interpersonal meta-function

Ideational meta-function

Textual meta-function

Functional Unification Grammar

Functional Description

Conclusion

2 Surface Realization

Introduction


Discourse Plan

The discourse plan is

generated by the

DISCOURE PLANNER

by taking into

consideration the

communicative goal and

the available Knowledge

Base.

The content is structured

appropriately.

Discourse plan defines:

•Choices made for the

entire communication

(may span multiple

sentences)

•Annotations (hypertext,

figures, etc.)

Surface realizer receives the fully

specified discourse plan.

Generates individual sentences

Constrained by the lexical and

grammatical resources

Resources

Define the realizer’s potential range of

output

If the plan specifies multiple-

sentence output, the surface

realizer is called multiple times.


So, the surface realization component produces

ordered sequence of words as constrained by the

lexicon and grammar.

Input

Sentence-sized chunks of the discourse specification

Influential approaches for surface realization

Systemic Grammar



No general consensus as to the level at which the

input to the surface realizer should be specified.

Some approaches specify only the propositional

content.


What does it do?

Surface Realization7

Derive a human readable sentence from a discourse

plan.

Discourse plan does not give syntax, only functional

information. The Surface Realizer adds syntactical

information and assures that the sentence will

comply with lexical and grammatical constraints.

What doesn’t it do?


Will not verify that the correctness of the data

provided by the discourse planner or that the

information makes sense.

Does not deal with more than one sentence at a

time. If the plan calls for many sentences, the

surface realizer will be called once for each sentence

required.

Simple Surface Realization Tools


Canned Text Systems- Takes a given input and matches it directly to a pre-made sentence.

- Commonly used in simple systems such as error messages or warnings.

- Has no flexibility whatsoever.

Template Systems- The idea of a template is that there are premade sentences with fill in the blank words that are filled in by the input.

- These systems work well with Form Letters and Slightly more advanced Error or Warning Messages.

- They are still very inflexible, but better than canned text systems.


The simple surface realization tools eventually gave

way to advanced Feature-based systems

Systemic Grammar Representation of sentences as collections of functions. Rules allow mapping from functions to grammatical forms. (Halliday, 1985)

Functional Unification Grammar Represents sentences as feature structures that can be combined and altered to produce sentences. (Kay, 1979)

“The system will save the document”

The discourse plan

would specify a saving

action done by a system

entity to a document

entity.

Other approaches

Include the specification

of the grammatical form

In this case, a future

tense assertion

Specification of lexical

items

In this case, save,

system and document



The two approaches take input at different levels.

Common factor

Input is functionally specified, rather than syntactically

specified

Factor typical of generational systems

Generation systems start with meaning and context

Specify the intended output in terms of function, rather

than form.


Can be stated in two ways

ACTIVE FORM

PASSIE FORM

Discourse planners tend not to work with the

syntactic terms.

They are most likely to keep track of the focus or

local topic of the discourse.

More natural to define this distinction in terms of focus.


Surface

Realization

Approaches

Systemic Grammar

Functional Unification

Grammar


If the document is the local topic of

discourse, it would be marked as

the focus which could trigger the

use of the passive.

“The document will be saved by

the system”

Both surface realization

approaches categorize grammar in

functional terms.

Systemic Grammar


Systemic-

Functional

linguistics

A branch of linguistics

that views language as a

resource for expressing

meaning in context

-An Introduction to

Functional Grammar,

Halliday (1985)


A part of Systemic-Functional

linguistics.

Represent sentences as collections

of functions and maintain rules for

mapping these functions on to

explicit grammatical forms.

Well suited for generation

Widely influential in NLG


Systemic sentence analysis organize the functions

being expressed in multiple layers.


Layers

“The system will save the

document”

Concepts of theme and

rheme were developed by

the Prague school of

linguistics

-Firbas, 1966

Thematic roles apply here

too, like AGENT,

EXPERIENCER,

INSTRUMENT, and so on.


Mood layer –simple declarative

structure

Subject

Finite (auxiliary)

Predicator (verb)

Object

Transitivity layer

Actor / Doer (system)

Process (saving)

Goal – the object being acted upon (document)

Theme layer

Theme

Rheme

Rheme

a topic of informal discussion

different from a theme


The three layers deal with different sets of functions.

Meta-functions

• Inter-personal meta function

Mood layer

• Ideational meta function

Transitivity layer

• Textual meta function

Theme layer

Interpersonal meta-function


Group the functions that establish and maintain the interaction between the sentence writer and the reader.

Represented by the mood layer Determines whether the writer is

Commanding

Telling

Asking

Examples would be whether the writer is telling the reader something or is asking a question.

Ideational meta-function


Concerned with the propositional content of the expression.

Transitivity layer determines Nature of process being expressed

Variety of case roles that must be expressed

Covers much of the semantics.

In other words, identify items like who the actors are, what the goals are for the sentence, and type of process being performed.

Textual meta-function


Concerned with the way the expression fits into the

current discourse.

Includes issues of thematization and reference.

Tries to fit the expression with a given theme and

reference.

Represented by the theme layer

Explicitly marks the system as the theme of the sentence


Explicit concern for interpersonal and textual issues

as well as traditional semantics

Feature of systemic linguistics that is attractive for NLG.

Many choices that generation systems make depend

on context of communication

Formalized by the interpersonal and textual meta-

functions.


System network

Grammar represented using a directed, acyclic and/or

graph, called a system network


Curly braces

AND parallel systems

Vertical lines

OR disjoint systems


Every clause (represented as the highest level

feature) will simultaneously have a different set of

features for mood, transitivity and theme.


Indicative, declarative clause expressing an active

material process with an unmarked theme.

Realization Statements


A systemic grammar uses realization statements to

map from the features specified in the grammar (like

Indicative, Declarative) to syntactic form.

Each feature in the network can have a set of

realization statements specifying constraints on the

final form of the expression.

Shows as italicized statements below each feature

Realization statements allow the grammar to

constrain the structure of the expression as the

system network is traversed.

Some simple operators


+X

Insert the function X

The grammar here

specifies that all clauses

will have a predicator.



X=Y

Conflate the functions X and Y. This allows the grammar to build a layered function structure by assigning different functions to the same portion of the expression. Active clauses conflate

the actor with the subject

Passive clauses conflate the goal with the subject



X>Y

Order function X

somewhere before

function Y.

Indicative sentences

place the subject

somewhere before the

predicator.



X:A

Classify the function X with the lexical or grammatical feature A.

Signal a recursive pass through the grammar at a lower level.

Grammar would include other networks similar to the clause network that would apply to phrases, lexical items and morphology. Indicative feature inserts a

subject function that must be a noun phrase.

Phrase further specified by another pass through the grammar.



X!L

Assign function X the

lexical item L.

Finite element of the

passive is assigned the

lexical item “be”

Procedure for generation

-Given a fully specified system network


1. Traverse the network from left to right, choosing

the appropriate features and collecting the

associated realization statements.

2. Build an intermediate expression that reconciles

the constraints set by the realization statements

collected during the traversal.

3. Recurse back through the grammar at a lower level

for any function that is not fully specified.


We can use the following specification as input.

(

:process save-1

:actor system-1

:goal document-1

:speechact assertion

:tense future

)



save-1 knowledge base instance is identified as the process of the intended expression. Assume all knowledge base

objects to be KLONE-styled instances

Actor and goal similarly specified as system-1 and document-1respectively.

Input also specifies that the expression be in the form of an assertion in the future tense.

Generation Process


Start at clause feature

Insert a predicator

+predicator

Classify predicator as a verb

predicator:verb

Proceed to mood system

Correct option for a system chosen by a simple query or decision network associated with that system

Decision based on the relevant information from input specification and from Knowledge Base.


Mood system chooses the indicative and declarative features Input specifies assertion.

Realization statements associated with the indicative and declarative features will insert subject and finite functions order them as subject, then

finite and then predicator.

+subject

subject > predicator

+finite

finite > predicator

subject > finite


The resulting function structure is as follows:


Assume save-1 is marked

as a material process in

the knowledge base.

Transitivity function

chooses the material

process feature

Insert goal and process

functions

Conflates the process with

the finite/predicator pair

+goal

+process

process= finite,predicator


Since there is no indication in either the input or knowledge base to use a passive, the system chooses the active feature, which

Inserts the actor and conflates it with the subject

+actor

actor=subject

Inserts the object, conflating it with the goal and ordering it after the predicator

+object

object=goal

predicator>object


This results in the following functional structure.


There is no thematic

specification in the input

Thematic network chooses

unmarked theme

Inserts theme and rheme

Conflate theme with subject

Conflate rheme with

finite/predicate/object group

+theme +rheme

theme=subject

rheme=predicator,object


This results in the full function structure as:


The generation process recursively enters the grammar a number of times at lower levels to fully specify the phrases, lexical items, and morphology.

This is due to the presence of the following statements

When the network found that it is an indicative statement

finite : auxiliary

subject : noun phrase

When active voice was identified

object : noun phrase


Noun phrase network

Create the lexical items The system and the document

Auxiliary network systems

Create the lexical item will

The choice of lexical items system, document and

save can be handled in a number of ways, most

typically by retrieving the lexical item associated with

the relevant knowledge base instances.

The noun phrase and auxiliary network systems

work similar to the clause network we have seen till

now.


Functional Unification Grammar uses unification to

manipulate and reason about feature structures.

With a few manipulations, the same technique can be

applied to NLG.

Basic Idea

Build the generation grammar as a feature structure with

lists of potential alternations

Then unify this grammar with an input specification built

using the same sort of feature structure.


Unification process

takes the features specified in the input

reconciles them with those in the grammar

produces a full feature structure which can then be

linearized to form sentence output.



A simple functional

unification grammar.

Expressed as an

attribute-value matrix

Supports simple

transitive sentences in

present or future tense

Enforces subject-verb

agreement on number


At highest level, the grammar provides alternatives for sentences, noun phrases and verb phrases CAT S

CAT NP

CAT VP

Alternation feature provided by the ALT feature on the left. Curly braces indicate that any of the

enclosed alternatives may be chosen and followed

This level also specifies a pattern indicating the order of the features specified at this level Actor

Process

Goal


At sentence level, grammar supports the following features. Actor NP

Process VP

Goal NP

Subject-verb agreement Enforced using the number

feature inside the processfeature.

Number of processes must unify with the path {actor number}

Path list of features specifying a path from the root to a particular feature.

Here, number of process must unify with the number of actor.


While the path is given

explicitly, we can also

have relative paths

Like the number feature

of the head feature of the

NP.

The path here,

{↑↑number }, indicates

that the number of the

head of the NP must

unify with the number of

the feature 2 levels up.

Use of {↑↑number}


VP level is similar to the NP level except that it has its own alternation between future and present tense.

Tense is specified in the input feature structure.

Unification will select the alternation that matches and then proceed to unify associated values.

If tense is present For example, the head will

be single verb.

If tense is future Insert modal auxiliary “will”

before the head verb.


This grammar is similar to the systemic grammar in

the point that it supports multiple levels, that are

entered recursively during the generation process.

The details of the particular sentence we want to

generate is given in an input feature structure.

Functional Description (FD)


The input feature structure.

It defines the input specifications for the particular

sentence we want to generate.

It is a feature structure just like the grammar.


Here, we see a sentence specification with a particular action the system

a particular goal the document

Process saving of the document by the system in the future

The input structure specifies the particular verbs and nouns to be used as well as the tense Different from input to systemic grammar

In systemic grammar, lexical items retrieved from knowledge base entries associated with actor and goal.

Tense, not included in systemic grammar, is computed by a decision network that determines relative points in time relevant to the content of the expression.


Since tense is also to be included in the input feature

structure (Functional Description), more decisions

have to be made by the discourse planning

component.

To produce the output, the input is unified with the

grammar.

May require multiple passes through the grammar.


The preliminary unification unifies the input FD with

the S level in the grammar

First alternative at the top level

This results in the structure:


The features specified in the input structure have

been unified and merged with the features at the top

level of the grammar.

Features associated with actor include the lexical item

system from the input FD and category NP from the

grammar.

Process feature combines the lexical item and tense from

the input FD with the category and number features from

the grammar.


Generation mechanism

now recursively enters the

grammar for each of the

sub-constituents.

It enters the NP level

twice for actor and

goal

It enters the VP level once

for the process.

Final FD



Every constituent feature that is internally complex has a pattern specification.

Every simple constituent feature has a lexical specification

The system now uses the pattern specifications to linearize the output, producing



The example didn’t specify the actor to be plural. We

can do that by adding the feature-value pair

number plural

to the actor structure in the input FD.

Subject-verb agreement would then be enforced by

the unification process.

Grammar requires that the number of heads of NP

and VP match with the number of the actor that

was specified in the input FD.

Conclusion



The two surface generation grammars illustrate the

nature of computational grammars for generation.

Both used functional categorizations.

Bidirectional grammar

Single grammar for both generation and understanding

Currently under investigation

Haven’t found widespread use in NLG

Additional semantic and contextual information required as input

to the generator

Sample NLG programs

KPML FUF/SURGE


A text generation

system based off of the

earlier Penman system.

Uses Systemic-

Functional Linguistics

Principles. http://www.fb10.uni-

bremen.de/anglistik/langpro/kpml/REA

DME.html

A text generation system and English Grammar using Functional Unification.

FUF – Functional Unification Formalism is an implementation of Functional Unification Grammar developed by Elhadad (1992,1993)

http://www.cs.bgu.ac.il/research/projects/surge/index.htm

http://www.fb10.uni-bremen.de/anglistik/langpro/kpml/README.html






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