From this representation, a new text is generated, using a text modei and action ruies.. 2 TEXT REPRESENTATION A text conveys information by various means.. This representation includes
Trang 1E F I M B E L , H G R O S C O T , J M L A N C E L , N S I M O N I N
C A P S O G E T I I N N O V A T ION
129, r u e de l ' U n i v e r s i t ~
7 5 0 0 7 P A R I S
F R A N C E
ABSTRACT
The following paper concerns a general scheme
for multilingual text generation, as opposed to
just translation Our system processes the text
as a w h o l e , f r o m w h i c h it e x t r a c t s a
representation of the meaning of the text From
this representation, a new text is generated,
using a text modei and action ruies
This process is done in six steps : word
analysis, sentence analysis using a Functional
Grammar, reference s o i v i n g and i n f e r e n c e ,
c o n s t r u c t i o n of the text pattern, sentence
generation, and word generation D i f f e r e n t
kinds of information are used st each step of
the p r o c e s s : t e x t o r g a n i z a t i o n , s y n t a x ,
semantic, etc
A l l the knowledge, as w e l l as the text, is
given in a d e c l a r a t i v e manner It is expressed
in a s i n g l e f o r m a l i s m n a m e d F u n c t i o n a l
Oescriptions It consists of lexical data, a
Functional Grammar, a knowledge network, action
r u l e s for r e f e r e n c e s o l v i n g and s e n t e n c e
g e n e r a t i o n , m o d e l s of t e x t , r u l e s o f
structuration, and sentence schema
Text representation, included in the semantic
network, is composed of different kinds of
o b j e c t s (not n e c e s s a r i l y distinct) : text
organization, syntactical information, objects
introduced by the discourse, a f f i r m a t i o n s on
t h e s e o b j e c t s , and l i n k s b e t w e e n t h e s e
affirmations
I TEXT VERSUS SENTENCES
the one hand rearrange a whole sentence in order
to respect certain s t y l i s t i c rules and on the other hand modify the order of these sentences Some of them may be d e l e t e d and others added
In any ease, in order to be a b l e to g e n e r a t e
a coherent text by means of the i n f o r m a t i o n extracted from a source text, it seems most important to us to understand what the text is refering to
We h a v e restricted our scope to economic geography texts, taken from a French r e v i e w named ATLASECO These texts deaI with i n d u s t r y and agriculture, i n v o i v i n g reiated c o n c e p t s ( g r o w t h , d e c i i n e , p r o d u c t i o n , e c o n o m i c a I balance ) Our system parses a french text, integrates its i n f o r m a t i v e content into a
k n o w l e d g e base, then generates a new text from information extracted from this base
W e p r e s e n t in p a r t (2) t h e t e x t
r e p r e s e n t a t i o n used throughout the process We
d e s c r i b e in p a r t (3) t h e k n o w l e d g e
r e p r e s e n t a t i o n , b a s e d on the f u n c t i o n a l
d e s c r i p t i o n s In part (4), we briefly describe
the different steps of the process
2 TEXT REPRESENTATION
A text conveys information by various means For example, the order of sentences, as well as their syntactic structure, may be significant The general text representation we use is the same for u n d e r s t a n d i n g and structuring This representation includes the different kinds of information needed for these two processes
Human translation is a complex process whose
a c t i v i t i e s are not yet e n t i r e l y understood
Generation of text differs from generation of
s i n g l e s e n t e n c e s : our w o r k s t r e s s e s the
necessary processing of the text as a whole and
restructuring of this text A translator must on
The text is r e p r e s e n t e d by a set of interrelated objects (sentences, words, semantic objects ) There are five c l a s s e s of objects, not necessarily distinct : the visible objects, the syntactic components, the discursive objects (defined later), the affirmations and the links
226
Trang 22.1 Visible Objects
V i s i b l e objects are chapters, paragraphs,
sentences, words and word sequences They are
reiated by positionai iinks, describing the
organization of the text, as shown by the
foIiowing figure :
TEXT
SENTENCE 1 " r='~"J 5ENTEN~En / S
/
OB3 1 083 2 083 n OB3 1 083 n
fig i
T h e s e l i n k s r e f l e c t the h i e r a r c h i c a l
organization of the text (chapters .) and the
dependent relationship of statements (order of
sentences .)
2.2 Syntactic Components
The sentences are represented by means of
u n o r d e r e d " s y n t a c t i c c a s e s " ( s u b j e c t ,
determiner ), linking a component with its
subcomponents For example :
"the big mouse" ~ ~ ~ ~
body determiner quail fier
fig Z
2.3 D i s c u r s i v e O b j e c t s
Discursive objects represent the semantic
objects o r entities introduced by the text For
example :
"The main crop i s wheat, whose p r o d u c t i o n has
s t r o n g l y d e c r e a s e d T h i s d e c l i n e has s t r o n g l y
a f f e c t e d the commercial balance."
These two constructions correspond to a
single discursive object, which could be
c a l i e d : "decrease of the production of wheat"
The meaning of a sentence is represented by a
network of discursive objects The propositions,
r e p r e s e n L i n g the d i s c u r s i v e o b j e c t s , are
normalized into an objscLive form For example :
'Peter met 3ohn" -> "meeting of 3ohn by Peter"
"Mary is ill" -> "illness of Mary"
This normalization is very usefui for co- reference soiving
The text i t s e l f may be a discursive object Hence, it is represented by an object which mentions topic, date and positional links
b e t w e e n s e n t e n c e s of text O u r i n g t h e Understanding Process, this object will be enlarged by new information, or modified (for instance topic may become more explicit)
2.4 Affirmations
Implicit within the text, as well as s i m p l y introducing objects, is the e v a l u a t i o n of certain objects "The production is increasing" introduces "the production" and a f f i r m s "the increase of production"
There are various statement values ("stated
p o s i t i v e l y in discourse", "dubious" ) These values may be given by the author, a figure, an economical organization, etc
2.5 Links
The links are m a i n l y operators (and, or, because ) that correlate the affirmations, which are die c u r s i v e objects or links : "A
b e c a u s e B" ; "A and B, b u t C"
They reflect internal structure of discourse
For example : "the production increases but the deficit remains important"
During Understanding phase, Lheir main role
is to transmit statement values to the objects they link
The structuring stage isolates argumentative effects of the discourse, and builds new links (possibly the same) The choice of these links
is important because they have to be coherently acceptable from the reader point of view
3 KNOM_EDGE REPRESENTATION
We use the same r e p r e s e n t a t i o n f o r a l l types
o f k n o w l e d g e i n t h e s y s t e m : l e x i c a l definitions, grammar rules, semantic rules,etc They are given separately in the formalism of functional descriptions and integrated into the
k n o w l e d g e base that is r e p r e s e n t e d by a functional descriptions network
Trang 3from the functional grammars (DIK 78), which is
a linguistic formalism that can be reiated to
case grammars (FILLMORE 68) Historically, Kay
( 8 1 ) used functional descriptions as a genecel
tool to represent grammars, independently of any
s p e c i f i c l i n g u i s t i c t h e o r y , a f t e r w h i c h
Rousselot (84) then used functional d e s c r i p t i o n s
to represent any kind of knowledge (grammar,
semantic rules, scripts ) in a system of story
understanding
We will use "DF" instead of "functional
description"
A DF may represent any kind of knowledge For
example , "William cut himsel~' may be described
by the following figure :
[action = cut
tense = past
actor = [name = Wiiliam]
obj_act <I actor>]
fig )
A OF is an unorde~ed set of identifier-object
pairs The identifiers are not pre-defined :
they may be added or removed in any way This
allows us to put various kinds of information in
the same description : syntactic, semantic, etc
The objects may be other DFB or p a t h s
A path is a notation whose aim is to point
to objects that ace already defined We h a v e
two kinds of paths :
- A local path <I list-of-identifier> points to
an object in the current OF, as defined by the
list of identifiers For example, in fig 3,
the path <I actor name> cefers to "William"
- A g l o b a l path, <g starting-point list-of-
i d e n t i f i e r s > p o i n t s to an o b j e c t in the
knowledge base It is defined in the same way
as the l o c a l path, except that the s t a r t i n g
point i s the curcent DF
The internal representation of a DF is a
labelled graph For example, the representation
of the DF in fig ) is shown in fig 4
as input and converts it d i r e c t l y into the labelled graph as shown in fig 4, which is then integrated into the knowiedge base
A functional description network is the graph that represents a set of i n t e r l a c e d DFB, i.e the knowledge base This network is a labelled graph whose nodes correspond to OFs, and whose links to elementary object properties These links are labelled with identifiers
colour
object ( D F ) property object (OF)
f i g 5
Each identifier (e.g colour) may be given
d i f f e r e n t p r o p e c t i e s (e.g i n h e c i t a n c e properties) This is done through attaching a node to the identifier
The graph may contain Dynamic Paths , which allow access to the objects A dynamic path is a function <f starting-point list-of-identifiers> whose value is a node on the graph Foc example,
in fig 4, the dynamic path <f (M) actor name> points to the node "William" Dynamic Paths ace very similar to substituable variables in formal systems
The processing of a DF network can be done at two levels :
(i.e propecties) or nodes (i.e objects) ;
- f o r m l e v e l : each node of the netwock can still be viewed as a description, which corresponds to a complex set of links This
a l l o w s us to w o r k on the f o r m of the objects (pattern matching and merging)
The main algorithms that operate at the form
l e v e l are those of c o m p a t i b i l i t y (pattern matching) and m e r g i n g "Compatilibity" is a
b o o l e a n f u n c t i o n that decides whether two descriptions may correspond to the same object
of the real world For example, could the events described by "William speaks to himselP' also be described by "Somebody speaks to Ted" 7 :
Fig 4
The system takes the DF, as shown in fig 3,
~action = speak actor ~name = William]
o b j - a c t = < I a c t o r > J
[action = speak actor = ~ is-a = huma~
obJ-act = [ name Ted]J
fig 6
228
Trang 4These two descriptions are n o t compatible,
because the name of ,he "obj-act" is "William"
on one hand and "Ted" on the other
"William cut himself "
action = cut
tense = past
o b j - a c t <1 a c t o r > ]
William]
"X cut Y"
[ a c t i o n = cut
tense = past
a c t o r = [ i s - a = human]
obj-act Estate changed]J
[ a c t i o n = cut
tense = past
actor = [ is-a = human
name = William state = changed]
o b j - a c t = <I actor>]
fig 7
Two c o m p a t i b l e descriptions can be merged
The result is a new description, more c o m p l e t e
than either of the originals For example, the
first two DFs of fig 7 are compatible, and give
the third one as the result of merging
T h e s e two a l g o r i t h m s , i n s p i r e d by the
functional unification introduced by Kay, are
very powerfuI and are used at each step of text
p r o c e s s i n g T h e i r p r e c i s e d e f i n i t i o n s use
mathematicaI transformations of iabeIIed graphs
and are given in (GROSCOT, ROUSSELOT 85)
/4 TEXT PROCESSING
In this section, we describe the f o l l o w i n g
different steps of the text processing :
- word anaiysis
- sentence anaIysis
- reference solving
- construction of the text pattern
- sentence generation
- word generation
/4.1 Word Analysis
The word analyser uses a knowledge base about
the standard inflexions of the initial language
and a dictionary of words and sequences of words Each item in the dictionary contains a semantic definition, its syntactical category, and its type of infiexion with its roots
We generate for each word a d e s c r i p t i o n of its syntactic features A I I s o i u t i o n s are generated For exampie, the analysis of the word
"burns" gives at least these two DFs :
i ) [word = burn number = p l u r a l category = substantive
" ' " ]
2) [word = b u r n
category = verb tense = present person = 5 number = s i n g u l a r
o , , ]
fig 8
/4.2 S e n t e n c e A n a l y s i s -
The a n a l y s e r is b a s e d on the w o r k of
R o u s s e l o t (8/4) It works sentence by sentence and assumes three important functions :
- r e c o g n i t i o n and construction of syntactic components of a sentence
- c o n t r o I of semantic constraints
- generation of the semantic representation of the sentence, i.e an affirmation or a link The a n a l y s e r uses a d e c l a r a t i v e grammar, which does not depend upon the way in which the
a n a i y s e r works The grammar is a DF, in which each identifier-object pair represents a grammar ruIe These rules aliow the analyser to s p i i t a syntactical category into constituents, to
v e r i f y the c o n s t r a i n t s , and to b u i l d the associated semantic representation
The analyser works in a top-down manner by means of an agenda which allows the separation
of the controls Also included in the anaIyser
is a graph that contains the partiaI a n a i y s i s which minimizes processing time during the backtracks
The starting point of this a n a l y s e r is the set of DFs which have been obtained and graphed after word analysis
We show here, in an example, the result of the analysis of the sentence "Agriculture is a success" :
o b j e c t : success
e v a l u a t e d - o b j e c t : [ o b j e c t = a g r i c u l t u r e
def-undef = d e f i n i t e ] statement-value = t r u e ]
fig 9
Trang 54 3 R e f e r e n c e Solving
This stage determines what the pronouns and
noun phrases point t o : known objects (already
in the k n o w i e d g e base), or new o b j e c t s
introduced (directly or indirectly) by the text
The process uses action rules, written by
means of OFs The rules interpreter works in a
"lazy" saturation mode : It l o c a l l y saturates
the knowledge base regardless of whether or not
all inferences have been made
The process has three main features :
- i t is d i r e c t e d by the s y n t a x ( d e f i n i t e
articles, pronouns )
- i t identifies the objects at the semantic
level, by testing the c o m p a t i b i l i t y of
discursive objects
- i t uses p o s i t i o n a l l i n k s to d e f i n e the
possible references in each case For example,
a demonstrative pronoun has to be found among
the preceding objects of the t e x t
It h a s two important effects :
- it constructs a coherent network of discursive
objects (the "meaning" of the text)
- it integrates the text into the k n o w l e d g e
base : each time that the text refers to a
known object, links are created from this
object to the text
W h e n t h i s is done, the p o s i t i o n of the information, may be inadequate for a coherent text Therefore, the text structure is changed : some information is enhanced ; the paragraphs are b a l a n c e d ; the information is reordered
a c c o r d i n g to c h r o n o l o g i c a l o r d e r and significance at the deepest l e v e l of the text
s t r u c t u r e
At t h i s step, o u t p u t t e x t s t r u c t u r e is
c l e a r l y apparent But it is not sufficient because the text has now become a sequence of unrelated statements Links are then created between the discursive objects introduced in the text (conjunctions, pronouns ) and e l l i p s e s are used to avoid repetitions
4.5 Sentence G e n e r a t i o n
From each OF of the text describing output
i n f o r m a t i o n , t h i s s t a g e g e n e r a t e s t h e description of a suitable sentence
S y n t a c t i c p a t t e r n s are a s s o c i a t e d w i t h
c o n c e p t s ( e v o l u t i o n s , a p p r e c i a t i o n s , numbers ) Action ruIes combine these patterns
to create the description of a sentence, i.e a iist of word descriptions
4 4 C o n s t r u c t i o n o f the t e x t p a t t e r n
Once the initial text has been analysed, its
i n f o r m a t i v e c o n t e n t is i n t e g r a t e d in the
knowledge base, thereby making it p o s s i b l e to
question this base and to reformulate the so-
obtained information with the aim of generating
a new t e x t
This s t a g e b u i l d s the text s t r u c t u r e
(paragraphs, sub-paragraphs, sentences ) using
two c o m p l e m e n t a r y approaches : the content of
information and the e v o I u t i o n of the v i s i b l e
is a refIective process : generation of a singIe
sentence of the text must take account the
preceding and following text All the processes
used for structuring are written by means of
rules
The starting point of structuring is a set of
i n f o r m a t i o n e x t r a c t e d from the k n o w l e d g e
network
First, all the information is inserted into a
text model by means of their topics Some
obvious redundancies are eliminated : only one
occurrence will be kept, in the most appropriate
place
A 6 Word G e n e r a t i o n
Word generation uses the same organization of the knowledge base (about standard inflexions)
as word anaiysis
From a list of word descriptions, inciuding syntactic features, the system constructs the output word For example :
word = burn
cat = verb tense = present person = ) > B U R N S
number = singula d
fig i0
.5 ANALYSING ECONOMIC GEOGRAPHY TEXTS
The texts we have analysed, taken from the
F r e n c h r e v i e w A T L A S E C O , describe the main
f e a t u r e s of the a g r i c u l t u r e in d i f f e r e n t
c o u n t r i e s They c o n t a i n an a v e r a g e of 20 sentences, which contain from i0 to 45 words
230
Trang 6The system used :
- 400 lexical items, which represent 2400 words
- 70 grammar ruies
- a semantic network containing 220 concepts, 80
of them being domain-dependant
- 80 semantic ruIes for inference and reference
solving
- i text model, containing 50 paragraphs and
subparagraphs
- lO0 rules for text structuring
- 30 sentence patterns for generation
O u r s y s t e m w a s a b l e to e x t r a c t t h e
s i g n i f i c a n t i n f o r m a t i o n that s u c h a text
conveys, and produce a new text from this
information As a matter of convenience, we used
French as the target language for validation,
however the process described here should also
be able to work in a manner independant of the
target language, by chaining certain parts of
the knowledge base
Now, we are in the process of adapting our
system to generate French appliance operation
manuals from the corresponding ones
ATLASECO Atlas dconomique mondial
Le Nouvel Observateur, 1982
DIK Simon
"Functional Grammar"
Publications in Language Science
Foris Publications, Oordrecht Holland, 1978
"The c a s e for case" in " U n i v e r s a l s in Linguistics Theory", E Bach and R Harms (ede), p.i-90, 1968
FIMBEL Eric
"Les reseaux miroirs : un mdcanisme d'infd- fence gdndraI ; a p p i i c a t i o n h u n syst~me d'aesimiiation de textes"
Th~se de l'universitd Paris 6, mars 1985
GRIZE Jean-Blaise
"Introduction h la logique n a t u r e l l e et approche logique du dialogue"
Approches formelles de la sdmantique naturelle Publication CNRS-UPS-UTM-ADI, 1982
GROSCOT Herbert, ROUSSELOT Franqois
"Un l a n g a g e d d c l a r a t i f u n i f o r m e et un analyseur syntaxico-sdmantique"
Proceedings of Cognitiva, Paris, juin i985
GROSS Maurice
"Mdthodes en syntaxe"
Hermann, 1975
KAY Martin
"Unification Grammars"
Xerox internal publication, 1981
McKEOWN Kathleen R
"Generating natural language text in response
to questions about database structure"
PH.D of the university of PennsyIvania, 1982
ROUSSELOT Francois
"Rdalisation d'un programme comprenant des textes en utiIisant un formaiisme ddclaratif pour reprdsenter toutes les connaissancee" Th~se d' dtat, Paris 6, 1984
SIMONIN Nathalie
"Utilisation d'une expertise pour engendrer des textes structurds en fcan~ais"
Th~se de l'universit~ Paris 6, mars i985