Anne Condamines

Towards an Ergonomic Linguistics. Application to the Design of Controlled Natural Languages

Keywords: Controlled natural language, corpus linguistics, ergonomic linguistics, readability, usability

This article proposes to define a "new" discipline within applied linguistics: ergonomic linguistics. The article addresses issues concerning language use within organisations, focusing on human-oriented Controlled Natural Languages (CNLs) whose role is to limit the problems encountered with natural language, in professional contexts in particular. An ergonomic linguistics approach involves first taking into account real linguistic productions and analysing them with corpus linguistics methods, and then evaluating the efficacy of linguists' recommendations using cognitive evaluation or acceptance tests. Two projects concerning human-oriented CNLs that adopt an ergonomic linguistics approach are presented, the first in the space domain and the second in aeronautics.

INTRODUCTION

Most information in companies and organisations is transmitted between humans via natural language, which is not a perfect vehicle for this purpose mainly because of its intrinsic vagueness and polysemy, even in professional contexts. Despite this fact, few linguists are hired by companies, either because speakers are not aware of the language difficulties, or because the task of making proposals to control language use is entrusted to expert speakers or to psychologists (ergonomists), while linguists are rarely involved in such projects. A possible reason for this situation is the fact that for several decades, most linguists were mainly interested in generativist approaches and the notion of an "ideal speaker", and consequently seldom explored real uses until at least the 1980s. They were therefore not present within firms, while psychologists have had a place there since the end of the Second World War. While corpus linguistics and sociolinguistics are concerned by real productions, studies conducted on linguistic data from firms are nevertheless relatively scarce. Moreover, when such studies do exist, the aim is rarely interventionist: linguists describe linguistic phenomena but do not propose solutions to the problems that the use of natural language may cause. The notion of "situated linguistics" which we proposed in [START_REF] Condamines | La linguistique appliquée comme science située[END_REF] concerns both real productions and real needs. With "ergonomic linguistics", I propose to take into account specific needs within organisations (principally companies); but, as in ergonomics, I also propose to place the user at the center of the approach, not only as speaker but also as the user of linguistic products. As we will see with the example of human oriented Controlled Natural Languages (CNLs), linguistic recommendations can be built from real productions and/or their efficiency can be evaluated by tests or experimentation. Their usability (a term borrowed from ergonomics) is thus one of the main aspects guiding this research. After presenting CNLs and the questions they raise in Part 2, I describe in Part 3 what I mean by ergonomic linguistics. Part 4 presents two studies on CNLs carried out using an ergonomic linguistics approach.

CONTROLLED NATURAL LANGUAGES AND THEIR USABILITY

This part presents Controlled Natural Languages (CNLs) and more specifically humanoriented CNLs and shows how the notion of usability can be adapted to them.

Controlled Natural Languages (CNLs)

The need to control and plan language is not recent. Some recommendations were made as long ago as the 16th century by F. Bacon, with his notion of 'plain language'. More recently, in 1930, C.K. Ogden published his book Basic English: A general introduction (Basic is a backronym for British, American, Scientific, International, Commercial) [START_REF] Ogden | Basic English: A general introduction[END_REF]. But these initiatives have often been criticized [START_REF] Shubert | The comprehensibility of simplified English in procedures[END_REF]. Flesch for example declared that Basic English, with its 850 words, was neither basic nor English (Flesch, 1944, 343). Few studies have been carried out, however, to evaluate proposals for normalisation, and those that exist, at least for human oriented CNLs are very often based on outdated criteria [START_REF] Dubay | Impact Information[END_REF][START_REF] Flesch | A new readability yardstick[END_REF][START_REF] Klare | A second look at the validity of readability formulas[END_REF][START_REF] Oakland | Language, reading, and readability formulas: Implications for developing and adapting tests[END_REF]. Very few assessments of their efficiency have been conducted.

With regard to the controlled languages themselves, the first one was designed in the Caterpillar firm in 1972. It was followed by many others [START_REF] Kuhn | A survey and classification of controlled natural languages[END_REF]. In all cases, the aim was to control language-related risks [START_REF] Condamines | Variations in terminology: Application to the management of risks related to language use in the workplace[END_REF], especially in domains where human life and/or high costs are concerned. These risks are linked to the use of Natural Language, which is supposed to convey information. However, it is well known that natural language is not dedicated to this role alone. Jakobson, for instance, described six functions of language [START_REF] Jakobson | Essais de linguistique générale : Les fondations du langage[END_REF], and information transmission is just one of these functions. Moreover, the lexicon is sometimes polysemic or vague. As a result, there may be a difference between what the speaker wants to convey and what the interlocutor understands. In order to avoid these intrinsic difficulties, and because the kinds of information that need to be transmitted in most situations within firms are restricted, some CNLs were designed. They were first developed in human written communication: Empirical CNLs were developed with the declared aim of improving the efficacy of written communication, specially of technical information in industry […]. (Ryan, 2018, 294).

The situation has changed, however, and perhaps one of the most important need now is for these CNLs to be managed with computer tools, in particular in Human Computer Interaction. With the development of the semantic web, a lot of work is now being done to develop CNLs as an interface for knowledge bases (Denaux et al., 2017;[START_REF] Fuchs | Attempto Controlled English for knowledge representation[END_REF]. In (Safwat and Brian, 2017), several of these CNLs are evaluated. These CNLs concern a very specific task: Controlled Natural Languages (CNLs) for knowledge creation and management offer an attractive alternative for non-expert users wishing to develop small to medium sized ontologies (Safwat and Brian, 2017, 190).

CNLs are therefore used to allow non-experts to use formalisms that they do not master. During the evaluation, it is therefore this precise task of assisting translation into a formalism that is targeted. Interesting as they may be, these works do not really fall under a linguistic problem because they are very little interested in the meaning and the interaction between humans. In some cases, CNLs "have been one approach to adjusting to the complexities, where the source text is rewritten in some systematic form" (Wyner et al., 2016, 92). But these studies still concern Human-Machine Interfaces and not Human-Human Communication which is what interest us.

It is generally agreed that a CNL is always related to a natural language:

A controlled natural language (CNL) is based on a natural language but includes restrictions on vocabulary, grammar, and/or semantics, in order to reduce or eliminate ambiguity and complexity. (Davis et al., 2017, 9). This necessary relationship comes from the fact that, in most cases, at one point or another in the process, the CNL must be able to be understood by a human. In the case of Human-Human Communication, usability, a concept borrowed from ergonomics, concerns the linguistic form given to the information to be transmitted.

The issue of usability

Usability is a concept that comes directly from ergonomics (or human factors). The ISO definition reads as follows:

Usability: extent to which a system, product or service can be used by specified users to achieve specified goals with effectiveness, efficiency and satisfaction in a specified context of use. (ISO, 2018).

Usability testing is mainly performed on products [START_REF] Dumas | A Practical Guide to Usability Testing[END_REF]. This notion is used in document formatting (Alamargot et al. 2007) and, more recently, for evaluating Human Computer Interaction [START_REF] Hustak | Principles of Usability in Human-Computer Interaction[END_REF][START_REF] Lewis | Practical Speech User Interface Design[END_REF][START_REF] Marquardt | HCI Tools: Strategies and Best Practices for Designing, Evaluating and Sharing Technical HCI Toolkits[END_REF].

But, once again, the choice of linguistic forms only partially requires knowledge of how the language works. With a more linguistic point of view, some sociolinguists use a closely related notion (acceptance) in the domain of language planning: For the sociolinguistic study of language planning, not only the content of planning activities is important, but also the acceptance or rejection of planning efforts. (Haarman, 1990, 105) I propose to use this notion of usability to characterise the readability of a text (or any linguistic production) written following human-oriented CNL recommendations, drawing on the classification developed by Kuhn [START_REF] Kuhn | A survey and classification of controlled natural languages[END_REF]. His PENS classification (precision, expressiveness, naturalness, simplicity) can be used to characterise any CNL. In order to address all these elements, I propose to adopt a two-step method, based on the following definition of the notion of usability applied to texts: A text (or any linguistic form) will be usable if it is close to the spontaneous forms used by speakers and if it is understood rapidly and without error in the context of the reader/listener.

THE ERGONOMIC LINGUISTICS APPROACH

I use the term "ergonomic linguistics" in order to characterise a linguistic approach that takes into account human needs concerning the readability of texts (or oral exchanges) within organisations. Such a notion belongs mainly to linguistics and, in particular, to applied linguistics as defined by AILA (http://www.aila.info/en/): Applied Linguistics is an interdisciplinary field of research and practice dealing with practical problems of language and communication.

But, ergonomic linguistics also draws on ergonomic concepts such as the notion of usability discussed above. As the main aspect in human oriented CNL concerns the transmission of meaning, it seems legitimate that linguistics plays a major role in ergonomic linguistics and that it organizes the contributions of the other disciplines.

Using real linguistic productions to design CNLs

As underlined above, CNLs are always related to a natural language, but in the case of specific technical domains, such as the ones encountered within firms for example, "natural language" means the language used in the firm, or even in a particular department of the firm, since they can be considered to constitute discourse communities [START_REF] Hymes | Models of the Interaction of Language and Social Life[END_REF]. This language is already reduced compared to the natural language spoken by the general population. Approaching the issue from another viewpoint, Harris [START_REF] Harris | Structural linguistics[END_REF]) dubbed these languages "sublanguages".

More or less consciously, human-oriented CNL designers (often domain specialists and expert speakers) generally base the linguistic constraints they propose on their own perception of the way they speak. As a result, a CNL is related to the sublanguage used in a firm or a department and, as Kittredge suggested, it may be designed from it.

One or more related sublanguages may serve as the basis for a controlled language, where standards are introduced to reduce ambiguity, limit complexity, and enforce uniform style (Kittredge, 2003, 430).

With the development of corpus linguistic approaches (especially corpus-driven approaches [START_REF] Tognini-Bonelli | Corpus Linguistics at Work[END_REF][START_REF] Biber | A corpus-driven approach to formulaic language in English[END_REF])), it is now possible to assist in designing humanoriented CNLs through the systematic study of real productions. More precisely, it seems possible to automatically identify phraseologisms, i.e. recurrent lexico-syntactic structures [START_REF] Gries | Phraseology and linguistic theory: A brief survey[END_REF]. These phraseologisms are often formulaic expressions that characterise the sublanguage from which the CNL has to be designed. It should therefore be possible to propose writing recommendations close to the spontaneous productions in use, which are already reduced compared to "natural language". Several tools can be used to extract such phraseologisms but some of them are dedicated to helping linguists to characterise the style of a corpus. It is the case for SDMC (Sequential Data Mining under Constraints), designed by the LINA Lab, the tool we used in our experiment:

[…] The goal is to provide to the linguist experts some prominent, relevant and understandable patterns which can be characteristic of a specific type of text so that these experts can carry out a stylistic analysis based on these patterns. (Quiniou et al. 2012, 167).

To achieve this goal, the authors thus present their method: First, we show that mining sequential patterns of words with gap constraints gives new relevant linguistic patterns with respect to patterns built on n-grams. Then, we investigate how sequential patterns of itemsets can provide more generic linguistic patterns (ibid. 166). This is possible when the corpus is very homogeneous, for example if the texts belong to the same domain and textual genre [START_REF] Bhatia | Analysing genre: Language use in professional settings[END_REF].

Set up tests to evaluate the effect of CNLs on readability

Readability concerns the linguistic dimension of usability. As noted by (O'Brien, 2010):

Little work however has been done on the effects of CL on readability […]. If CL rules do indeed reduce ambiguity, then we might reasonably assume that a secondary consequence is that the readability of the text is improved. (O'Brien, 2010, 143). This is a real problem. However, beyond the controversial first readability formulas [START_REF] Bruce | Why readability formulas fail[END_REF], evaluating readability is feasible. For example, De Jong and Schellens detail fourteen methods to evaluate readability [START_REF] Jong (de | Reader-Focused Text Evaluation. An overview of Goals and Methods[END_REF]. Most of them could be used to evaluate the efficiency of a human-oriented CNL. The same goes for the tests set up to evaluate the CNLs developed in the context of the semantic web. Moreover, new experiments are now available. O'Brien, for example, used experiments from neuroergonomics (eye tracking) in order to evaluate the efficiency of human-oriented CNLs, in this case in a translation task [START_REF] O'brien | Controlled language and readability[END_REF]. Neuroergonomics, which is the application of neuroscience to ergonomics, is probably a promising line of enquiry for the future in the evaluation of CNLs.

Towards an ergonomic linguistics

The term "ergonomic linguistics" is little used. The term "linguistic ergonomics" is used by ergonomists but only for evaluating document design [START_REF] Allwood | Evaluating and improving the usability of a user manual[END_REF]Shackel, 1991) or characterising human-computer interfaces [START_REF] Broadbent | Language and Ergonomics[END_REF][START_REF] Bodart | Using Ergonomic Rules for Evaluation by Linguistic Ergonomic Criteria[END_REF]:

An ergonomic criterion is a well recognized usability dimension in human-computer interaction whose reliability, effectiveness and usability have been assessed. Linguistic ergonomic criteria are criteria that have been linguistically decomposed into subcriteria according to the seven layers of Nielsen's linguistic model of interaction. (Bodart and Vanderdonckt, 1995, 367) The term ergonomic linguistics is preferred here because it situates the approach within the paradigm of linguistics, and specifically of situated linguistics, i.e. an approach that takes into account both real linguistic productions and the needs linked to these productions. Fundamentally, they belong to the general issue of language planning (itself part of situated linguistics) and to the possibility of normalizing language exchanges. This issue has been raised by numerous scholars but rarely dealt with in firms where specific problems are encountered (Condamines et al., 2017).

Nevertheless, at least two linguists use the term ergonomic linguistics. The first is Sager, in the domain of language planning: This opposition between natural and artificially constructed languages offers many interesting research perspectives for ergonomic linguistics, a new field of study associated with language planning and with efficiency of communication. (Sager, 1994, 9) The second one, François Rastier, generally uses the term ergonomie linguistique (linguistic ergonomics) in French, but in a paper written in English one finds ergonomic linguistics:

We can thus assume that the development of ergonomic linguistics depends mainly on the progress achieved in descriptive linguistics and, more specially, descriptive semantics.

[…] This analysis must, however, keep to certain "ecological" conditions, that is the corpus of data to be collected must carefully exclude any artefacts. (Rastier, 1993, 336-338).

In this passage, he claims that real uses need to be taken into account.

While agreeing with these two scholars concerning the need to draw inspiration from ergonomics and the role of real uses and corpus linguistics for exploring them, I think that a third point has to be added: the evaluation of CNLs' usability before they are made available.

So, notion of ergonomic linguistics covers a three-point approach:

-A linguistic approach based on ergonomic notions such as usability; -A method for constructing CNLs based on real productions using corpus linguistic tools; -An evaluation of the CNLs based on (neuro)psycholinguistic experimentation or acceptability tests.

In the last part, two examples of projects that applied an ergonomic linguistics approach are presented.

EXAMPLES OF ERGONOMIC LINGUISTICS PROJECTS

The two studies presented below were conducted as part of PhD research, the first one in the space domain [START_REF] Warnier | Contribution de la linguistique de corpus à la constitution de langues contrôlées pour la rédaction technique[END_REF]) and the second one in the aeronautic domain [START_REF] Jahchan | To what extent does text simplification improve human comprehension ? Cognitive evaluations for the optimization of the airbus cockpit controlled language for future aircraft[END_REF]. These two studies are part of the response to business needs aimed at creating effective linguistic recommendations, that is to say adapted to the transmission of information and really usable.

Creating a new human oriented CNL in order to improve the writing of specifications

This study originated from a request by the Quality Assurance Sub-Directorate of CNES (Centre National d'Études Spatiales), the French Space Agency: how could the writing of space project requirements be improved?

Requirements are defined by IEEE (1990) as "conditions or capabilities that must be met or possessed by a system or system component to satisfy a contract, standard, specification, or other formally imposed document". Most of them are written and organized in documents called requirement specifications, i.e. sets of requirements.

The readability (usability) of specifications plays a crucial role in the building of systems since they are reference documents. At CNES, until recently, no CNL had been defined in order to guide the writing of specifications. Nevertheless, some spontaneous regularities appear, as is always the case when speakers collaborate in the same firm and for the same objectives. These regularities also correspond to sublanguages as we saw above. In such cases, the extra-linguistic situation is accompanied by linguistic regularities. So we can consider that specifications belong to a textual genre. Since there is no CNL at CNES (even if speakers have perhaps been in contact with CNLs in other firms), we can consider that it is possible to develop a CNL from these regularities, i.e. to define a standard for the writing of specifications that is close to the spontaneous productions used at CNES.

As mentioned above, the project was based on the patterns identified using SDMC. Then, from these regularities, we identified linguistic problems (use of pronouns, length of sentences, use of modalization ...) from which we built an assessment test.

The studied corpus was made of requirements extracted from several specifications written (in French) at CNES for two past projects. About 2,500 requirements were extracted for Pleiades and approximately 1,000 for Microscope (a smaller project). After removing all the tables and figures (for which an automatic analysis would be much more difficult), the corpus contained 161,403 words.

Here are some examples of extracted patterns, that we considered as phraseologisms.

-Some of them correspond to terms [START_REF] Warnier | A Methodology for Identifying Terms and Patterns Specific to Requirements as a Textual Genre Using Automated Tools[END_REF].

{PRP_ en}{NOM_ pointage}{ADJ_ inertiel} (preposition/name/adjective), (inertial pointing).

-Other correspond to real sentences templates {sur}{réception}{de}{le}{tc}{le}{lvc}{envoyer} (on receipt of the tc the lvs sends)

In this case, the structure corresponds to a "boiler plate", that is a template in which it is just necessary to indicate the recipient of the tc.

- We then selected patterns corresponding to what seemed most characteristic of the specifications genre. In the case of the patterns identified above, since the concern textual data are requirements, we have identified the need to impose an element (must) but also to modalize this requirement by softening it (can). In other cases, the injunctions are modalized with present or future tenses. For all the linguistic issues identified (expression of injunction, use of conjunctives, use of nominalizations…), we built alternative structures that, in our opinion, conveyed the same meaning. We were also inspired by the recommendations of official CNLs (ASD-STE (AeroSpace and Defence Industries Association of Europe, Simplified Technical English, (ASD-STE, 2007), and INCOSE (International Council on Systems Engineering, (Incose, 2016). Twenty series of sentences were then tested. The survey was conducted through an online questionnaire, using Qualtrics, a tool dedicated to the management of online surveys. Each user was given a definition of what a specification is and was asked to rate the structures from 5 to 1 based on their assessment of the relevance of the structure, for more details see (Warnier and Condamines, 2017a). Eighty-seven persons answered the questionnaire: 48 respondents, accustomed to reading specifications, were from CNES, and 39 were nonexperts (ordinary speakers) [START_REF] Warnier | Improving requirement boilerplates using sequential pattern mining[END_REF].

Here are two examples of the tested series, concerning the alternative linguistic forms of the injunction in a particular statement.

L'opérateur a le choix du mode de saisie : il rentre il peut rentrer/ il doit pouvoir rentrer/ il rentrera/ il pourra rentrer/ il devra pouvoir/ l'adresse directement ou indirectement en précisant le nom d'un label.

(The operator has the choice of input mode: he enters/ he can enter/ he must be able to enter/ he will enter/ he will be able to enter/ he will have to be able to enter/ the address directly or indirectly by specifying the name of a label.) La configuration est/ peut être/ doit pouvoir être/ sera/ pourra être/ modifiée par 2 événements (The configuration is/ can be/ must be able to be/ will be/ will be able to be/ modified by 2 events.) I can't give all the details of the experiments and all the results (see for this [START_REF] Warnier | Contribution de la linguistique de corpus à la constitution de langues contrôlées pour la rédaction technique[END_REF]). In the case of the injunction, both the experts and the existing CNLs prefer the pattern with doit or devoir even if the corpus does not always use it. We can conclude that, for experts, the recommendations have to indicate the necessary to use this modal (or another injunctive form) in order to distinct requirements from information. In this study the two elements of ergonomic linguistics, namely using a corpus-based approach and testing the best structures for conveying information, were both present (Condamines and Warnier, 2017). The study ended with the production of recommendations in the requirement writing at CNES (Ergonomic Controlled Natural Language for Requirement Writing).

Improving readability of the CNL dedicated to alarms in Airbus Cockpits

The second study is included in a project with Airbus Group. It concerned the linguistic form given to alert messages in Airbus cockpits. The current messages (current CNL) were designed in 2003 [START_REF] Spaggiari | A controlled language at Airbus[END_REF]. The alarms are intended to warn the pilot of any type of problem, from the most harmless to the most serious. Depending on the gravity of the situation, (s)he must intervene or not. These forms are syntax poor (for example : Cabin Altitude Regulated to 700 FT). There are two reasons for this. First, there is a size constraint related to the size of the screens. This point should evolve in the new cockpits. Second, the deletion of syntactic elements is very frequent, not only in CNLs but also in specialized discourses [START_REF] Kittredge | Sublanguage: Studies of language in restricted semantic domains[END_REF]. Consciously or not, speakers consider that the extra-linguistic situation plays an important role in the interpretation and compensates for the absence of syntactic elements. The underlying hypothesis is that if the reading time is reduced, the interpretation time is also reduced, which entails cognitive economy. An evaluation of this hypothesis was conducted by Carol Simpson (2016). In a study done in NASA's Ames Research Center, she showed that, in oral alerts, by forming clear unambiguous sentences using the same original keywords, the message was detected more accurately and pilot's reaction times was faster. For instance fuel low was inserted in a sentence in the same order the fuel pressure is low. Response times to sentences were approximatively 1 second shorter than response times to two-word messages. The initial hypothesis concerning the efficiency of short messages was invalidated, at least in this study. The same hypothesis concerning form and length of the messages of written alarms (contrary to Simpson's study on oral messages), was evaluated by Jahchan in her PhD thesis [START_REF] Jahchan | To what extend does text simplification entail a more optimized comprehension in human-oriented CNLs[END_REF], [START_REF] Jahchan | To what extent does text simplification improve human comprehension ? Cognitive evaluations for the optimization of the airbus cockpit controlled language for future aircraft[END_REF]. She proposed to start from existing messages and to evaluate a more natural CNL, i.e messages enriched with syntactic or lexical elements. Generally speaking, the experiments performed were based on calculating the reaction time of the subjects after the presentation of messages with vs without all the syntactic or lexical elements (more "natural" messages), in different situations. It is not possible to detail these studies (see [START_REF] Jahchan | To what extent does text simplification improve human comprehension ? Cognitive evaluations for the optimization of the airbus cockpit controlled language for future aircraft[END_REF] but I present one of them hereafter. It consists in the presentation of a message and then a photo that is either congruent or incongruent with the message. The message is either consistent with the CNL or closer to natural language. In the case of congruence between the message and the photo, response times were compared between controlled messages and more 'natural' messages. The messages were built from the structures of real messages but the lexicon was modified since the experiments were carried out both with expert speakers and non-expert speakers. We wanted to assess whether the experts (pilots), used to the current CNL, had a different appreciation of the form of the message. Note that in this case, we start from the existing CNL and not from what could have been said spontaneously before the design of this CNL. This is due to the fact that the pilots are accustomed to these messages and that it is not a question of creating a new CNL but of seeing how to modify the existing one to make it more efficient. The constructed messages respected exactly the form of alarm messages as in the example below. For example, [Adj Noun Num Noun Noun Noun available] corresponds to the structure of certain messages in the cockpit (L TK 1700 KG MIN AVAIL). N.Jahchan constructed examples with the same syntactic structure, for example Empty house 2 metal basement doors available or, as in the example below, Local bakery 2 vanilla wedding cakes available. The difficulty was that the examples had to be able to correspond to representations on a photo. More natural (more syntactically rich) messages were also randomly proposed, for example in the chosen example: There are 2 vanilla wedding cakes available in local bakery [There are Num Noun Noun Noun available in Adj Noun]. Figure 1 shows the two messages randomly proposed and the photo presented following each message.

[Here is the figure]

Local bakery 2 vanilla wedding cakes available

Vs There are 2 vanilla wedding cakes available in local bakery Figure 1: Example of the two messages randomly proposed and the photo presented after them Seventy-two persons participated in this experiment. Thirteen of them were native speakers. The English level of the others was variable. Competences concerning the CNL used in Airbus cockpits were also different. As noted above, the test consisted in the presentation of photos that were either congruent or incongruent with the message (randomly proposed with or without syntactic elements). Participants responded using a joystick. In the case of a good response (i.e, with respect to the congruence between the message and the photo, as in the case presented above since only two of the four cakes are (or appear to be) vanilla), the response time between the end of the reading and the action on the joystick was calculated. Full details concerning the way the data were constructed, the way the experiment was elaborated and the way the results were interpreted are available in [START_REF] Jahchan | To what extent does text simplification improve human comprehension ? Cognitive evaluations for the optimization of the airbus cockpit controlled language for future aircraft[END_REF]. To summarize the results obtained, we can say that, in all cases, the results were not better with the CNL (briefer messages) than with more natural messages. Moreover, in the event of ambiguity, more natural language messages can be processed more rapidly. This study presents an interest both for the Airbus Company (which will be able to take these results into account to define future cockpits)and for linguistics. From a situated linguistics perspective, it shows that syntactic information may play a crucial role, particularly in critical situations (another experiment was done to simulate a critical situation). This is an important point showing the importance of an ergonomic linguistics approach in firms.

CONCLUSION

In this article, I proposed to go towards a new discipline, relating to applied linguistics, but that uses concepts and methods from ergonomics such as usability: ergonomic linguistics. It is mainly concerned with how linguists can intervene within organisations where natural language is used in order to convey information between humans. In order to normalize linguistic uses, ergonomic linguistics proposes to take into account users' needs and the usability or acceptance of the linguistic norms proposed. First, real productions have to be taken into account and studied with corpus linguistic methods. Then, when linguistic norms have been developed from these real uses, their efficiency has to be assessed with experiments or acceptance tests. Two studies have been presented dealing with Controlled Natural Language issues and the way ergonomic linguistics may help to design and evaluate texts written with CNL recommendations. Such an approach requires skills in corpus linguistics (and more generally in tool-assisted methods) and in cognitive experimentation. A single researcher seldom possesses all these skills, especially as research in these areas is progressing rapidly thanks to deep learning and to neuroergonomics. Future research will no doubt take place in multidisciplinary teams but hopefully we will better understand eventually what it means "to make sense".


Anne Condamines
email: anne.condamines@univ-tlse2.fr

Aurélie Picton
email: aurelie.picton@unige.ch

Textual Terminology: Origins, Principles and New Challenges

Keywords: Textual Terminology, specialised corpora, methodological principles, challenges, distributional analysis, knowledge-rich contexts, applied needs

This chapter deals with Textual Terminology, an approach that emerged in the early 1990s along with other approaches presented in this book. One of its main characteristics is that it arose from an encounter between related disciplines that focused on the use of text mining (Natural Language Processing) and modeling tools (Knowledge Engineering and Artificial Intelligence). These disciplines have worked together to consider the new needs related to terminology, and their evolution, the tools and the diversification of needs have resulted in significant methodological developments in Textual Terminology. This chapter traces these developments and discuss the original bases, principles, challenges and perspectives that constitute Textual Terminology.

Introduction

This chapter is a reflection on Textual Terminology from its emergence in the 1990s to the new issues it faces today. The second section of the chapter identifies the main elements that contributed to its foundation in the 1990s. Special importance is placed on the tools and collaboration with research in Natural Language Processing (NLP), Knowledge Engineering (KE) and Artificial Intelligence (AI). The third part focuses on the methodology of Textual Terminology as well as the systematic framework for tool-assisted methods aimed at analysing specialised corpora. In particular, we discuss the place of users in the overall analysis and stress the need for collaboration at each stage of the process. The fourth part highlights the modalities and possibilities that Textual Terminology should encompass, in the light of the parallel evolution of corpora, needs and approaches in Textual Terminology, NLP, and KE/AI. This perspective enables us to better situate Textual Terminology in the context of current approaches and to propose research issues to be addressed in the short and medium term.

The origins of Textual Terminology

Textual Terminology arose from a combination of the following: (i) new needs in relation to terminology; (ii) the development of corpus linguistics; and (iii) the encounter between Natural Language Processing and Knowledge Engineering/Artificial Intelligence.

New needs in relation to terminology

Until the 1990s, terminology studies were mainly related to translation needs. However, because of the need to manage and analyse digital documents and resources with NLP tools, terminology came into the spotlight as a resource for managing electronic documentation.

Tasks such as document storage, information search and retrieval, and even translation all required up-to-date terminologies adapted to real-world use.

The structure of databases had to be adapted to new tools, and their entries also needed optimisation. For example, in a study conducted in 1992 at Matra Marconi Space (an aerospace company, now Airbus Defence and Space), only 15% of the terms found in the documentation in French concerning test/simulation benches were recorded in the official terminology in the "Vocabulaire des sciences et techniques spatiales" 1 .

The need was then no longer limited to the creation of terminology standards. What was required was the creation of repositories that corresponded to actual term usage and were usable by tools. It was thus necessary to base the structure of terminology repositories on the analysis of digital corpora that were beginning to be available in large quantities in companies.

However, in the early 1990s, most terminology studies were carried out within the framework of the General Theory of Terminology (GTT), developed in the 1930s by Eugen Wüster (see chapters by Candel and Humbley in this volume). Wüster was a Viennese engineer, who advocated a prescriptive view of terminology with the objective of guaranteeing non-ambiguous communication in science and technology and especially within companies, both in the same language as well as different ones. Hence, one of the cornerstones of the GTT was biunivocity: one term <> one concept. Polysemy was thus not acceptable. Although Wüster was familiar with the work of linguists such as Saussure, he believed that it was necessary to separate terminology from discursive variations. One explanation for this is that Wüster was firmly convinced that concepts were the starting point for terminology [START_REF] Wüster | Introduction to the General Theory of Terminology and Terminological Lexicography[END_REF][START_REF] Wüster | Introduction to the General Theory of Terminology and Terminological Lexicography[END_REF].

The strictly onomasiological approach of the GTT was harshly criticized by corpus linguists, especially when electronic corpora (internet and intranet among others) became widely available.

Corpus linguistics and corpus-based Terminology

1 Edited by the DGLFLF (Délégation Générale à la Langue Française et aux Langues de France, General Delegation for the French Language) https://www.culture.gouv.fr/Sites-thematiques/Langue-francaise-etlangues-de-France/La-DGLFLF. Accessed October 27, 2020.

Corpus linguistics began to appear before the 1990s. One of its main objectives was the construction of general language dictionaries. Specialised texts were thus excluded as the corpora had to represent the general language. According to Sinclair (2005, 14), "[a]nother factor that affects balance is the degree of specialisation of the text, because a specialised text in a general corpus can give the impression of imbalance". However, the methods developed to study general corpora were subsequently used for specialised corpora: "[c]orpus-based terminology can best be characterized as a working method which explores a collection of domain-specific language material (corpus) to investigate terminological issues" (Gamper and Stock 1998, 149). This is the case for definitional contexts or conceptual relationship markers (Auger and Barrière 2008;[START_REF] L'homme | Terminological Relationships and Corpus-based Methods for Discovering them: An Assessment for Terminographers[END_REF]. And indeed, part of corpus linguistics was quickly devoted to the use of specialised corpora to build terminologies. The use of corpora led to a rapprochement between terminologists and linguists. For terminologists, it resulted in the discovery of term variation and the need to propose theories that were more in line with these variations. Sager (1990, 58) writes: "[t]he increasing tendency to analyse terminology in its communicative, i.e., linguistic context, leads to a number of new theoretical assumptions and also new methods of compilation and representation".

One of the main aspects investigated concerned the social dimension of terminology (see chapter by Gaudin and Delavigne in this volume). The study of specialised texts means that it is necessary to adopt a situated vision of how language functions, in other words, to consider text production and its context. For this reason, terminology could no longer be based on a purely onomasiological approach which mainly focused on the creation of norms.

Researchers throughout the world began to criticize Wüster's vision and decided to study real term usage, (e.g., [START_REF] Condamines | Terminology: New Needs, New Perspectives[END_REF][START_REF] Gaudin | Pour une socioterminologie -Des problèmes sémantiques aux pratiques institutionnelles[END_REF]Gaudin , 2003;;[START_REF] Temmerman | Towards New Ways of Terminological Description. The Sociocognitive Approach[END_REF]. For example, Cabré (1999, 114) argued that "Terminology, as both a discipline and an activity, but especially as a discipline, needs a new orientation which stresses its social and pragmatic aspects."

In Textual Terminology, as for the French semanticist [START_REF] Rastier | On Signs and Texts: Cognitive Science Faces Interpretation[END_REF], it is neither the term nor the corpus that constitutes the most significant element but rather the text (see Chapter by Pecman in this volume). It is mostly at this level that the communicative situation, the field and especially genre should be considered. If texts are collected to form a study corpus, it is because they belong to genres that are relevant to the purpose of the study. In such an approach, the interpretation of language data needs to take the nature of the texts and the objective of the study, into consideration.

In addition to the crucial role of texts, the other defining characteristic of Textual Terminology is the use of NLP and Knowledge Engineering to analyse corpora and structure terminologies.

Terminology, NLP and Knowledge Engineering

The 1980s witnessed the approximation of terminology and knowledge (or Artificial Intelligence). In 1985, Nedobity wrote a paper entitled "Terminology and Artificial Intelligence". In 1987, the first Terminology and Knowledge Engineering (TKE) conference took place at the University of Trier. In 1991, Skuce and Meyer wrote an article entitled "Terminology and Knowledge Engineering: Exploring a Symbiotic Relationship" (1991, 29/1). In 1993, the Terminology and Artificial Intelligence group in France brought together computer scientists, linguists and terminologists, who played a major role in the development of Textual Terminology. These few examples show that, as early as the end of the 1980s, the close relationship between the issues of terminology and those of Knowledge Engineering (or Artificial Intelligence) was perceived.

Clearly, these disciplines were all united by a shared interest in representing knowledge in the form of networks of terms/concepts. Knowledge engineers realized that they could use existing terminology databases and/or methods inspired by corpus-based terminology (Aussenac-Gilles, [START_REF] Nathalie | How can Knowledge Acquisition Benefit from Terminology?[END_REF]Aussenac-Gilles, Biélow and Szulman 2000;[START_REF] Buitelaar | Ontology Learning and Population: Bridging the Gap between Text and Knowledge[END_REF], and terminologists realized that they could use the knowledge representation modes of knowledge engineering to help build coherent and comprehensive resources [START_REF] Meyer | Towards A New Generation of Terminological Resources: An Experiment In Building A Terminological Knowledge Base[END_REF]). In fact, the representation of knowledge in the form of a graph was perfectly adapted to natural language processing tools.

With the development of ontologies, the relationship with terminology became even clearer, which led to the emergence of termino-ontological resources [START_REF] Bourigault | Construction de ressources terminologiques ou ontologiques à partir de textes : un cadre unificateur pour trois études de cas[END_REF] or termontology resources [START_REF] Kerremans | Construing Domain Knowledge via Terminological Understanding[END_REF]. In regard to terminology, the evolution of databases enriched with insights from Knowledge Engineering tools gave rise to terminology knowledge bases. The main objective of this representation model is to replace, insofar as possible, a definition by a network of conceptual relationships [START_REF] Condamines | Terminological Knowledge Bases[END_REF].

The need to systematically build a network of concepts from corpora led to the development of a significant number of studies and tools based on the notion of markers of conceptual relations (and knowledge-rich contexts [START_REF] Meyer | Extracting Knowledge-Rich Contexts for Terminography : A Conceptual and Methodological Framework[END_REF]Marshman in this volume)).

Based on this same objective, NLP and Terminology pursued their own research agendas. For NLP, it was a question of data extraction and retrieval though focused on the identification of conceptual relations (Bourigault, L'Homme and Jacquemin 2001). For terminology, it was a question of seeing how to adapt lexicological studies on the construction of local grammars with a view to identifying conceptual relations in corpora. In relation to the phenomena observed in specialised corpora, various studies focused on the role of textual genre and the effectiveness of markers [START_REF] Condamines | Corpus Analysis and Conceptual Relation Patterns[END_REF][START_REF] Condamines | Taking Genre into Account for Analyzing Conceptual Relation Patterns[END_REF][START_REF] Marshman | Portability of Cause-effect Relation Markers across Specialised Domains and Text genres: A Comparative Evaluation[END_REF].

The Principles of Textual Terminology Today

This section describes the methodological principles underlying the approach of Textual Terminology, the stages involved as well as the main protagonists.

Steps in a Textual Terminology analysis

In Textual Terminology, linguistic analysis typically takes place in six stages: (i) definition of the (theoretical or applied) purpose of the analysis; (ii) compilation of the corpus; (iii) selection of tools; (iv) use of tools; (v) validation of the results; (vi) integration of the results into linguistic paradigms.

Purpose of the analysis

The purpose of the analysis may stem from an extra-academic need or from scientific inquiry, often triggered by an extra-academic demand. In any case, especially when it concerns an extra-academic demand, the request needs to be 'translated' into a linguistic question. This makes it possible, on the one hand, to use knowledge already acquired in linguistics and, on the other hand, to integrate the results acquired into the linguistic paradigm of choice once the analysis has been completed. It is then a question of understanding how studies that are doubly situated (by the objective and by the nature of the corpus studied) contribute to the advancement of knowledge.

For example, a few years ago, to celebrate the 50th anniversary of its creation, the CNES (Centre National d'Etudes Spatiales, the French National Space Agency) asked us to carry out a research project on how space, as a body of knowledge, real or imaginary, was more or less consciously disseminated to the general public. We proposed to address this question from the perspective of determinologisation, i.e., the movement of terms from specialised language into general language [START_REF] Meyer | When Terms Move into Our Everyday Lives: An Overview of De-terminologization[END_REF]. This seemed a good indicator of the penetration of the domain-specific knowledge into general knowledge.

A thesis is in progress on this questionpractically unexplored in linguistics to datein the field of particle physics (Humbert-Droz in progress).

Compilation of the corpus

At the beginning of corpus linguistics, a corpus was intended to represent a (sample of) language, mainly with a view to building a general language dictionary or even a grammar.

To ensure their representativeness, well-balanced corpora were built to report on a variety of communicative situations, but not specialised communication (see Section 1.2). Even though representativeness might seem easier to achieve in a specialised corpus, the notion of 'domain', does not really simplify the problem of corpus compilation. This notion is generally based on a top-down view. In other words, knowledge is organized a priori into domains within which discourse and terminology are assumed to be structured. This division is already problematic, as knowledge is not naturally discontinuous [START_REF] Rogers | What is a 'Domain' and is this a Useful Question?[END_REF].

Moreover, this pre-established organisation is only partially relevant when it comes to considering a particular need. From the point of view of Textual Terminology, the corpus should be as representative as possible, but this representativeness also encompasses the objective of the study (Humbert-Droz, Picton and Condamines 2019). In the case of the previously mentioned determinologisation study, the question of representativeness also takes into consideration the types of texts through which terms are disseminated. We thus chose to build a corpus composed of four sub-corpora: (i) a corpus presenting projects carried out at CNES; (ii) a corpus of press releases issued by the CNES; (iii) a corpus of press releases containing space terms; (iv) and a corpus from the general press [START_REF] Condamines | Néologie par déterminologisation : méthode de repérage et catégorisation en corpus dans le domaine de la physique des particules[END_REF].

Another important point within Textual Terminology concerns the comparison of subcorpora. Depending on the study to be conducted, the corpus is organised into sub-corpora, which are compared in a way that is relevant to the study. Corpora can thus be organised based on time period, levels of expertise, disciplines, different departments within a company, etc.

One difficulty with building a specialised corpus concerns the availability of the texts and the difficulty of accessing data. There are various reasons for this, some of which are specific to the analysis of language for specific purposes. For instance, as Ghaddar and Langlais (2020, 3600) note, in an industrial context, "privacy issues and the commercial value of financial domain data" make it difficult to access data in specialised fields. [START_REF] Grabar | Annotated Corpus with Clinical Cases in French[END_REF] also discuss individual privacy and data protection issues that limit access to certain kinds of medical texts. Moreover, in order to systematically study the productions with the help of tools, texts must be in written and digital form. This requirement excludes, for example, handwritten notes, radio programs (unless they are transcribed), lectures, or even confidential publications. These constraints can result in rather small corpora and create methodological problems (see for instance [START_REF] Bowker | Working with Specialized Language: a Practical Guide to Using Corpora[END_REF].

Selection of tools

Tools analyse character strings and not words as semantic units. They quantify these strings, find similarities between them, and/or search for predefined strings in a corpus. The objective of NLP researchers is to propose results that could be associated with a certain meaning. In Textual Terminology, the tools are designed for a specific task (i.e., terminology and conceptual relation extractors). They may be more generic (e.g., taggers, concordancers, lexicometric, textometric tools, etc.). It is therefore necessary to decide which tools to choose, depending on the objective of the study.

Use of tools

Since Textual Terminology proposes to enter the corpus via terms, for whatever purpose, a term extractor should be the first tool used. Once the initial list of candidate terms has been established, the next step is to analyse the contexts in which they appear. There are two ways to study such contexts. The first way is to study the meaning based on the distribution of terms in the corpus (bottom-up analysis). The second way is to search term contexts for elements that are known to be relevant to the study (top-down analysis).

Bottom-up analysis;

This type of analysis involves categorizing term contexts and then interpreting them (based on our competence as speakers and linguists). It is based on the wellknown distributional approach developed in two currents, one inspired by sociolinguistics [START_REF] Firth | Papers in Linguistics 1934-1951[END_REF] and the other by a mathematical (and behaviourist) vision of language [START_REF] Harris | Methods in Structural Linguistics[END_REF]. The main idea underlying this approach is that there is a correlation between distributional similarity and meaning similarity. It is thus possible to understand the meaning of a word or group of words by considering all its contexts.

Most analyses in Textual Terminology focus on corpora and the comparison of subcorpora. In such cases, differences in distribution are associated with differences in the communicative situation, prior to the sub-corpus organisation. For example, if the corpus is organised in sub-corpora of experts vs. non-experts (e.g., to study popularisation as in [START_REF] Delavigne | Term Usage and Socioterminological Variation. The Impact of Social and Local Issues on the Movement of Terms[END_REF] or [START_REF] Ledouble | Term Circulation and Conceptual Instability in The Mediation of Science: Binary Framing of the Notions of Biological versus Chemical Pesticides[END_REF]), a difference in term distribution, such as when the term is used in general language, can be interpreted as a sign of meaning variation.

Top down analysis;

In the early days of Textual Terminology, when the main purpose was the construction of terminology networks, research focused on defining and specifying conceptual relation markers or lexico-syntactic elements to which an a priori interpretation is associated. The hypothesis is that these markers, associated with knowledge-rich contexts [START_REF] Meyer | Extracting Knowledge-Rich Contexts for Terminography : A Conceptual and Methodological Framework[END_REF], are less likely to vary from one specialised corpus to another. The choice of appellation is very interesting as it introduces the notion of "wealth of knowledge". This is not only relevant to conceptual relation markers but also to linguistic units of different types, related to different types of knowledge. This means that for each analysis, elements that are directly related to the objective of the study can be defined a priori. The purpose here then is not to build a relational network, but rather to track the presence of elements that indicate a shift in meaning, variation, etc. that speakers are aware of.

For example, if the analysis focuses on the evolution of a term, word, or phrase over a period of time, one can find markers such as, before, [concept name2] formerly called [concept name1] [START_REF] Picton | Diachronie en langue de spécialité. Définition d'une méthode linguistique outillée pour repérer l'évolution des connaissances en corpus. Un exemple appliqué au domaine spatial[END_REF][START_REF] Picton | The Dynamics of Terminology in Short-Term Diachrony: A Proposal for a Corpus-based Methodology to observe Knowledge Evolution[END_REF], that "mark" the presence of relevant information regarding the evolution of a domain or its terminology.

Validation of the results

In Textual Terminology, the results obtained must be validated at many levels. Firstly, results must be acceptable to peer analysts. This implies that the proposed interpretations must be associated with textual contexts that justify them. These interpretative choices can be discussed and justified if necessary.

The second type of validation is the one provided by experts in the field. One of the characteristics of specialised corpus analysis is that the analyst in most cases is not a domain expert and is thus not familiar with the terms or phrasing used by experts. This means that data may be misinterpreted. It is therefore necessary to work closely with experts in the field, who analyse a set of contexts that have been pre-selected either because of their difficulty or importance. Together, they can thus co-construct an interpretation that makes use of linguistic knowledge and knowledge of the field. Validation by experts then often proceeds in a coconstruction process [START_REF] Picton | Diachronie en langue de spécialité. Définition d'une méthode linguistique outillée pour repérer l'évolution des connaissances en corpus. Un exemple appliqué au domaine spatial[END_REF][START_REF] Picton | The Dynamics of Terminology in Short-Term Diachrony: A Proposal for a Corpus-based Methodology to observe Knowledge Evolution[END_REF].

A third type of validation can be performed by end-users, when the project call stems from an applied need. From a strictly lexical point of view, these users are language professionals, such as translators, technical writers, or even computer scientists who need resources to make the tools work. Nevertheless, end-users can also be LSP teachers, nonexperts wishing to access simplified definitions, experts from other disciplines, etc.

Therefore, the results are not only a set of terminology entries. They can also be data interpretations based on the study of terms. The way in which these results are presented may fall under what, in ergonomics, is called 'usability'. This part of the validation can therefore be performed in association with ergonomists, which also situates Textual Terminology within the framework of ergonomic linguistics (see Section 4.1.2.).

Integrating the results into linguistic paradigms

From a theoretical point of view, the consideration of extra-academic needs presents a risk.

Even though the results might fulfil these needs in an ad hoc manner, their contribution to linguistics might be overlooked. On the one hand, the results obtained might not be sufficient to enrich the description of terms and their use. In fact, it goes without saying that if results are not elaborated, they cannot be reused to deal with other similar problems. For instance, in the study of determinologisation, the results obtained are relevant to neology. In the case of popularisation, they pertain to shifts in meaning (e.g., Condamines, Humbert-Droz and Picton, forthcoming or Ledouble 2020). Integrating (and theorising) these results in linguistic paradigms is thus of major importance.

Skills and protagonists

Echoing what has been described above, the following types of protagonist may be involved in this process (Table 1):

 Terminologists-linguists, who organize the work and conduct the analyses. From the perspective of a scientific study, they act as intermediaries between the extra-academic world and linguistic knowledge.

 Domain experts, who help to compile the corpus, validate the interpretation of the results and, in some cases, validate the results within the context of the initial project goals.

 End users, who may work in companies and are often the source of the demand, driven by quality departments or design offices.

 Terminologists-linguists may call upon other types of skill, e.g., those of NLP experts to adapt computer tools to the needs of the analysis.

 Ergonomists and their expertise can be used to characterize the need and evaluate the usability of the proposed results.

End-users

Since the early 1990s, these principles have remained relatively stable. However, several aspects need to be re-examined. On the one hand, a major evolution has taken place in NLP and Knowledge Engineering, thanks to the availability of huge amounts of data on the Internet. On the other, studies of specialised corpora have diversified. The following section describes the evolution of Textual Terminology and the perspectives that have emerged.

Textual Terminology today: Challenges and Perspectives

Over the last thirty years, Textual Terminology has evolved considerably in regard to the following: (i) emergence of applied needs; (ii) development of corpus linguistics; (iii) crossfertilisation with NLP, KE/AI. This section reappraises these three very closely linked areas, as they are today. This sheds light on the future of Textual Terminology and its development in the short and medium term.

New needs and perspectives

New needs in Terminology

Whereas research initially focused on the creation of termino-ontological resources (see Section 1), from the early 2000s onwards, the need to update these resources began to surface (e.g., [START_REF] Condamines | Variation de la terminologie dans le temps : une méthode linguistique pour mesurer l'évolution de la connaissance en corpus[END_REF]. This question led to the consideration of diachronic variation (see Section 2).

From then, new needs gradually emerged, which involved the study of other types of dialogical variation [START_REF] Freixa | Causes of Denominative Variation in Terminology: A Typology Proposal[END_REF]). As explained in section 2.1.1, a project request from the CNES led to the integration of the analysis of diastratic variation. At the same time, another project was initiated by the Geneva Centre for Humanitarian Studies2 . Its objective was to highlight differences in term usage from different perspectives in the field, in English3 [START_REF] Egger | How Do Humanitarian IO Conceptualise the World ? An Interdisciplinary Approach to the Analysis of Concepts Used in Humanitarian Action[END_REF]. Diatopic variation is in this case important. We can also mention a study that we carried out on exobiology (funded by the CNES), an emerging discipline that involves different disciplines. This work shed light on the fact that terminological variation was not only a difficulty for interdisciplinarity, but that it could contribute to identifying the dynamics between multiple points of view regarding a concept [START_REF] Condamines | How Can Linguistics Help to Structure a Multidisciplinary Neo-Domain Such as Exobiology?[END_REF].

In addition to envisaging other types of variation, this led us to reflect on the simultaneous analysis of different kinds of variations. In the case of determinologisation, for example, variation is both the diastratic (movement of terms from specialised texts to general texts) and the diachronic (movement of terms over time). This implies working on fairly complex comparable corpora, which are analysed, based on different types of variation.

In these analyses, the general methodological approach remains the same as the one described in Section 3. However, in addition to the overall difficulty of ensuring the representativeness of the data and their balance in the different sub-corpora (Section 3.1.1.), the consideration of these more complex comparable corpora also highlights the issue of data availability. This question is directly related to the NLP tools and methods that Textual Terminology can use with these corpora (see Section 4.2.).

Towards new kinds of need: Controlled Natural Languages

As a method of systematic linguistic analysis with NLP tools in specialised domains, Textual

Terminology can be implemented in different ways. Its methodology can also be adapted to elements other than lexical units, such as the definition of Controlled Natural Languages (CNLs). CNLs are linguistic recommendations intended to limit risks related to the use of the natural language, particularly in companies and businesses (Ryan 2018, 294). Since this is also one of the functions of terminologies [START_REF] Condamines | Variations in Terminology. Application to the Management of Risks Related to Language Use in the Workplace[END_REF], many CNLs also have a terminology component. Like terminologies before the 90s, they are usually developed by experts in the field, sometimes with the help of translators. This means that CNLs are mainly established by introspection, based on the competence of domain experts.

In another project with the CNES, we were asked to build a CNL for the writing of satellite specifications. We thus compiled a corpus of specifications written without instructions in order to identify their lexico-syntactic regularities (typical of that textual genre) and to use these regularities to construct the CNL.

We used the SDMC tool (Sequential Data Mining under Constraints) (Quiniou, Cellier and Legallois 2012, 167), on the corpus of specifications from the CNES. The tool was able to identify phrasemes, which not only include recurring lexico-syntactic structures but also multiword terms (for example, en pointage inertiel [inertial pointing]) and expressions. By deleting the terms present in the CNES database, we were able to maintain the structures that seemed to correspond to sentence structures [START_REF] Warnier | A Methodology for Identifying Terms and Patterns Specific to Requirements as a Textual Genre Using Automated Tools[END_REF], for example [sur réception de tc, le lvc [envoyer] …] (on receipt of the tc, the lvc [send]...). Based on the selected structures, we proposed alternative structures that conveyed the same information. In the example above, envoyer [send] may have the following forms: envoie/doit envoyer/doit pouvoir envoyer/enverra [send/must send/must be able to send/ will send]. Concerning injunctions, 20 sets of structures were thus specified in different lexical contexts. These structures were then proposed to specialists in the writing of specifications, who evaluated their acceptability. These evaluations of spontaneous formulations (corpus) by means of acceptability tests [START_REF] Warnier | Contribution de la linguistique de corpus à la constitution de langues contrôlées pour la rédaction technique : l'exemple des exigences de projets spatiaux[END_REF]) allowed us to propose usable recommendations. In fact, CNES decided to use our CNL to train the writers and editors of specifications.

These results point to the need for companies and businesses to employ linguists. The development of this CNL also led to a number of theses carried out at the CLLE laboratory and highlighted the need to borrow concepts such as 'usability' from ergonomics. The consideration of usability in the construction or adaptation of CNLs allowed Condamines (2020) to develop the concept of 'ergonomic linguistics'.

Ergonomic linguistics provides a framework to propose answers to language needs, especially within firms. The aim is to propose linguistic standards (terminologies, CNLs, etc.)

that will be effective in a given context as well as acceptable to users. From the perspective of Textual Terminology, (see Table 1), at the beginning of the study, ergonomic linguistics characterizes a need, and at the end of the analysis, it evaluates the relevance of the results in relation to this need and helps to make them available in a form that can be applied by the end-users. The integration of ergonomic skills and perhaps even cognitive psychology (to conduct usability testing) could be an interesting perspective for Textual Terminology.

Textual Terminology, NLP, and KE/AI: Where does the "symbiotic relationship" go?

As previously mentioned in Section 1, corpus analysis for the compilation of terminoontological resources was one of the founding pillars of Textual Terminology and of its connection with NLP and KE. However, the situation is quite different today. According to Condamines (2018, 343), "the original 'symbiotic relationship' between terminology and Knowledge Engineering, evoked by [START_REF] Skuce | Terminology and Knowledge Engineering: Exploring a Symbiotic Relationship[END_REF], no longer applies". There are at least two possible reasons for this: (i) the loss of a common research objective; (ii) the question of corpus size and the methods used to explore it.

As pointed out by [START_REF] Condamines | Terminological Knowledge Bases[END_REF], research objectives have experienced a dramatic change. There is considerably less emphasis placed on building termino-ontological resources from texts, especially based on symbolic approaches, whereas more is placed on ontologies, based on automatic learning methods. The development of these methods has triggered a real paradigm shift in NLP. Even if some studies still use a symbolic approach to build ontologies (e.g., [START_REF] Asim | A Survey of Ontology Learning Techniques and Applications[END_REF], studies such as those by [START_REF] Church | A Pendulum Swung too Far[END_REF] or [START_REF] Hall | Studying the History of Ideas Using Topic Models[END_REF] have estimated that since the early 1990s, the use of statistical methods in NLP (including machine learning) has risen from 30% to over 90%.

The role of linguistics in NLP has thus become less important. As Eensoo and Valette (2013, 2), point out, "[p]our une même applicationpar exemple la traduction automatiqueles méthodes symboliques, jadis, mobilisaient pendant plusieurs années une armada de linguistes pour l'écriture de règles ; aujourd'hui, pour peu que des corpus parallèles de taille suffisante soient disponibles, un système par apprentissage nécessitera très peu de ressources humaines et de temps". 4 Efforts in NLP are now focused on developing these approaches, which most often require very large corpora.

Despite the lack of consensus on corpus size (e.g., Corpas Pastor and Seghiri Domínguez 2010), Fabre and Lenci (2015, 9) observe, "[t]here has been a clear shift from the treatment of middle-sized specialised corpora to the acquisition of distributional thesauri in the 90's (…), to the compilation of corpora as large as possible, often heterogeneous in genre and domain". In the context of (neural) machine translation for instance, Ghaddar and Langlais (2020, 3595) explain that "[t]hese models require the use of large-scale parallel corpora to train millions of internal parameters". In the field of automatic distributional analysis methods, underlying much current research in NLP (e.g., Heylen and Bertels 2016;

Lenci 2018), Boleda (2020, 230) specifies that "[a] rule of thumb is to have at least 20-50 instances of each expression to represent; many languages, domains, or time periods simply lack these data."

Whereas some authors seek to show that applications of current distributional approaches are possible in smaller corpora5 (e.g., [START_REF] Fabre | Ajuster l'analyse distributionnelle à un corpus spécialisé de petite taille[END_REF][START_REF] Tanguy | Extrinsic Evaluation of French Dependency Parsers on a Specialised Corpus: Comparison of Distributional Thesauri[END_REF], these corpora still have a minimum of several million occurrences, which is still quite large in comparison to the usual size of specialised corpora (e.g., L'Homme 2020). Moreover, with the development of new demands for terminology analysis (Section 3.1.), an increase in the size of specialised corpora is foreseeable. However, the following points about large specialised corpora should be highlighted:

 Specialized corpora are still relatively small in comparison to current approaches in machine learning (and especially distributional analysis in general language).

 Even if the total number of occurrences is in the billions, the division into subcorpora for the purposes of comparability considerably reduces the size of the datasets to be analysed. In the case of the Humanitarian corpus (85M occurrences), the sub-corpora have between several millions and less than 1M occurrences (Picton, Drouin and Humbert-Droz, forthcoming).

 Specialized corpora are still difficult to collect and compile (section 2.1.1).

Nevertheless, in view of new needs and the fact that data access is, to a certain extent, easier, Textual Terminology should tackle the issue of large specialised corpora, especially since it is closely linked to that of the NLP methods and approaches.

Analysing specialised corpora today: methodology

The nature and evolution of specialised corpora raise different questions regarding the tools and NLP methods available for the analyses, and in particular, as they require the combination of quantitative analysis (e.g., to use data to identify leads and trends) and qualitative analysis (to refine the initial quantitative observations and interpret them appropriately).

This issue goes hand in hand with the need for data visualisation to analyse complex data (Picton, Drouin and Humbert-Droz, forthcoming). [START_REF] Caple | Using Kaleidographic to Visualize Multimodal Relations ithin and across Texts[END_REF], for example, discuss this issue in relation to the analysis of data from different media

(including text and images in news items). [START_REF] Anthony | Visualisation in Corpus-based Discourse Studies[END_REF] shows the diversity of possible means of visualisations and their impact on corpus analysis results. In diachronic linguistics, [START_REF] Hilpert | Dynamic Visualizations of Language Change: Motion Charts on the Basis of Bivariate and Multivariate Data from Diachronic Corpora[END_REF] proposes dynamic visualisation solutions to handle big diachronic comparable corpora for a fine-grained description of phenomena. Although these reflections seem to largely apply to the field of general language, Textual Terminology can greatly benefit from them.

Nonetheless, other approaches in NLP research seem far removed from the specific needs of Textual Terminology and specialised corpora. For example, distributional analysis  The complexity and diversity of distributional approaches make it often difficult for terminologists to understand how these approaches work and their applicability to the data at their disposal (e.g., Boleda 2020). Distinguishing the pros and cons of each approach requires advanced skills related to computer science, AI, and statistics.

 As previously mentioned, these methods are generally based on very large and heterogeneous general data, which do not reflect the context and needs of specialised corpora, even large specialised corpora. Current discussions in NLP do not therefore address certain issues that are directly related in terminology.

However, various studies tend to show that the nature of corpora has an impact on the results provided by a machine learning approach in distributional analysis. For example, Del [START_REF] Tredici | Short-Term Meaning Shift: A Distributional Exploration[END_REF] discusses recent methods for distributional analyses and short-term diachrony, whose relevance for terminology is evident (e.g., Dury 2004, in this volume;[START_REF] Picton | The Dynamics of Terminology in Short-Term Diachrony: A Proposal for a Corpus-based Methodology to observe Knowledge Evolution[END_REF]). These authors clearly highlight the impact of the temporal shift in corpora on the results of diachronic distributional analysis and the difficulties of interpreting them. Choosing the right distributional method for the right corpus is thus of prime importance but remains a difficult point that requires navigating between the specific needs of terminology and the evolution of NLP.

Regarding this distance between NLP and Textual Terminology, Firth's (1957) remark, "[y]ou shall know a word by the company it keeps", is very often cited to justify the distributional approach in NLP. This quote is often taken out of context, however, with respect to Firth's work as a whole. In another excerpt, quoted in Duvivier-Senis (2016), Firth describes the distributional approach as follows: "[f]irst the structure of the appropriate contexts of situation must be stated 7 . Then the syntactical structure of the texts. The criteria of distribution and collocation should then be applied" [START_REF] Firth | Linguistic Analysis as a Study of Meaning[END_REF](Firth [ ] 1968, 19), 19). Various authors emphasise the same idea concerning the interpretation of the results provided by distributional approaches, such as in Fabre and Lenci (2015, 9):

What was clearly asserted in Harris' original method was the fact that such inductive semantic classifications reflected the Distributional Semantics Today's use of words in specific corpora. The approach was set in the context of the theory of sublanguages, based on the assumption that only corpora from restricted domains could guarantee the possibility to build up clear-cut semantic categories. (our emphasis)

Consequently, there seems to be a certain distance between the original assertions by Firth and Harris (who had, however, different theoretical perspectives) on distributional analysis and current developments in machine learning and Artificial Intelligence on big data, which rely on large and heterogeneous data, by definition.

These elements fuel the reflection about the nature of the relationship between NLP and Textual Terminology today. While it is clear that the symbiotic relationship no longer exists, close collaboration seems possible and even essential. However, this collaboration must consider the specificities of specialised (large and complex) corpora and question the relevance and portability of technical developments in NLP for the new needs of Textual Terminology.

Concluding remarks

This chapter has proposed a retrospective look at the origins of Textual Terminology. After describing its methodological approach and the questions it raises, this perspective enabled us to highlight the evolution of this theory in the short and medium term, and where to situate it today.

The elements used to define Textual Terminology might initially seem quite similar to those of applied corpus linguistics [START_REF] Hyland | Corpus Applications in Applied Linguistics[END_REF] or even specialised discourse analysis (Gotti and Giannoni 2006). However, the specificity of Textual Terminology lies in the following: (i) it concerns only specialised corpora; (ii) the purpose of the study is considered throughout the analysis; (iii) terms are the point of entry into the texts; and (iv) tools are used as much as possible, with particular attention to the nature of the results obtained.

It also allowed us to highlight the cross-fertilisation between Textual Terminology and other theories of terminology and, consequently, the ideas they share. We are referring to the need to base analysis on specialised corpora, the consideration of the situational context of project calls, the consideration of polysemy and variation, etc. (see chapter by Delavigne and

Gaudin in this volume). Nevertheless, Textual Terminology, while nourishing these links, bases its specificity on four main axes: (i) the focus on tools for exploring corpora; (ii) the systematisation of its methodological approach (with a view to scientificity and

  Today some (non-exhaustive) examples of large specialised corpora include the following: more than 23M occurrences for the Ecolexicon corpus (León-Araúz, San Martín and Reimerink 2018); 85M for the Humanitarian Encyclopedia 6 (Egger, Picton and Schopper 2018); 4.5M for the TALN corpus built by CLLE lab(Tanguy, Fabre and Bard 2020), etc.

  approaches and techniques, which are at the heart of Textual Terminology methods (Section 3.1.4.), are developing exponentially in NLP, along with machine learning and big data possibilities. In terminology, this path has been explored in some depth for semantic relation extraction (e.g., Bernier-Colborne and Drouin 2016, Bertels, this volume) and term extraction (e.g., Drouin, Morel and L'Homme 2020). However, regarding the overall corpus analysis, two issues arise:

  reproducibility); (iii) the central role of experts (language professionals, users, field experts, ergonomists) throughout the work process; (iv) applicability of results to linguistics. This picture of Textual Terminology today, as well as of its needs and challenges, led us to highlight the new ways in which it can work together with other disciplines. For instance, within the framework of Textual Terminology, ergonomics and psychology have become of great importance to meet new needs (e.g., CNLs) and ensure the usability of the results obtained. Finally, the relationship of Textual Terminology with NLP and KE/AI has been transformed by the scientific goals and developments specific to each discipline. From being symbiotic, the modalities of this relationship have evolved into a real need for collaboration. Because of these expanded horizons, Textual Terminology is currently in rapid development and can look forward to a very bright future.

Table 1 -

 1 Tasks/stages and protagonists -Textual Terminology

	Tasks/Stages	Protagonists
	Identification of a need or a problem	(ergonomists)
	(translation into a linguistic issue)	End-users
		Domain experts
		Language experts (analysts, terminologists-
		linguists)
	Constitution of the corpus	Language experts
		Domain experts
	Selection and use of tools	Language experts
		If necessary, NLP experts
	Interpretation of the clues provided by tools Language experts
	Construction of a final interpretation	Language experts
		Domain experts
	Integration of results into a linguistic Language experts

https://humanitarianstudies.ch/. Accessed October 17, 2020. (the Centre was formerly known as CERAH).

https://evidenceaid.org/do-we-all-speak-and-practice-humanitarian-in-the-same-way/. Accessed October 17, 2020.

"[f]or a single application -for example automatic translation -symbolic methods used to mobilize an armada of linguists for several years to write rules; today, provided that parallel corpora of sufficient size are available, a learning-based system will require very few human resources and time" (our translation).

Special issues of periodic journals also question the place and assets of small corpora, for instance the CORPUS journal, issue 18, https://journals.openedition.org/corpus/3094. Accessed October 17, 2020.

https://humanitarianencyclopedia.org/concept-analysis/. Accessed October 17, 2020.