Who’s Next? From Sentence Completion to

Conceptually Guided Message Composition

Michael Zock, Paul Sabatier and Line Jakubiec-Jamet

(L.I.F - CNRS - UMR 6166)

Laboratoire d’Informatique Fondamentale de Marseille

Universit

e de la M

editerran

ee Facult

e des Sciences de Luminy

163, Avenue de Luminy - Case 901 13288 Marseille C

edex 9 - France

Abstract. Natural language generation is typically based on messages and goals.

We present here our views on how to help people to provide this kind of input,

i.e. how to communicate thoughts to the computer, so that it could produce the

corresponding surface-forms (sentences). The resource we are building is com-

posed of a linguistically motivated ontology, a dictionary and a graph generator,

whose respective functions are (a) guiding the user to make his choices concern-

ing the concepts/words to build the message from, (b) to provide knowledge of

how to link the chosen elements to yield a message (compositional rules), and (c)

the visual display of the output, i.e. message graph representing the user’s input.

Our starting point is Illico, a system developed for French. Yet, being designed

for sentence completion rather than message construction, it tends to drown the

user by providing too many options, a shortcoming that we try to overcome via

the mentionned ontology combined with a tool checking conceptual well formed-

ness. In order to make our goal feasable (allow users to express freely ideas of

any sort), we will start by limiting ourselves to two small domains: soccer and

textbooks designed for learning French.

1 Introduction

Illico is a generic software tool designed for analysis and synthesis of natural language.

To this end various applications have been developed, in particular: natural language

interfaces for knowledge bases, a communication aid in French and German for dis-

abled people [1], software for language rehabilitation and education of autistic children

[2], linguistic games for language learning [3], support for simultaneous composition

of sentences in Arab and French [4]. For more details about the system and its appli-

cations you may want to take a look at: http://www.lif-sud.univ-mrs.fr/

paulsab/ILLICO/illico.html. Illico provides tools and formalisms to mod-

ularly encode lexical, syntactic and semantic (conceptual and contextual) knowledge to

account at the various levels for the well formedness of natural language expressions.

Given this knowledge, Illico is able to perform any of the following tasks :

– analyze expressions (words, phrases, clauses, sentences, etc.) in terms of well-

formedness and display the different representations associated with each one of

them at the various levels (lexical, syntactic and semantic);

Zock M., Sabatier P. and Jakubiec-Jamet L. (2007).

Who’s Next? From Sentence Completion to Conceptually Guided Message Composition.

In Proceedings of the 4th International Workshop on Natural Language Processing and Cognitive Science, pages 38-46

DOI: 10.5220/0002431700380046

 SciTePress

– detect during analysis lexically, syntactically or semantically unexpected words, to

synthesize the expected ones in inﬂected or lemmatized form;

– guide the composition of expressions in case of error or upon the user’s request;

– avoid dead ends during the process of synthesis and guided composition. By dead

end, we mean that the system comes to a halt, without being able to propose any

candidate to continue or end the expression built so far.

Illico’s engine is based on a mechanism of coroutine processing. Both for analysis and

synthesis, it checks and executes the different constraints (lexical, syntactic and seman-

tic) as soon as the structures of the different representations on which they apply are

built, the process being dynamic and taking place in parallel. Representations are pro-

cessed non deterministically in a top-down manner. This allows the system to analyse

and to synthesize expressions simultaneously, and to guide composition incrementally,

i.e. by partial synthesis.

Illico stops the analysis as soon as it encounters an unexpected expression, be it for

reasons of lexical, syntactic or semantic incongruences, to propose in exchange all pos-

sible expressions ﬁtting into this place and being in agreement with the constraints at

hand. The implemented top-down strategy is powerful enough to allow for analysis and

synthesis to be performed in a single pass. The same principle is used for composition.

Hence, a sentence is composed step-by-step, left to right, Illico offering at each step

a list of candidates for continuing the sentence built so far. Figure 1 (next page) illus-

trates this mode. The user having reached a certain state is waiting for suggestions by

the system.

Fig. 1. Illico Screen Printing.

Guided composition of expressions (words, phrases, clauses, sentences, etc.) is one

of the functionalities of Illico. But for the time being, the system is limited to sentence

completion. We shall see in the remainder of this paper what else is needed in order to

support guidance of message composition.

2 Limitation of Sentence Completion, or the Need of Controlling

Conceptual Input

Sentence completion systems are different from conventional surface generators [5, 6]

which start from a goal and a set of messages (input) in order to produce the correspond-

ing surface form (output). Actually sentence completion systems are less than surface

generators, which does not mean that they cannot be just as useful. Working quietly in

the background, Illico tries to be pro-active, making reasonable guesses about what the

author could say next. Hence, it supposes somehow, that the author knows at least to

some extent what he is/was going to say

. It’s main concern being to prevent the author

to get blocked or stranded. To do so it offers a set of terms (words) which, pragmati-

cally, semantically and linguistically speaking are possible continuations at this point

in the chain. Illico works basically the following way. Given some input (typically one

or several words) the system generates all possible consequences. To this end it relies

on a formal grammar. Since the next item in the chain can only be one element out of

many (the set, i.e. list of possible words), and since the system cannot guess the one

the user has in mind, it will ask him. To keep the list reasonably small it relies not only

on syntactic, but also on semantic and pragmatic knowledge, presenting only items that

satisfy the speciﬁed constraints.

As one can see, unlike most parsers, Illico performs analysis by synthesis, and while

it does not need any help from the user for analysis, it does so for synthesis, as, other-

wise it would produce too many sentences, most of which do not correspond at all to

the writers intention. Yet this is clearly not what we want. Figure 1 should give you a

rough idea of how Illico works. You’ll see basically three windows with, at the bottom,

the output produced so far (generally an incomplete sentence, here: “the child takes a

picture of the”); at the very left, the candidates for the next slot (apple, beer, brother,

...), and in the main frame, various kinds of representation, like the system’s underly-

ing ontology, pragmatic, semantic and syntactic information concerning the sentence

in the making. While Illico has various nice features, like for example, the fact that

you never get stuck, and that the produced outputs will always be well-formed at the

various levels, it has also a few shortcomings, in particular with respect to the number

of candidates displayed. Indeed, the candidate list (size of the set, or number of items

from which to choose) could deﬁnitely be improved, (a) if the system presented at the

top level categories (types) rather than instances (words), which should only be shown

at the very end (leaf level of the ontology), (b) if frequency were taken into account

(the likelihood of words varying with the topic), (c) if governors (eg. nouns) were pre-

sented before their dependants (eg. determiners, adjectifs), and (d) if variables were

given rather than all possible morphological values.

Of course, we can also assume, that the author does not even know that. But this is a bit of an

extreme case.

Another problem linked to set size (i.e. great number of words from which to

choose) is the fact that large sets tend to be distracting and to cause forgetting. Indeed,

as the number of candidates grows (as is typically the case at the beginning of a clause),

increases the danger to get drowned. Likewise, with the distance between the governor

and its dependant increasing, grows the danger to end up producing something that,

while in line with the language (Illico produces only well-formed sentences) does not

correspond anymore to what one had in mind. Memory and divided attention having

taken their toll. In order to avoid this, governing elements should always be determined

ﬁrst, and the set of data from which to choose should be kept small. In other words,

ﬁltering and navigation become a critical issue, and there are at least two ways to deal

with them.

In order to reduce the number of candidates from which to choose, one can ﬁlter out

linguistically and conceptually irrelevant material. This strategy is generally used both

by the speaker and the listener as long as optimal transmisssion of information, i.e.

reasonable input/output are considered as a major means to achieve a given communi-

cation goal (default case). Hearing someone say : “I’d love to smoke a....”, our mind or

ears will be “tuned” to smokeable items (cigar, cigarette, pipe) rather than to any noun,

no matter how correct all of them may be from a syntactic point of view. With regard

to our problem (sentence completion or message composition) this means, that the list

to be presented should be small and contain but “reasonable” items

. How can this be

achieved without sacriﬁzing coverage? Indeed, even a ﬁltered list can still be quite large.

Imagine that you were to talk about people, food, or a day of the year, etc. The number

of representatives of each category is far too big to allow for fast identiﬁcation, if the

candidates are presented extensively in an unstructured, or only alphabetically struc-

tured list. This can be avoided, and navigation can conserably be eased by categorizing

items, presenting them as a conceptually structured tree (type hierarchy) rather than as

a ﬂat list. Instead of operating at the concrete level of instances (all days of the year) the

user will now operate (navigate or choose) at a much higher level, using more abstract

words (generic concepts, type names, hypernymes) like month, weekdays, hour, etc.

Of course, ultimately he will have to choose one of the concrete instances, but having

eliminated rapidly, i.e. via categories, most of the irrelevant data, he will now choose in

a much smaller list. The gain is obvious in terms of storage (at the inferface level) and

speed of navigation.

3 Incremental Building and Reﬁning a Message Graph

To show what we have in mind, take a look at ﬁgure 2 (preceding page). It is inspired

by SWIM, an ontology-driven interactive sentence generator [10]. Let’s see how this is

meant to work. Suppose you were to produce the underlying message of the following

sentence “Zidane hammered the ball straight into the net”. This would require several

walks through the conceptual network, one for each major category (Zidane, hammer,

This idea is somehow contained in Tesni

ere’s notion of valency [7], in Schank’s conceptual

dependancy [8] and McCoy and Cheng’s discourse focus trees [9].

Step 1: Zidane...

adjective

adverb

•size

•color

•shape

• nationality

•degree

•manner

•frequency

simple

verb

•grasp

•move

•ingest

• tranf. of poss.

action, state,

process

predicate / relation

spatial

relation

preposition

•goal

•source

•position

• direction

logical

relation

conjunction

•purpose

•condition

•consequence

• cause

modifiers

quality / state

manner

adjective

adverb

composite (idea)

object

•comm. status

•definite

•indefinite

•number

•singular

•plural

•coordinate

•subordinate

tense

•past

•present

•future

•size

•color

•shape

• nationality

•degree

•manner

•frequency

vveerrbbaalliizzee

rreessttoorree

ccaanncceellccoonnffiirrmm

ccooppyyaadddd

mmoovvee

mmooddiiffyy

rreemmoovvee

lliinnkk

Scene

Attitude/perspective

purpose

polarity

positive

relation

negative

object

mood

•question

•statement

•command

•indicative

•subjunctive

simple

verb

•grasp

•move

•ingest

• tranf. of poss.

action, state,

process

•past

•present

•future

tense

predicate / relation

spatial

relation

preposition

•goal

•source

•position

• direction

logical

relation

conjunction

•purpose

•condition

•consequence

• cause

modifiers

quality / state

manner

adjective

adverb

composite (idea)

Idea

object

•comm. status

•definite

•indefinite

•number

•singular

•plural

•person

•place

•thing

•coordinate

•subordinate

•size

•color

•shape

• nationality

•degree

•manner

•frequency

ZZ II DDAANNEE

Step 2: ...hammered the ball...

number

singular

ZZIIDDAANNEE

Agent

Object

before now

time

HHAAMMMMEERR

BBAALLLL

vveerrbbaalliizzee

rreessttoorree

ccaanncceellccoonnffiirrmm

ccooppyyaadddd

mmoovvee

mmooddiiffyy

rreemmoovvee

lliinnkk

Scene

Attitude/perspective

purpose

polarity

positive

relation

negative

object

mood

•question

•statement

•command

•indicative

•subjunctive

simple

verb

•grasp

•move

•ingest

• tranf. of poss.

action, state,

process

•past

•present

•future

tense

predicate / relation

spatial

relation

preposition

•goal

•source

•position

• direction

logical

relation

conjunction

•purpose

•condition

•consequence

• cause

modifiers

quality / state

manner

adjective

adverb

composite (idea)

Idea

object

•comm. status

•definite

•indefinite

•number

•singular

•plural

•person

•place

•thing

•coordinate

•subordinate

•size

•color

•shape

• nationality

•degree

•manner

•frequency

conceptual

structure

possible

linguistic form

number

singular

ZZ II DDAANNEE

Agent

Object

NNEETT

direction

before now

time

directly

manner

HH AAMM MM EE RR

BBAALL LL

vveerrbbaalliizzee

rreessttoorree

ccaanncceellccoonnffiirrmm

ccooppyyaadddd

mmoovvee

mmooddiiffyy

rreemmoovvee

lliinnkk

Scene

Attitude/perspective

purpose

polarity

positive

relation

negative

object

mood

•question

•statement

•command

•indicative

•subjunctive

simple

verb

•grasp

•move

•ingest

• tranf. of poss.

action, state,

process

•past

•present

•future

tense

predicate / relation

spatial

relation

preposition

•goal

•source

•position

• direction

logical

relation

conjunction

•purpose

•condition

•consequence

• cause

modifiers

quality / state

manner

adjective

adverb

composite (idea)

Idea

object

•comm. status

•definite

•indefinite

•number

•singular

•plural

•person

•place

•thing

•coordinate

•subordinate

•size

•color

•shape

• nationality

•degree

•manner

•frequency

Step 3: ...straight into the net.

Fig. 2. An Interactive Graph Editor for Incremental Message Composition.

ball, straight, net)

. The path for “Zidane” would be “scene/idea/objects/entity/person”,

while the one for the shooting-act would be “scene/idea/relation/action/verb”. Concern-

ing this yet-to-be-built resource, various questions arise concerning the components

(nature), their building and usage. The three problems are somehow related.

What are the components? In order to allow for the interactive building of the mes-

sage graph we will need three components: a linguistically motivated ontology, a dic-

tionary and a graph generator. The ontology is needed for guiding the user to make his

choices concerning the elements to build the message from: concepts/words. The fact

that the user has chosen a set of building blocks (concepts, i.e. class names, or words)

does not mean that we have a message. At this point we have only a set of elements

which still need to be connected to form a coherent whole (conceptual structure or mes-

sage graph). To this end the system might need additional information and knowledge.

Part of this can be put into the dictionary. Hence, nouns can be speciﬁed in terms of sub-

categorial information (animate, human, etc.), verbs in terms of case-frames and roles,

etc. These kinds of restrictions should allow the connection of the proper arguments,

for example, baby and milk, to a verb like drink. The argument connected via the link

agent is necessarily animate, the information being stated in the lexicon. If despite all

this, the system still cannot build the graph (suppose the user had given only the equiv-

alent of two nouns and two adjectifs), it will engage in a clariﬁcation dialogue, asking

the user to specify, which attribute qualiﬁes which object. Once all objects (nodes) are

linked, the result still needs to be displayed. This is accomplished via the graph gen-

erator, which parallely to the input displays incrementally the message structure in the

making.

How to use the resource? Obviously, as the ontology or conceptual tree grows, in-

creases access time, or more precisely, the number of elements from which to choose

to reach the terminal level (words). In addition, the metalanguage (class names) will

become more and more idiosyncratic. Both of these consequences are shortcomings

which should deﬁnitely be avoided. This could be done in several ways: (1) allow the

message to be input in the user’s mother tongue. Of course, this poses other problems:

lexical ambiguity, structural mismatches between the source and the target language;

(2) start navigation at any level. Indeed, when producing a sentence like, ”give me a

cigarette”, hardly nobody would start at the root level, to reach eventually the level of

the desired object. Most people would immediately start from the hypernym or base

level; (3) allow access via the words’ initial letters, or, even better, (4) via associatively

linked concepts/words: apple yielding computers which may prompt then Java (both a

computer language and an island), coffee (product of Java) and ﬁnally mokka, the target

word. For more details, see [11].

Last, but not least, there is no good reason to have the user give all the details

necessary to reach the leaf-, i.e. word-level. He could stop anywhere in the hierarchy,

providing details later on. This being so, he can combine breadth-ﬁrst and depth-ﬁrst

strategies, depending on his knowledge states and needs. Obviously, the less speciﬁc

the input, the larger the number of words from which we must choose later on. Yet,

The upper part shows the conceptal building blocks, structured as a tree, and the lower part

contains the result of the choices made so far, that is, the message built up to this point. To

simplify matters we’ve ignored the attitude or speech-act node in the lower part of our ﬁgure.

this is not necessarily a shortcoming, quite to the contrary. It is a quality, since users

can now decide whether they want to concentrate ﬁrst on the big picture (general struc-

ture or frame of the idea) or rather on the low level details (which speciﬁc words to

use). Full lexical speciﬁcation is probably not even wanted, as it is not only tiresome

as soon as the ontology grows (imagine the time it might take just to produce the con-

ceptual equivalent to a message like a beer, please), but also it may pose problems later

on (surface generation), as the words occuring in the message graph might not be syn-

tactically compatible with each other. Hence, we will be blocked, facing a problem of

expressibility.

4 Conceptual and Computational Issues

Building the kind of editor we have in mind is not a trivial issue and various problems

need be addressed and solved :

– coverage : obviously, the bigger the coverage, the more complex the task. For

practical reasons we shall start with a small domain (soccer), as we can rely al-

ready on a good set of resources both in terms of the ontology and the corre-

sponding dictionary [12]. Kicktionary, developped by Thomas Schmidt (http://

www.kicktionary.de/Introduction.html), is a domain-speciﬁc trilin-

gual (English, German, and French) lexical resource of the language of soccer. It is

based on Fillmore’s Frame Semantics [13] and uses WordNet style semantic rela-

tions as an additional layer to structure the conceptual level.

– language speciﬁcity : there are good reasons to believe that the conceptual tree

will be language dependant. Think of Spanish or Russian where verb-form de-

pends on whether an action is completed or not (aspect), yielding two, morpholog-

ically speaking, entirely different root-forms (ser/estar, meaning to be in Spanish,

or “uchodits” vs “uitsi” to walk in Russian).

– ergonomic aspects (readability) : the graph’s readability will become an issue as

soon as messages grow big. Imagine the underlying graph of a multiple embedded

relative-clause. Also, rather than frightening the user by showing him the entire tree

of ﬁgure 2, we intend to show only the useful (don’t drown the user), for example,

the children nodes for a given choice.

– the limits of symbolic representation : as shown elsewhere for time [14] and space

[15], symbolic representations can be quite cumbersome. Just think of gradient

phenomena like colors or sounds, which are much easier represented analogically

(for example, in terms of a color-wheel), than categorially.

– the problem of metalanguage : we will discourage the user if, learning the target

language is only possible by learning yet another (meta) language.

– the conceptual tree : there are bascially two issues at stake: which categories put

into the tree and where to place them. Indeed, there are various problematic points

in ﬁgure 2. Where shall we put negation ? Shall we factor it out, or put it at every

node where it is needed ?

5 Checking Conceptual Well-formedness

Obviously, messages must be complete and well-formed, and this is something we

would like to check in our future component. Suppose you were to make a compari-

son, then you must (at least at the beginning) mention the two items to be compared

(completeness), and the items must be comparable (well formedness). In other words,

having chosen some predicate, a certain number of speciﬁc variables or arguments are

activated, waiting for instantiation. Yet, arguments are not only of a speciﬁc kind, play-

ing a given role, they also have speciﬁc constraints which need to be satisﬁed.

While checking well-formedness for single words does not make sense (apart from

spell checking, which is not our concern here), it does make sense to check the com-

patibility and well-formedness of the combination of concepts or words, to see whether

they produce an acceptable conceptual structure. In the case of our example, ﬁgure 2,

this means that, having received as input something like to shoot (or, to hammer), we

know that there is someone, performing this action, with a speciﬁc target in mind (the

goal), and that the action can only be performed in so many ways (manner). While not

all of this information is mandatory, some of it is (agent, object, target), and there are

deﬁnitely certain constraints on the various arguments (the agent must be animate, the

object, some kind of sphere, typically used in soccer games, etc.). Being formalized

and stored in a conceptual dictionary, this information can now be used by our sys-

tem to check the well formedness of a given structure and its compatibility with the

user’s input. The conceptual information being organized hierarchically, we use Coq

[16], that is, a typed λ-calculus providing a framework with a rich type system. Hence,

the dictionary entries might look as follows :

Parameter hammer : Agent -> Object -> Target -> Prop.

Parameter Zidane : human.

Parameter ball: soccer_instrument.

Parameter net: soccer_equipment.

P rop stands in Coq for the type of proposition ; roles (Agent, Object, T arget) and fea-

tures (human, soccer instrument, soccer equipment) are generic types. We must also ex-

press conceptual constraints for the various types, for example, Agents must be animate. Coq

uses the subtype principle (inheritance graph) to check that all constraints are satisﬁed, deﬁn-

ing human, soccer instrument and soccer equipment respectively as subtypes of Agent,

Object and T ar get. When all the constraints are satisﬁed, we have what it takes to represent the

semantics of a sentence, which in our case would be something like, ”there is an agent who did

something in a speciﬁc way, by using some instrument.” In other words : there is a person, an

object and a target (let us call them respectively a, b and c), the three arguments being linked via

an action that is being performed in a speciﬁc way. This general idea can be described as follows :

Message definition := exists a, exists b, exists c,

is_someone a /\ is_something b /\ is_manner c /\ relation a b c.

Hence, in order to produce the global message Zidane hammered the ball straight into the net, we must instantiate the com-

posite propositions respectively by is

Zidane of type human → P rop, is ball of type soccer instrument →

P rop, is net of type soccer equipment → P rop. Hammer is already declared. Once this is done, the param-

eters Zidane, ball and net can be applied to produce the desired result, the system type-checking the compatibility of

the involved parameters. In other words, checking the conceptual well-formedness and consistency of the messages amounts

basically to type-checking the elements of which the message is composed.

6 Conclusion and Perspectives

We have presented and discussed a practical solution to one of the shortcomings of an existing system. To improve its

conceptual component and to allow for conceptually guided message composition we suggested to build a linguistically

motivated ontology combined with a dictionary and graph generator. While there are many ontologies, we cannot draw on

them directly, because they were simply not built for message composition. Strangely enough, a lot of relevant work has

been produced outside the area of language generation, yet it has been largely ignored. Many great minds have devoted their

energy to design an interlingua or universal language, Ramon Lullus, Leibniz, Descartes or bishop Wilkins, to name just

those. We will certainly take a closer look at this work, as in spite of its age, it might contain highly useful information for

our future work.

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