- Email: [email protected]
A French learner's workbench
Pergamon
A FRENCH LEARNER’S
WORKBENCH
MARIE HAYET Department of Language and Linguistics. fr-ntuil: h~yetmc~cci.umist.~c.uk~
England
language learning intended for students distinct scientific students studying approach which this system attempts support is based on the view that ignorance of format linguistic devices quality and the etficiency software environment integrates packages for the presentation graphics and sound in study and reference materials. software for remote access or computer-mcdivted communication. practice and testing. software to the learning process. The method of study is independent, purposes of each unit of courseware. enables learning material supports theoretical lcaming. Provision has been made for practice support empirical mediated communication enables learning and supports communicative on-line electronic linguistic tools which learning through a of guided discovery and exploration Initi;il using this learning indicate th;lt elfcctivc CALL does ;I of media and approacha which text-based st;lntl-alone packapcs may not schievc.
reading
This paper presents a hypcrmcdia computer assisted from the point of view of its software components and the spccitic I~tngtIi~Se-learning tasks which they enahlc[ 11. An approach based on linguistic technology is pccLlliilriy suitable for the student p~~piIt~~(ion of a university of technology. Non-specialist students read French to get it footing in the Frenchspeaking world of their professional speciality. Most of these students are highly computcrlitcrate(2j. For language-specialist students. CALL forms p:trt of their education in ~~~t~ltilin~lii~~ linguistic engineering; over and above a learning method, it constitutes an object of study and devclopmcnt work[3]. Within this framework, it is appropriate that Icarncrs should make extensive use of electronic linguistic tools for the study of French, at lcast of those tools which are sufliciently simpic and robust to support the acquisition of the foreign language structures adequately. Such tcchnologics have therefore been integrated to support a method of learning which Iargcly involves the exemplification of the linguistic phenomena which are to be studied through guided discovery. The method also subserves a process of induction for the acquisition of the more formal linguistic principles or rules. The French learner’s workbench is based on a hypermedia platform which breaks away from traditional, linear and text-based CALL systems. It allows for a wide range of learning stimuli and supports the learning of new material, as well as practice or testing for remedial purposes. Multimedia also recreates a more genuine linguistic environment and features authentic documents and textual. graphical. or sound documents. Sound is used for linguistic illustrations, for the comparative grammar of spoken vs written language, for the pronunciation of certain words. on their own and in context. It is intcndcd to add sound to local dictionaries and to develop a course in articulatory phonetics. with supporting illustrations and exercises.
The hypermedia CALL system runs on an IBM PC compatible 486SX machine which provides an acceptably fast mode of operation. The multimedia pcriphcrals attached to the machine are a CD-ROM drive, a microphone and a pair of speakers, via a SoundBlaster Pro board. High resolution, 256 colour and black-and-white still images are displayed on a colour monitor by a Tseng ET4000 Turbo MegaVGA video card with 1 Mbyte of memory. 91
The operating environment is based on Microsoft-Windows 3.1. Only limited use is made of the Windows multimedia extensions as the SoundBlaster Pro board editing software (Sound Blaster Pro Voice Editor) was found to be of better quality for the recorded voice. microphone input tiles which we most frequently used. and incurred lower overheads than the built-in Windows multimedia extension, e.g. automatic buffering for playback from hard disk with the editor. stereophonic .WAV files. use of compressed sound liles[J]. Various tools have been added for the acquisition. edition and manipulation of graphics. Guide (below) provides the means of controlling the speed of the succession of displays by sending a simple coded script to the Windows Media Control Interface (MCI). Guide can also play .MMM animation files and there is even a function to create and distribute Guide documents on CDROM. Finally. the French learner’s workbench is connected to a network and communications software enables on-line access (via serial port) to remote software resources. The public domain package Kermit is being used until it is replaced by Telbin, a sophisticated but public domain TCP communications package and terminal emulator (an enhanced version of Telnet) developed by Clarkson University in the U.S.. when an Ethernet network will be in place and all machines will have been equipped with network cards.
The application is based on the hypertext package Guide 3. I, a proprietary product of OWL International Inc.[5]. As is usually the cast with hypcrtcxt, the dcfuult cnvironmcnts have been dcsigncd to support information systems and were not intcndcd as Icnrning mnchincs. Among thcsc, Guide was sclcctcd bccausc it came with its own programming lungwgc. Logiix. which allows for a cornplctc customization. of the cnvironmcnt to suit ;I specific application, hcrc for ;III educational purpose. A well-known drawback of hypcrtcxt is the case with which the user can bccomc lost in “hyporspncc”. Dctrimcntal as this may bc in an information system, it bccomcs ;I serious problem in a Icarning contcxt[b]. Ellvrts to turn an information system into an clrcctivc Icarning tool thcrcforc conccntratcd on such issues as navigation within the system. In CALL whcrc the learning proccdurcs and the courseware arc to bc used by any Icarncr, irrcspcctivc of their personal Icnrning needs or style. some compromise had to bc rcachod bctwccn highly dircctivc learning paths and complctc user freedom within the courscwarc. On the one hand. the abscncc of any constraints would cquatc with presenting learners with a grammar book, ;I dictionary and some excrciscs, and hoping that they can learn enough from the mistakes they make. On the other, a strict succession of screen displays inhibits all personal enquiry and fosters a passive attitude to Icarning[7]. The interface developed and the organization of the documents have been designed to support teaching strategies and learning scenarios which have, to a large extent, been prc-detcrmincd but still remain tlexible enough to allow for individual enquiry and multiple learning styles. It is unclear yet whether mixing learning approaches to suit diKcring learning styles may prove educationally enriching for all learners, as it reinforces acquisitions made in any idiosyncratic style. When the learners’ bchaviour has been thoroughly studied and their resulting knowledge gain has been evaluated. the interface will be developed into ;I fully-flcdgcd learning management subsystem. An on-line help system comprises two tutorials, one on how to use Windows and one on the application. in the form of an example of II short lesson navigating from core documents to reference material, exercises and available tools. A Session button gives information about the date. the time of day, the title of the current course unit, the title of the document which should be studied that week, the title of the current document. the total number of pages in the current document, the current page number, the list of the documents which have been opcncd and those which have not, the list of the excrciscs which have been attempted and those which have not. The system keeps track of such records for all learners who are known individually. Guide, with Logiix, was also chosen for its capability to seamlessly integrate misccllancous software packages as subprocesses whether these are hosted locally on the PC hard disk or remotely. This enables a highly modular structure where application components can be added, modified or removed.
A French Icclrncr‘s uorkbcnch
90
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tusks. A number of rcscarch programnws. c.g. EC DELTA ACT[8]. huvc invcstigatcd innovative forms 01‘ conifutcr-
Icarning ancl f’ound that. properly orgunizcd. group-b;tscCi Icarning can bc Learning shown to have ii positive impact on st~~dcnts’ acquisition of subject-spwific knowlcdgc. to lvork as ;I constructive nmnbcr of a group is ;I key transfcrablc skill--one which is gaining
supportccl collaborative
incrcusing
The in-house.
attention
;~pplication others
sol’tw:lrc ivhich
in f IE.
intcgratcs arc
;III
array ol’sofl~varc n~o~l~~lcs and packa~cs. Sornc have olhcrs arc comnicrcial or sharcwrc.
in the public domain
constitute
the
;~pplication
f:Lf:CTRONIC
itself
can
bc classilicd
LfNCUlSTlC
Although intcndctl li>r fxiclising linguists. thcsc tools through appropriate teaching stratqics. The tools comprise tcrrninologics. thesauri ant1 lists. stored locally or rcnwtcly
in three
been tlcvclopd packages. The
catqorics.
TOOLS arc iiscd as lan~ii;igc-tca~lling tools bilingual and monolin_cu:ll dictionark on CD-ROMs in the main library of
the university[lO]. a spelling and grammar checker for both languages (Grammatik 5)[11]. simple parsers returning linguistic statistical data to be used for stylistic analysis and writing exercises, and a sophisticated concordancer (TACT) which operates both on English and French. together with a growing collection of text corpora. Commercial lexicographical and terminological resources are used within the constraints of their own software. e.g. various Larousse dictionaries. In-house lexicographical collecting focuses on lexica which lie outside the scope of traditional dictionaries: words for new realia. e.g. “Minitel”, words to create or translate new concepts. e.g. “text-parsing” or “twonking”. Franglish and orthodox words (as published freely by the Commissariat G&&al de ia Langue Francaise). e.g. “butteur” for “bulldozer”, idioms. especially euphemistic denominations, e.g. “technicien de surface” or contextual modulations (“rien i cirer”), slang. jargon. idiolectic lexica, false friends, proverbs and set expressions. etc. These have been mounted on the old British term-bank prototype, developed in our department some years ago for a CEC-funded project[ 12,131. Among the commercial packages there features a dictionary which provides an exceptionally rich environment for the study of the French lexicon: Dicologique. Dicologiqlrr Dicologiquc calls itself a dictionary. but it does not contain standard definitions and has no fixed entries. It contains words and phrases (105,000. 50% more than the Grand Robert or the Laroussc) and 20.000 “notions”. e.g. “>illusion de la raison” (illusion of reason). “>place de ce qui occupe une position” (location of something which occupies a place). “>fort physiqucment” (PhySiCillly strong). “>mais” (but), etc. Dicologiquc makes full USC of the capabilities of the clcctronic medium and of the concepts of invcrtcd files and hypcrtcxt to structure information hierarchically as in a classification, and in combination as in a thesaurus. Words, phrases and notions can cntcr into i\ number of sumantic relations which enable navigation through the data. When looked up using the function Rcchcrchc. each entry is displayed and “dclincd” by what is known as a “quasi-dtlinition”. A quasi-dclinition is not an explanatory text, with quotations, idiomatic phrases, indications of etymology, phonetic translitcralions, clc.. it is a set of notions, words and phrases which marks the position of an entry within a semantic field and indicates its semantic relations with cognate entries. From its position and the semantic relations, one infers the meaning of the entry[l4]. This approach to defining a word or a term is useful in teaching, to introduce the concept of semantic field and to build organized collections. It can also support the introduction of the semantic relations which underlie glossaries or terminologies, various metalinguistic concepts, e.g. gloss, paraphrase, periphrasis, a~ well as the various types of dictionaries and their usage. Dicologique uses six types of semantic relations: liste. classc, description or caractCristiquc, termes liCs (related terms), tht:me, with an indication of the number of items which arc linked by any such relation. These are loosely based on the standard relations found in classifications and thesauri, although in Dicologique they arc more specific and the network therefore richer. Lisrc. Listes link the words which share a given scmunteme. “Fcuille” has part of its meaning in common with “foliation”, “fane”, etc. The terms composing a list arc always of the same grammatical category as the entry, i.c. verbs. nouns, adjectives or adverbs. Listcs contain synonyms or near-synonyms and antonyms, but the longer the list, the less perfect they arc likely to be. Some linguistically impcrfcct terms, howcvcr, arc usage or documentary synonyms, which give an interesting indication of the preferred term in a particular environment. The listc-notion encompasses some aspects of the relation of cquivalcncc in a traditional thesaurus-for “a”, use term “b” or term “c” is used for “d”-as it groups together scattered terms on the basis of their linguistic USagC. Listcs arc useful writing aids to avoid repetitions. to find the precise word or, with the help of an equivalent French term. the translation of concepts already known in a foreign language. They help to spot false friends among semantically cognate terms. They support the study of lexical semantics with the description of the words in terms of their relations with other words within a subject field, while developing the awareness of the groupings dictated by usage in a foreign language. They are also a good vehicle for the study of the morphology and syntax of the words,
A French bmcr’s
=
101
workbench
DICOLOGIOUE
3echerche
Navlgatlon
reQu@te
aklal.
fin
2
feullle
2 : feuille. >page (liste)
N C fem. sing. [s mobs)
3 : feuille. N C fem. sing. >lame (liste) >m&al (termes lies)
(22 mots) (44 mots)
4 : feullle. N C fem. slng. >autre ornement (classe)
(82 mots)
5 : feulll& AdjecWVx >garnl de feullles (plante) (liste)
(3 mots)
6 : feullle. Xechnlque
N C fem. slng. plcturale (classe)
(32 mots)
7 : feullle. N C fem. sing. >symbole chretlen (classe)
[63 mots)
LOO-IONS feullle a fendre Lot NomlnaJe volr la feullle a I’envers Lot VerbalelArg. feullle allmentalre Lot Nomlnale feullle d’acanthe Lot Nomlnale feullle de thou Lot NomlnafelFam. feullle de papler Lot Nomlnale Lot Nomlnale feullle de presence feullle de route Lot NomlnaJe feullle de sauge Lot NomlnalelDld. feullle de vene Lot Nomlnale Lot NomlnalelDld. dorure A la feullle feullle volante Lot Nomlnale wllle
: 38
Fig. 2. Screen dump illusrr;tring Dicologique.
compounds. in relation to their meaning and for exercises in lexicographical information, e.g. definitions, exumplcs, rcgistcrs, translations, etc. Clcrsse. Classe denotes hierarchical relations. from the gcncric to the partitive, of realia. “Feuillc” is one elcmcnt of a class of 82 architectural ornaments. As a dictionary. Dicologique dcscribcs words; as ;I mix bctwcen classification and thesaurus. it dcscribcs cxtralinguistic objects and realities. Classes establish relations between terms which belong to the same domain of activity or knowledge. e.g. “>sciencc m6dicalc (classe) (I25 mots)“. They arc used for relations of location in time or for social and administrative hicrarchics and only cvcr contain nouns or noun-phrases. Through classc-notions. it is easy to introduce the principle of classification for purposes of defining and structuring the contest in which the language opcratcs, its associated sublanguage and text type(s). As they group together different terms denoting varying degrees of precision in their description. they help learners refine the accuracy of their linguistic expression, from especially
CAE 23:1/2-H
102
MARIEHAYET
the broader term to the narrower term. This structuring of the lexicon is also highly appropriate for advanced or corporate training. as learners with a good knowledge of a domain can find translations for unknown words easily[ 151. Description. Description is another type of partitive relation, denoting the physical or the conceptual components of the entry. “Feuille” is a physical part of a tree. Terms associated with this relation will therefore be nouns or noun-phrases. As well as providing anatomically organized lists of vocabulary, the semantic relation “description” provides a good framework to learn about metonymy and other related stylistic figures[ 16). Curuck-isfique. Caractlristiques state a property of the entry and are mostly adjectives. ‘Vert’ (green) is a caracteristique of “feuille”. Most caracttristiques denote colour. shape or size. and form groupings according to this characteristic. Caracttristiques-notions can be used to generate clues for cross\vord puzzles or riddles like: “What is red and has a stone?” Answer: a list of red fruit comprising a variety of cherries and plums. Puerile as they may seem. such riddles oblige the learners to think about cognate words, as all elements of the riddle may not appear directly as an entry. They must also interpret the riddle correctly to find the correct French equivalent words (stone j kernel). then cross t\vo or more sets of terms possessing either quality to select those which have both (function “rcQu~te” ). Difficulty in the interpretation of the main predicate can be increased, e.g. “What is red or green and grows in kitchen gardens ?” (answer: pepper); “Qui n’est pas en colCrc mais voit rouge et mcmc vert‘?” (Who is not angry but sees red. and green to boot’?) (answer: a daltonian). Tcrnrcs lib. Termcs Ii& (rclatcd terms) contain various kinds of words or phrases. &longing to ill1 grilll~nliitiCill catcgorics. e.g. adjective corresponding to il noun entry, il property of that entry, the name of a scicncc, a profession. etc. “Fcuillc” meaning “sheet” (e.g. of “metal”) is rclatcd to 44 words denoting characteristics of metal, tools and tcchniqucs of metalwork. This semantic relation is useful to construct banks of terms for technical languages. The “tcrmcs Ii&” relation can cxtcnd the Icxicographical study conducted with the “listc” relation to the study of terms within a subject licld. with the addition of further information. c.g. dclinitions for univocal terms, usage contexts discovcrcd in Corporil, qllillity indication, i.e. slandarcl, prcfurcd, nonprcfcrrcd,
foreign
Iiln~llilgC
CcllliVillCntS.
ok:.
T/M%I~~.ThL:mcs
show most clearly the way in which knowlcdgc in Dicologiquc has been structured. In “fcuille dc la pIante (thcmc)” appear “chose en formc dc fcuillc (classc) (6 words)” (things in the shape of a leaf), “qualificatif de la formc dcs feuillcs (liste) (66 words)“, etc. Thtimcnotions are hypcrtcxt nodes which can bc navigated downwards (fonction contenu) and up\vards (fonction remontCe)[ 171. “Feuille” is embedded in a higher notion “description dcs plantes (th2mc) (5080 words)“. itself embedded in “regne vt:gGtal (thcmc) (45260 words)“. The higher in the hierarchy one is, the more “tht:mcs” there are, relative to other relations. In teaching, the th2mcrelation is used for indexing applications and in LSP for the passage between word and descriptor, or term. Spccinljlrnc,tions. A host of functionalitics based on words as strings of characters csisIs in Dicologique. When looking something up which does not feature in the dictionary. the system displays morphologically close words (“mots SC rapprochant de X”). The function Musclucs Hhich allows wild cards to replace one or several characters in a word helps with crosswords or the search of some affixes to study a particular type of word formation or to find rhymes. or to teach Verlan! The function Anagrammes. which also gives palindromes. can be used for games with young or inexperienced learners. Finally, the function reQuttc enables a user to mark certain words or groups of words and to subsequently apply Roolcan operations on thcsc groups. The electronic medium offers iI good opportunity to revisit the problem of vocabulary acquisition. Complementary clcctronic tools can be used to make notes and build up specific Icxicographical resources, e.g. simple text editors todraft word lists. datilbases to structure iind rctricve Icxicographical information sclcctivcly, term-bank systems containing very thorough linguistic descriptions. and concordancers for the contextualization of lexicographical and terminological data[ IS]. TACT
TACT is a text-retrieval program which is being developed at the University of Toronto. directly available by anonymous ftp. TACT gives much information about a text corpus:
It is total
A
French Irurnsr‘s workbench
103
number of words, number of different words with their frequencies and a distribution graph showing the number of occurrences for each 10% of the text. their location in the text by chapter. section, paragraph. act or scene. etc.. the context in which they occur. and their direct collocations within five words on either side of the word. TACT also calculates the probability of collocations. the number of such collocations one can expect to find in a given text and the standard deviation. Collocations can be extended to simultaneous occurrences of two or more words within a predetermined unit, a paragraph. for instance. TACT also allows complex input requests, using a sequence of m&a-characters and letters to describe a word. phrase or sequence. for more powerful retrieval. Using such regular expressions, one can search for any structure, from morphemes to whole phrases or even patterns, taking word positions as a basis. Regular expressions can be stored. modified and subsequently reused. In CALL, TACT is used in various ways[l9]: as an expansion of Dicologique for keywords in context (KWIC) and the selection of terms[30]. to define and describe the text-types in which they appear; in morphosyntax and text grammar (see the next section Mili-Melo) to study patterns and linguistic phenomena as they occur in various corpora. It has a context-sensitive help system and allows the user complete control over the way results are displayed. Otltcr supporlirtg
tool.~
Other software packages support the work of a practising linguistic tools specifically. They comprise:
linguist
and a learner
without
being
an clcctronic notebook with a facility to import certain matcrinl by direct cutting and pasting (other material has been made read-only to discourage cxcuss printing) a simple datilbilsc USC~ to gcIlcr:itc forms or to manage information: and misccllancous utilities like an impcrinl-metric convcrtcr for USC in iI lesson about the languilgc of basic scicncc and for another about clothing and tcxtilcs. Indeed, almost
:111y
clcctronic
tool can bc of
USC its il
Icarning aid.
The course units contain embccldcd stilncf:ld CALL cxcrciscs mounted on various authoring systems, for practice and testing of rules and patterns but also to support the Icurning process itself. It is an important feature that all cxercisc software can be used for any of these three purposes. A question is not always asked to check whether somebody knows something, it can also act as a formative devicc[Zl]. The cxcrcisc in Fig. I asks sequences of questions about the markers of t?noncintion, aspects, and tcnscs ivhich have been dcsigncd to help learners ask themselves the appropriate questions bcforc deciding on the USCof it linguistic tense and translating the contextualized English form into French. The authoring -some
systems used to mount such exercises are:
Logiix scripts, csscntially multiple-choice
qucstionnaircs
which arc launched iIS part
of speed-reading cxcrciscs directly under Guide to ensure faster and timed operation, and to stop Iearncrs from rctricving the answers from the study material by changing application under Windows; -Question MilrkrM taking advantage of the possibility of mixing question typcs[22]: multiple of a stored string of choice. multiple sclcct. all questions based on the comparison
alphanumeric characters with a string given as an answer, gap-filling and some questions allowing free-format ilnswcrs in natural LlllgUilgC; -the Wide packages: Mot pour Mot (rebuilding of texts based on strings of characters). A votrc avis (multiple choice qucstionnaircs). Autrcmcnt Dit (altcrnativc formulations or transformation patterns). A dcmi mot (gap-filling), Tcrme i Terme (pairing up indcpcndcnt elements somehow conncctcd). En tous scns (dictionary-building software with limited space for information about each entry) ct Jcux de Mots (games on the Icxicon). A juste titre
IM
(matching titles with short texts, matching one title from a group with a text which can be progressively revealed); -an
original
tool developed in house: MGM2lo.
In all practice exercises, access to grammar
references (in lessons)
or to dictionaries
(integrated
tools) is possible at any time.
Mdi-Aldo.
M&MOlo
was developed to support
a course in text grammar
and discourse
structures[23] and to be used. to a lesser extent. for some manipulations in morphology. afixes in particular, and sentence grammar. Available authoring packages did not meet the requirement to “explore the interface between syntax and the construction of coherent text”. that is to say to create exercises to manipulate complex syntax or discourse articulations in a wider linguistic context. In software terms. M&Ii-MClo is a fairly simple text manipulation package which performs the deconstruction
and reconstruction
of texts at all levels of linguistic
units: morphemes, words.
phrases, clauses, sentences and paragraphs. It can also handle groups of sentences in keeping with the rhetorical structure of a text, separated by discourse connectors. e.g. “d’abord, en premier lieu. aussi, en revanche. cependant. non seulement . . mais (encore), car, ainsi. d’ailleurs, notamment, d’oti. done”. to quote but one example of each rhetorical category. It is possible to reconstruct any linguistic units into any greater linguistic units, i.e. morphemes can be reconstructed into words or compounds-a central theme in French grammar. especially with “noms compos&“[%] and technical tcrms[75]; words into compounds, clnuses or sentences: phrases into clauses or scntcnccs. sentcnccs into paragraphs or groups of scntcnccs; paragraphs into text. Howcvcr. not all types of reconstruction,
e.g. morphemes or words into text, make much scnsc.
Another useful feature of ML:li-ML:lo rclatcs to the contcxtu~llizntion of linguistic phcnomcna. Thus. when an cxcrcisc requires it. reconstruction and dcconstruction can bc pcrformcd while the context of the piccc of text being worked on is displayed in a window. As the reconstruction progrcsscs. the correctly ordcrcd picccs of text arc added to that context. Two scrollable windows appear on the screen, one which contains the stock of random units and 011~ whcrc reconstruction work takes place, with or without context. If ncccssary. the Icarncr can go back to another window which contains the description of the task and spccilic instructions or help. It is also possible to return
to the original
vcrsiou of the text to bc manipulated if this
has bcsn allowed by the tutor in the setting of the cxcrcisc. Lcarncrs successively sclcct the clcmcnts from the jumbled list to rcconstitutc the original order or. in the cast of mobile olcmcnts, e.g. complctivc ClilUXS, in any correct order. Deconstruction is intcndcd to bc pcrformcd mostly by the tutor but it can also bc initiated by the learner as an exercise to spot structural clemcnts. The dcconstruction into words, scntcnccs or paragraphs is pcrformcd automatically by the software but manual intervention is often required to distinguish. “aujourd’hui”
for instance, bctwccn “c’cst” (it is) or “est-il” (is it) (3 words) and between (today) (I word) or “rouge-gorge” (robin) (I word). Similarly. the dcconstruction
into morphemes. phrases. clauses. and discourse movements is performed by marking the text with jumbling units and upper jumbling boundaries. The marking process is done intcractivcly as it is possible to jumble the marked elements at any time to check whcthcr the marking has been done correctly. In theory, the same text could be marked to bc used for ditTcront purposes but few texts constitute good examples for more than one type of structure. Texts can be written by the tutor or the learner but they arc normally sclccted from corpora held in clcctronic form and retrieved selcctivcly on the basis of their containing a good proportion of a given linguistic structure. Textual structures to bc cxcmplilicd, manipulated and rcflcctcd upon with Mtli-1Melo comprise: -specific word order dillicultics: compounds (nominal or adjectival) and their modifiers (adjective, adverbs. complctivc clauses): pronoun placement, ncgativc. passive and intcrrogativc constructions; some diflicult relative clauses: cleft and pseudo-cleft sentences. dislocated or topicalizcd elements, with the comparison of thcsc structures with their “unmarked” counterparts and, in context. their efTcct on textual cohesion. --complex
syntax
(e.g. hypotaxis.
embedding. co-ordination)
and the recursive
nature of
A French Iramrr’s
workbench
105
syntactic rules (nesting), by gradually increasing the complexity of a core sentence. or by comparing, in their context. sentences in which mobile clauses. e.g. ‘circonstancielles”. adverbial, appositive clauses, appear in ditferent positions. --text cohesion and coherence[26]. e.g. the order of complements or the sequencing of parts of text (sentences and paragraphs in particular), especially when they deviate from the norm to support a particular pragmatic purpose. e.g. to place a greater emphasis on a particular piece of information. or in keeping with a particular pragmatic parameter. e.g. what is already known by an interlocutor: the study of anaphora and cataphora by replacing in the text previously-removed anaphoric or cataphoric elements[77]. or by substituting such elements by self-contained equivalents. and studying the ensuing impact on the overall text structure. Such a tool. which jumbles predefined linguistic units and embeds components within text. is highly versatile but its greatest potential lies with the uses which are devised to develop learners’ awareness of language structures and their sensitivity to language in general. thus supporting the acquisition of various receptive and productive skills[B-301. CONCLUSION
The technology-based approach to CALL which wc have dcscribcd closely integrates linguistic study and electronic linguistic tools to bring about a rctlection on linguistic phcnomcna and linguistic usage. Learning about the tool, what it is intcndcd for. what it dots and what it fails to achicvc implies rcflccting on langungc. The >Ipproi~ch also gives lcarncrs a good grounding in the USC of the information technologies. Co[nputcr-ilssistcd Icarning and clcctronic linguistic tools constantly intcruct, thus incrciIsillg the IcilrntXs’ cxposurc to IilIl~Uil~C illld enhancing their linguistic awarcncss through critical reactions to inevitably impcrfcct tools. AS CALL. the mclhod yields the organizational bcncfits to bc cxpcctcd when computers rcplaoc tutors. iIt ICilSt l0 SOnlC cxtcnt, e.g. indcpcndcnt learning, Sony dcgrcc of open learning. the possibility of absorbing incrcascd student numbers and sonic ncccssary rcmcdial provision at no extra cost in tutor time. AS sol’lwitrL’based teaching slratcgics with mi~nip~~lations, the 111cthod c[lhiInccs the lcnrni~~g process by concentrating 011 active learning for ciich individual lcarncr. 11~ and large, fhc students enjoy CALL but they do not tind it easy. The task remains to design and JCVC~O~ better lOO!S, to dcvisc intcrcsting ways of making USC ofothcr existing tools, in particular perhaps to organize transparent access to bigger Unix systems whcrc fhc sun shines on analyscrs, parsers, taggers. gcncrators, and machine translation software. REFERENCES I.
2.
Ambron S. and Hoopcr K. (Eds). f~rrcnrcrivr A/lr/rirwc/io. Microsoft Press. Washingron ( I’JHB). Dodieovic M.. In~crdisciolin;~rv: coIllnulrr-ilssisIcd linnuisfic rcsc;lrch and [hc dcvslopmrnt of LSI’ courscwrc.
.l 5. Dcrgan
M.. Guide. CTfSS File 13. 27 -29 ( 1992). J.-F.. Coenirive oroccssine of hvncrdocumcnts: when does non-tinwrily . http? Prr~cwc/i+~ o/’ 4//r AC‘ZI C~r/imu~r~EC~T ‘92. pp. I3 I -iJO. Mii;;no ( 19921. Gilt B.. Learning s~ylcs and the computer. or locus ofcon[rol and ideology in CAI. Nsw tuchnolo_cy for A new world. Budapcs~. In press. Dcl~a-STIG. Rcscarch and tschnotogy dcvclopmcnl of Ictcm;~tic sys~cms for llcxibl c and dislancc Icarning. Annual report. Brussels (1993). Kornum L.. Foreign Ianguagc teaching and tc;lrnin_p in a mulrimcdi;l cnvironmcnr. C
6. Rouct 7. 8. 9. IO. II. I’. 13. IJ. IS.
Ctrlicw
106
MANE HAYET
in RrsrricreJ Domu~ns (Edited by Grishman R. and Kittredge R.). pp. 69-83. Erlbaum, Hillsdale. N.J. (1986). 16. Fass D. C.. Semantic relations. metonymy and lexical ambiguity resolution: a coherence-based account. Procwclin,~ of t/w 9fh Annual Coyniriw Sciuncr .Socirr.v Confcrmrr. pp. 575.-586. Scrrttle & Washington ( 1987). 17. Raymond D. R. and Tompa F. W., Hypertest and the New Oxford English Dictionary. Technical Report. University of Waterloo Centre for rhe New OED. Waterloo. Ontario (1987). 18. Slator B. M. and Wilks Y. A.. Toward semantic structures from dtctionary entries. Pruwrclings uj’fhr Srcond Annuul Rock! Mounfuin Conferrncc on Arffjiciul Infelligmcr. Boulder. Colo.. pp. 85-96 ( 1987). 19. Tribble C. and Jones G.. Conconluncrs in r/w C/ussroon~: A Resourcr Book for 7ruchcr.s. Longman. Harlow ( 1990). 20. Meyer I. and McHaftie B.. De la focalisation $ I’amplilication: nouvelles perspectives de reprisentation des don&es terminologiques. Working paper 93/01. Laboratory of Artificial Intelligence. University of Ottawa. Ottawa (1993). 2634, Ftvrier-Mars (1984). 21. Soul&Susbielles N.. La question. outil ptidagogique dipa&? Le/iun~wis duns Iv Montk. 22. 23. 24. 25. 26. 27. 28. 29. 30.
Ward C.. Prcprring und I/sing Ohjccfivr Qwzfions. Stanley Thornes Ltd. Cheltenham (1981). Adam J.-M.. Elt;nrmrs de Lingrrisfiyuv ~.rfuclk~,-T/ft;orir cf Prufiqrrc dc Linguirfiquc Tr.rfuellc. Mardage. Liege ( 1990). Gross G.. Syntaxe des noms. Numtiro sp&ial de Ltmgtw frtrn~~uisc* No. 69 (1986). Guilbert L.. La specificit du terme scientifique et technique. Ltrn,qw Frurrpti.rr No. 17. 3 I33 ( 1971). Cemiglia C.. Medsker K. and Connor U.. Improving coherence by using computer-assisted instruction. In C~~lrcw~rc~~ in Hiifing VI (Edited by Connor U. and Johns A. M.). TESOL. Alexandria (19901. Comish F.. Anuphoric Rekffions in English und French. A Diswursr Ptqwcfiw. Croom Helm. London (IYS6). Anpelis P. J.. Sentence combining. error analysis and the teaching of writing. New directions in second language learning. Tcurhing und Bilinpul Educufion (Edited by Burt M. K. and Dulay H.). TESOL. Washington (1975). Butler C. S. (Ed.). Cwupufcrs trrrcl ltiiffcw fi*.vfs. Blackwell. Oxford (1992). Pkry-Woodley hl.-P.. Lcs &ifs tkmr I’rlppr~,rfi~str,~c,. Hachcttc Collection Refirences. Paris ( 1993 ).
A FRENCH LEARNER’S
WORKBENCH
MARIE HAYET Department of Language and Linguistics. fr-ntuil: h~yetmc~cci.umist.~c.uk~
England
language learning intended for students distinct scientific students studying approach which this system attempts support is based on the view that ignorance of format linguistic devices quality and the etficiency software environment integrates packages for the presentation graphics and sound in study and reference materials. software for remote access or computer-mcdivted communication. practice and testing. software to the learning process. The method of study is independent, purposes of each unit of courseware. enables learning material supports theoretical lcaming. Provision has been made for practice support empirical mediated communication enables learning and supports communicative on-line electronic linguistic tools which learning through a of guided discovery and exploration Initi;il using this learning indicate th;lt elfcctivc CALL does ;I of media and approacha which text-based st;lntl-alone packapcs may not schievc.
reading
This paper presents a hypcrmcdia computer assisted from the point of view of its software components and the spccitic I~tngtIi~Se-learning tasks which they enahlc[ 11. An approach based on linguistic technology is pccLlliilriy suitable for the student p~~piIt~~(ion of a university of technology. Non-specialist students read French to get it footing in the Frenchspeaking world of their professional speciality. Most of these students are highly computcrlitcrate(2j. For language-specialist students. CALL forms p:trt of their education in ~~~t~ltilin~lii~~ linguistic engineering; over and above a learning method, it constitutes an object of study and devclopmcnt work[3]. Within this framework, it is appropriate that Icarncrs should make extensive use of electronic linguistic tools for the study of French, at lcast of those tools which are sufliciently simpic and robust to support the acquisition of the foreign language structures adequately. Such tcchnologics have therefore been integrated to support a method of learning which Iargcly involves the exemplification of the linguistic phenomena which are to be studied through guided discovery. The method also subserves a process of induction for the acquisition of the more formal linguistic principles or rules. The French learner’s workbench is based on a hypermedia platform which breaks away from traditional, linear and text-based CALL systems. It allows for a wide range of learning stimuli and supports the learning of new material, as well as practice or testing for remedial purposes. Multimedia also recreates a more genuine linguistic environment and features authentic documents and textual. graphical. or sound documents. Sound is used for linguistic illustrations, for the comparative grammar of spoken vs written language, for the pronunciation of certain words. on their own and in context. It is intcndcd to add sound to local dictionaries and to develop a course in articulatory phonetics. with supporting illustrations and exercises.
The hypermedia CALL system runs on an IBM PC compatible 486SX machine which provides an acceptably fast mode of operation. The multimedia pcriphcrals attached to the machine are a CD-ROM drive, a microphone and a pair of speakers, via a SoundBlaster Pro board. High resolution, 256 colour and black-and-white still images are displayed on a colour monitor by a Tseng ET4000 Turbo MegaVGA video card with 1 Mbyte of memory. 91
The operating environment is based on Microsoft-Windows 3.1. Only limited use is made of the Windows multimedia extensions as the SoundBlaster Pro board editing software (Sound Blaster Pro Voice Editor) was found to be of better quality for the recorded voice. microphone input tiles which we most frequently used. and incurred lower overheads than the built-in Windows multimedia extension, e.g. automatic buffering for playback from hard disk with the editor. stereophonic .WAV files. use of compressed sound liles[J]. Various tools have been added for the acquisition. edition and manipulation of graphics. Guide (below) provides the means of controlling the speed of the succession of displays by sending a simple coded script to the Windows Media Control Interface (MCI). Guide can also play .MMM animation files and there is even a function to create and distribute Guide documents on CDROM. Finally. the French learner’s workbench is connected to a network and communications software enables on-line access (via serial port) to remote software resources. The public domain package Kermit is being used until it is replaced by Telbin, a sophisticated but public domain TCP communications package and terminal emulator (an enhanced version of Telnet) developed by Clarkson University in the U.S.. when an Ethernet network will be in place and all machines will have been equipped with network cards.
The application is based on the hypertext package Guide 3. I, a proprietary product of OWL International Inc.[5]. As is usually the cast with hypcrtcxt, the dcfuult cnvironmcnts have been dcsigncd to support information systems and were not intcndcd as Icnrning mnchincs. Among thcsc, Guide was sclcctcd bccausc it came with its own programming lungwgc. Logiix. which allows for a cornplctc customization. of the cnvironmcnt to suit ;I specific application, hcrc for ;III educational purpose. A well-known drawback of hypcrtcxt is the case with which the user can bccomc lost in “hyporspncc”. Dctrimcntal as this may bc in an information system, it bccomcs ;I serious problem in a Icarning contcxt[b]. Ellvrts to turn an information system into an clrcctivc Icarning tool thcrcforc conccntratcd on such issues as navigation within the system. In CALL whcrc the learning proccdurcs and the courseware arc to bc used by any Icarncr, irrcspcctivc of their personal Icnrning needs or style. some compromise had to bc rcachod bctwccn highly dircctivc learning paths and complctc user freedom within the courscwarc. On the one hand. the abscncc of any constraints would cquatc with presenting learners with a grammar book, ;I dictionary and some excrciscs, and hoping that they can learn enough from the mistakes they make. On the other, a strict succession of screen displays inhibits all personal enquiry and fosters a passive attitude to Icarning[7]. The interface developed and the organization of the documents have been designed to support teaching strategies and learning scenarios which have, to a large extent, been prc-detcrmincd but still remain tlexible enough to allow for individual enquiry and multiple learning styles. It is unclear yet whether mixing learning approaches to suit diKcring learning styles may prove educationally enriching for all learners, as it reinforces acquisitions made in any idiosyncratic style. When the learners’ bchaviour has been thoroughly studied and their resulting knowledge gain has been evaluated. the interface will be developed into ;I fully-flcdgcd learning management subsystem. An on-line help system comprises two tutorials, one on how to use Windows and one on the application. in the form of an example of II short lesson navigating from core documents to reference material, exercises and available tools. A Session button gives information about the date. the time of day, the title of the current course unit, the title of the document which should be studied that week, the title of the current document. the total number of pages in the current document, the current page number, the list of the documents which have been opcncd and those which have not, the list of the excrciscs which have been attempted and those which have not. The system keeps track of such records for all learners who are known individually. Guide, with Logiix, was also chosen for its capability to seamlessly integrate misccllancous software packages as subprocesses whether these are hosted locally on the PC hard disk or remotely. This enables a highly modular structure where application components can be added, modified or removed.
A French Icclrncr‘s uorkbcnch
90
Etude dss temva du past6
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pscr-group Iwrning and collaborative projects JITOL. ECOLE, COLEARN,
mail systcni
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em.
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Icarning ancl f’ound that. properly orgunizcd. group-b;tscCi Icarning can bc Learning shown to have ii positive impact on st~~dcnts’ acquisition of subject-spwific knowlcdgc. to lvork as ;I constructive nmnbcr of a group is ;I key transfcrablc skill--one which is gaining
supportccl collaborative
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array ol’sofl~varc n~o~l~~lcs and packa~cs. Sornc have olhcrs arc comnicrcial or sharcwrc.
in the public domain
constitute
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Although intcndctl li>r fxiclising linguists. thcsc tools through appropriate teaching stratqics. The tools comprise tcrrninologics. thesauri ant1 lists. stored locally or rcnwtcly
in three
been tlcvclopd packages. The
catqorics.
TOOLS arc iiscd as lan~ii;igc-tca~lling tools bilingual and monolin_cu:ll dictionark on CD-ROMs in the main library of
the university[lO]. a spelling and grammar checker for both languages (Grammatik 5)[11]. simple parsers returning linguistic statistical data to be used for stylistic analysis and writing exercises, and a sophisticated concordancer (TACT) which operates both on English and French. together with a growing collection of text corpora. Commercial lexicographical and terminological resources are used within the constraints of their own software. e.g. various Larousse dictionaries. In-house lexicographical collecting focuses on lexica which lie outside the scope of traditional dictionaries: words for new realia. e.g. “Minitel”, words to create or translate new concepts. e.g. “text-parsing” or “twonking”. Franglish and orthodox words (as published freely by the Commissariat G&&al de ia Langue Francaise). e.g. “butteur” for “bulldozer”, idioms. especially euphemistic denominations, e.g. “technicien de surface” or contextual modulations (“rien i cirer”), slang. jargon. idiolectic lexica, false friends, proverbs and set expressions. etc. These have been mounted on the old British term-bank prototype, developed in our department some years ago for a CEC-funded project[ 12,131. Among the commercial packages there features a dictionary which provides an exceptionally rich environment for the study of the French lexicon: Dicologique. Dicologiqlrr Dicologiquc calls itself a dictionary. but it does not contain standard definitions and has no fixed entries. It contains words and phrases (105,000. 50% more than the Grand Robert or the Laroussc) and 20.000 “notions”. e.g. “>illusion de la raison” (illusion of reason). “>place de ce qui occupe une position” (location of something which occupies a place). “>fort physiqucment” (PhySiCillly strong). “>mais” (but), etc. Dicologiquc makes full USC of the capabilities of the clcctronic medium and of the concepts of invcrtcd files and hypcrtcxt to structure information hierarchically as in a classification, and in combination as in a thesaurus. Words, phrases and notions can cntcr into i\ number of sumantic relations which enable navigation through the data. When looked up using the function Rcchcrchc. each entry is displayed and “dclincd” by what is known as a “quasi-dtlinition”. A quasi-dclinition is not an explanatory text, with quotations, idiomatic phrases, indications of etymology, phonetic translitcralions, clc.. it is a set of notions, words and phrases which marks the position of an entry within a semantic field and indicates its semantic relations with cognate entries. From its position and the semantic relations, one infers the meaning of the entry[l4]. This approach to defining a word or a term is useful in teaching, to introduce the concept of semantic field and to build organized collections. It can also support the introduction of the semantic relations which underlie glossaries or terminologies, various metalinguistic concepts, e.g. gloss, paraphrase, periphrasis, a~ well as the various types of dictionaries and their usage. Dicologique uses six types of semantic relations: liste. classc, description or caractCristiquc, termes liCs (related terms), tht:me, with an indication of the number of items which arc linked by any such relation. These are loosely based on the standard relations found in classifications and thesauri, although in Dicologique they arc more specific and the network therefore richer. Lisrc. Listes link the words which share a given scmunteme. “Fcuille” has part of its meaning in common with “foliation”, “fane”, etc. The terms composing a list arc always of the same grammatical category as the entry, i.c. verbs. nouns, adjectives or adverbs. Listcs contain synonyms or near-synonyms and antonyms, but the longer the list, the less perfect they arc likely to be. Some linguistically impcrfcct terms, howcvcr, arc usage or documentary synonyms, which give an interesting indication of the preferred term in a particular environment. The listc-notion encompasses some aspects of the relation of cquivalcncc in a traditional thesaurus-for “a”, use term “b” or term “c” is used for “d”-as it groups together scattered terms on the basis of their linguistic USagC. Listcs arc useful writing aids to avoid repetitions. to find the precise word or, with the help of an equivalent French term. the translation of concepts already known in a foreign language. They help to spot false friends among semantically cognate terms. They support the study of lexical semantics with the description of the words in terms of their relations with other words within a subject field, while developing the awareness of the groupings dictated by usage in a foreign language. They are also a good vehicle for the study of the morphology and syntax of the words,
A French bmcr’s
=
101
workbench
DICOLOGIOUE
3echerche
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reQu@te
aklal.
fin
2
feullle
2 : feuille. >page (liste)
N C fem. sing. [s mobs)
3 : feuille. N C fem. sing. >lame (liste) >m&al (termes lies)
(22 mots) (44 mots)
4 : feullle. N C fem. slng. >autre ornement (classe)
(82 mots)
5 : feulll& AdjecWVx >garnl de feullles (plante) (liste)
(3 mots)
6 : feullle. Xechnlque
N C fem. slng. plcturale (classe)
(32 mots)
7 : feullle. N C fem. sing. >symbole chretlen (classe)
[63 mots)
LOO-IONS feullle a fendre Lot NomlnaJe volr la feullle a I’envers Lot VerbalelArg. feullle allmentalre Lot Nomlnale feullle d’acanthe Lot Nomlnale feullle de thou Lot NomlnafelFam. feullle de papler Lot Nomlnale Lot Nomlnale feullle de presence feullle de route Lot NomlnaJe feullle de sauge Lot NomlnalelDld. feullle de vene Lot Nomlnale Lot NomlnalelDld. dorure A la feullle feullle volante Lot Nomlnale wllle
: 38
Fig. 2. Screen dump illusrr;tring Dicologique.
compounds. in relation to their meaning and for exercises in lexicographical information, e.g. definitions, exumplcs, rcgistcrs, translations, etc. Clcrsse. Classe denotes hierarchical relations. from the gcncric to the partitive, of realia. “Feuillc” is one elcmcnt of a class of 82 architectural ornaments. As a dictionary. Dicologique dcscribcs words; as ;I mix bctwcen classification and thesaurus. it dcscribcs cxtralinguistic objects and realities. Classes establish relations between terms which belong to the same domain of activity or knowledge. e.g. “>sciencc m6dicalc (classe) (I25 mots)“. They arc used for relations of location in time or for social and administrative hicrarchics and only cvcr contain nouns or noun-phrases. Through classc-notions. it is easy to introduce the principle of classification for purposes of defining and structuring the contest in which the language opcratcs, its associated sublanguage and text type(s). As they group together different terms denoting varying degrees of precision in their description. they help learners refine the accuracy of their linguistic expression, from especially
CAE 23:1/2-H
102
MARIEHAYET
the broader term to the narrower term. This structuring of the lexicon is also highly appropriate for advanced or corporate training. as learners with a good knowledge of a domain can find translations for unknown words easily[ 151. Description. Description is another type of partitive relation, denoting the physical or the conceptual components of the entry. “Feuille” is a physical part of a tree. Terms associated with this relation will therefore be nouns or noun-phrases. As well as providing anatomically organized lists of vocabulary, the semantic relation “description” provides a good framework to learn about metonymy and other related stylistic figures[ 16). Curuck-isfique. Caractlristiques state a property of the entry and are mostly adjectives. ‘Vert’ (green) is a caracteristique of “feuille”. Most caracttristiques denote colour. shape or size. and form groupings according to this characteristic. Caracttristiques-notions can be used to generate clues for cross\vord puzzles or riddles like: “What is red and has a stone?” Answer: a list of red fruit comprising a variety of cherries and plums. Puerile as they may seem. such riddles oblige the learners to think about cognate words, as all elements of the riddle may not appear directly as an entry. They must also interpret the riddle correctly to find the correct French equivalent words (stone j kernel). then cross t\vo or more sets of terms possessing either quality to select those which have both (function “rcQu~te” ). Difficulty in the interpretation of the main predicate can be increased, e.g. “What is red or green and grows in kitchen gardens ?” (answer: pepper); “Qui n’est pas en colCrc mais voit rouge et mcmc vert‘?” (Who is not angry but sees red. and green to boot’?) (answer: a daltonian). Tcrnrcs lib. Termcs Ii& (rclatcd terms) contain various kinds of words or phrases. &longing to ill1 grilll~nliitiCill catcgorics. e.g. adjective corresponding to il noun entry, il property of that entry, the name of a scicncc, a profession. etc. “Fcuillc” meaning “sheet” (e.g. of “metal”) is rclatcd to 44 words denoting characteristics of metal, tools and tcchniqucs of metalwork. This semantic relation is useful to construct banks of terms for technical languages. The “tcrmcs Ii&” relation can cxtcnd the Icxicographical study conducted with the “listc” relation to the study of terms within a subject licld. with the addition of further information. c.g. dclinitions for univocal terms, usage contexts discovcrcd in Corporil, qllillity indication, i.e. slandarcl, prcfurcd, nonprcfcrrcd,
foreign
Iiln~llilgC
CcllliVillCntS.
ok:.
T/M%I~~.ThL:mcs
show most clearly the way in which knowlcdgc in Dicologiquc has been structured. In “fcuille dc la pIante (thcmc)” appear “chose en formc dc fcuillc (classc) (6 words)” (things in the shape of a leaf), “qualificatif de la formc dcs feuillcs (liste) (66 words)“, etc. Thtimcnotions are hypcrtcxt nodes which can bc navigated downwards (fonction contenu) and up\vards (fonction remontCe)[ 171. “Feuille” is embedded in a higher notion “description dcs plantes (th2mc) (5080 words)“. itself embedded in “regne vt:gGtal (thcmc) (45260 words)“. The higher in the hierarchy one is, the more “tht:mcs” there are, relative to other relations. In teaching, the th2mcrelation is used for indexing applications and in LSP for the passage between word and descriptor, or term. Spccinljlrnc,tions. A host of functionalitics based on words as strings of characters csisIs in Dicologique. When looking something up which does not feature in the dictionary. the system displays morphologically close words (“mots SC rapprochant de X”). The function Musclucs Hhich allows wild cards to replace one or several characters in a word helps with crosswords or the search of some affixes to study a particular type of word formation or to find rhymes. or to teach Verlan! The function Anagrammes. which also gives palindromes. can be used for games with young or inexperienced learners. Finally, the function reQuttc enables a user to mark certain words or groups of words and to subsequently apply Roolcan operations on thcsc groups. The electronic medium offers iI good opportunity to revisit the problem of vocabulary acquisition. Complementary clcctronic tools can be used to make notes and build up specific Icxicographical resources, e.g. simple text editors todraft word lists. datilbases to structure iind rctricve Icxicographical information sclcctivcly, term-bank systems containing very thorough linguistic descriptions. and concordancers for the contextualization of lexicographical and terminological data[ IS]. TACT
TACT is a text-retrieval program which is being developed at the University of Toronto. directly available by anonymous ftp. TACT gives much information about a text corpus:
It is total
A
French Irurnsr‘s workbench
103
number of words, number of different words with their frequencies and a distribution graph showing the number of occurrences for each 10% of the text. their location in the text by chapter. section, paragraph. act or scene. etc.. the context in which they occur. and their direct collocations within five words on either side of the word. TACT also calculates the probability of collocations. the number of such collocations one can expect to find in a given text and the standard deviation. Collocations can be extended to simultaneous occurrences of two or more words within a predetermined unit, a paragraph. for instance. TACT also allows complex input requests, using a sequence of m&a-characters and letters to describe a word. phrase or sequence. for more powerful retrieval. Using such regular expressions, one can search for any structure, from morphemes to whole phrases or even patterns, taking word positions as a basis. Regular expressions can be stored. modified and subsequently reused. In CALL, TACT is used in various ways[l9]: as an expansion of Dicologique for keywords in context (KWIC) and the selection of terms[30]. to define and describe the text-types in which they appear; in morphosyntax and text grammar (see the next section Mili-Melo) to study patterns and linguistic phenomena as they occur in various corpora. It has a context-sensitive help system and allows the user complete control over the way results are displayed. Otltcr supporlirtg
tool.~
Other software packages support the work of a practising linguistic tools specifically. They comprise:
linguist
and a learner
without
being
an clcctronic notebook with a facility to import certain matcrinl by direct cutting and pasting (other material has been made read-only to discourage cxcuss printing) a simple datilbilsc USC~ to gcIlcr:itc forms or to manage information: and misccllancous utilities like an impcrinl-metric convcrtcr for USC in iI lesson about the languilgc of basic scicncc and for another about clothing and tcxtilcs. Indeed, almost
:111y
clcctronic
tool can bc of
USC its il
Icarning aid.
The course units contain embccldcd stilncf:ld CALL cxcrciscs mounted on various authoring systems, for practice and testing of rules and patterns but also to support the Icurning process itself. It is an important feature that all cxercisc software can be used for any of these three purposes. A question is not always asked to check whether somebody knows something, it can also act as a formative devicc[Zl]. The cxcrcisc in Fig. I asks sequences of questions about the markers of t?noncintion, aspects, and tcnscs ivhich have been dcsigncd to help learners ask themselves the appropriate questions bcforc deciding on the USCof it linguistic tense and translating the contextualized English form into French. The authoring -some
systems used to mount such exercises are:
Logiix scripts, csscntially multiple-choice
qucstionnaircs
which arc launched iIS part
of speed-reading cxcrciscs directly under Guide to ensure faster and timed operation, and to stop Iearncrs from rctricving the answers from the study material by changing application under Windows; -Question MilrkrM taking advantage of the possibility of mixing question typcs[22]: multiple of a stored string of choice. multiple sclcct. all questions based on the comparison
alphanumeric characters with a string given as an answer, gap-filling and some questions allowing free-format ilnswcrs in natural LlllgUilgC; -the Wide packages: Mot pour Mot (rebuilding of texts based on strings of characters). A votrc avis (multiple choice qucstionnaircs). Autrcmcnt Dit (altcrnativc formulations or transformation patterns). A dcmi mot (gap-filling), Tcrme i Terme (pairing up indcpcndcnt elements somehow conncctcd). En tous scns (dictionary-building software with limited space for information about each entry) ct Jcux de Mots (games on the Icxicon). A juste titre
IM
(matching titles with short texts, matching one title from a group with a text which can be progressively revealed); -an
original
tool developed in house: MGM2lo.
In all practice exercises, access to grammar
references (in lessons)
or to dictionaries
(integrated
tools) is possible at any time.
Mdi-Aldo.
M&MOlo
was developed to support
a course in text grammar
and discourse
structures[23] and to be used. to a lesser extent. for some manipulations in morphology. afixes in particular, and sentence grammar. Available authoring packages did not meet the requirement to “explore the interface between syntax and the construction of coherent text”. that is to say to create exercises to manipulate complex syntax or discourse articulations in a wider linguistic context. In software terms. M&Ii-MClo is a fairly simple text manipulation package which performs the deconstruction
and reconstruction
of texts at all levels of linguistic
units: morphemes, words.
phrases, clauses, sentences and paragraphs. It can also handle groups of sentences in keeping with the rhetorical structure of a text, separated by discourse connectors. e.g. “d’abord, en premier lieu. aussi, en revanche. cependant. non seulement . . mais (encore), car, ainsi. d’ailleurs, notamment, d’oti. done”. to quote but one example of each rhetorical category. It is possible to reconstruct any linguistic units into any greater linguistic units, i.e. morphemes can be reconstructed into words or compounds-a central theme in French grammar. especially with “noms compos&“[%] and technical tcrms[75]; words into compounds, clnuses or sentences: phrases into clauses or scntcnccs. sentcnccs into paragraphs or groups of scntcnccs; paragraphs into text. Howcvcr. not all types of reconstruction,
e.g. morphemes or words into text, make much scnsc.
Another useful feature of ML:li-ML:lo rclatcs to the contcxtu~llizntion of linguistic phcnomcna. Thus. when an cxcrcisc requires it. reconstruction and dcconstruction can bc pcrformcd while the context of the piccc of text being worked on is displayed in a window. As the reconstruction progrcsscs. the correctly ordcrcd picccs of text arc added to that context. Two scrollable windows appear on the screen, one which contains the stock of random units and 011~ whcrc reconstruction work takes place, with or without context. If ncccssary. the Icarncr can go back to another window which contains the description of the task and spccilic instructions or help. It is also possible to return
to the original
vcrsiou of the text to bc manipulated if this
has bcsn allowed by the tutor in the setting of the cxcrcisc. Lcarncrs successively sclcct the clcmcnts from the jumbled list to rcconstitutc the original order or. in the cast of mobile olcmcnts, e.g. complctivc ClilUXS, in any correct order. Deconstruction is intcndcd to bc pcrformcd mostly by the tutor but it can also bc initiated by the learner as an exercise to spot structural clemcnts. The dcconstruction into words, scntcnccs or paragraphs is pcrformcd automatically by the software but manual intervention is often required to distinguish. “aujourd’hui”
for instance, bctwccn “c’cst” (it is) or “est-il” (is it) (3 words) and between (today) (I word) or “rouge-gorge” (robin) (I word). Similarly. the dcconstruction
into morphemes. phrases. clauses. and discourse movements is performed by marking the text with jumbling units and upper jumbling boundaries. The marking process is done intcractivcly as it is possible to jumble the marked elements at any time to check whcthcr the marking has been done correctly. In theory, the same text could be marked to bc used for ditTcront purposes but few texts constitute good examples for more than one type of structure. Texts can be written by the tutor or the learner but they arc normally sclccted from corpora held in clcctronic form and retrieved selcctivcly on the basis of their containing a good proportion of a given linguistic structure. Textual structures to bc cxcmplilicd, manipulated and rcflcctcd upon with Mtli-1Melo comprise: -specific word order dillicultics: compounds (nominal or adjectival) and their modifiers (adjective, adverbs. complctivc clauses): pronoun placement, ncgativc. passive and intcrrogativc constructions; some diflicult relative clauses: cleft and pseudo-cleft sentences. dislocated or topicalizcd elements, with the comparison of thcsc structures with their “unmarked” counterparts and, in context. their efTcct on textual cohesion. --complex
syntax
(e.g. hypotaxis.
embedding. co-ordination)
and the recursive
nature of
A French Iramrr’s
workbench
105
syntactic rules (nesting), by gradually increasing the complexity of a core sentence. or by comparing, in their context. sentences in which mobile clauses. e.g. ‘circonstancielles”. adverbial, appositive clauses, appear in ditferent positions. --text cohesion and coherence[26]. e.g. the order of complements or the sequencing of parts of text (sentences and paragraphs in particular), especially when they deviate from the norm to support a particular pragmatic purpose. e.g. to place a greater emphasis on a particular piece of information. or in keeping with a particular pragmatic parameter. e.g. what is already known by an interlocutor: the study of anaphora and cataphora by replacing in the text previously-removed anaphoric or cataphoric elements[77]. or by substituting such elements by self-contained equivalents. and studying the ensuing impact on the overall text structure. Such a tool. which jumbles predefined linguistic units and embeds components within text. is highly versatile but its greatest potential lies with the uses which are devised to develop learners’ awareness of language structures and their sensitivity to language in general. thus supporting the acquisition of various receptive and productive skills[B-301. CONCLUSION
The technology-based approach to CALL which wc have dcscribcd closely integrates linguistic study and electronic linguistic tools to bring about a rctlection on linguistic phcnomcna and linguistic usage. Learning about the tool, what it is intcndcd for. what it dots and what it fails to achicvc implies rcflccting on langungc. The >Ipproi~ch also gives lcarncrs a good grounding in the USC of the information technologies. Co[nputcr-ilssistcd Icarning and clcctronic linguistic tools constantly intcruct, thus incrciIsillg the IcilrntXs’ cxposurc to IilIl~Uil~C illld enhancing their linguistic awarcncss through critical reactions to inevitably impcrfcct tools. AS CALL. the mclhod yields the organizational bcncfits to bc cxpcctcd when computers rcplaoc tutors. iIt ICilSt l0 SOnlC cxtcnt, e.g. indcpcndcnt learning, Sony dcgrcc of open learning. the possibility of absorbing incrcascd student numbers and sonic ncccssary rcmcdial provision at no extra cost in tutor time. AS sol’lwitrL’based teaching slratcgics with mi~nip~~lations, the 111cthod c[lhiInccs the lcnrni~~g process by concentrating 011 active learning for ciich individual lcarncr. 11~ and large, fhc students enjoy CALL but they do not tind it easy. The task remains to design and JCVC~O~ better lOO!S, to dcvisc intcrcsting ways of making USC ofothcr existing tools, in particular perhaps to organize transparent access to bigger Unix systems whcrc fhc sun shines on analyscrs, parsers, taggers. gcncrators, and machine translation software. REFERENCES I.
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