John Mcdonald
email: jmcdonald@cs.depaul.edu

Michael Filhol
email: michael.filhol@cnrs.fr2021

Natural Synthesis of Productive Forms from Structured Descriptions of Sign Language

Keywords:

Natural animation of Sign Language directly from linguistic descriptions continues to be a challenge especially in cases where the forms involved are more productive, such as geometric depictions. Prior work laid the foundation for natural Sign Language synthesis with the Paula animation system directly from AZee linguistic descriptions. This paper considers more elaborate discourse, composed of several clauses linked together by the overall meaning and involving largely productive signing. We make the case that one of the keys to natural animation of such discourse lies also in the segments between the typically annotated signs, in other words on the segments traditionally termed "transitions". By studying an example discourse video and the corresponding motion capture, we progressively build an efficient linguistic description of it and specify how to animate it naturally.

Introduction

Signing avatars continue to be an active area of research due to their potential in a variety of applications for Deaf-hearing communication including:

• an output target for translation in short, scripted situations where interpreters are generally not available [START_REF] Lancaster | Voice activated display of american sign language for airport security[END_REF]);

• a display to hide the identity of a signer in online communication [START_REF] Kipp | Assessing the deaf user perspective on sign language avatars[END_REF]);

• a tool for Sign Language education [START_REF] Jamrozik | Teaching students to decipher fingerspelling through context: a new pedagogical approach[END_REF].

For an avatar to serve in these capacities, it must be able to produce the full range of motions and linguistic structures in Sign Language with a naturalness that does not distract the viewer from the message being communicated. Unfortunately, the naturalness and breadth of communicative ability of signing avatars remains a challenge due to a variety of factors that cause the avatar to be judged as robotic or strange. A primary cause of robotic synthesis is the lack of subtleties in the movements of the human body which contribute to the richness of the language in both linguistic and bio-mechanical capacities. These subtleties include

• the position and orientation of joints;

• motion details including acceleration profiles of joints;

• relative timing and coordination of the movements of body parts.

In fact, the pacing and dynamics of the body's motions can be influenced by both the emotions of the signer and by the grammatical structure of the discourse [START_REF] Johnson | A segmental framework for representing signs phonetically[END_REF][START_REF] Wilbur | The linguistic description of american sign language[END_REF]). Reproducing such dynamic subtleties is critical for synthesis to be judged natural. The avatar's linguistic input must represent, and the animation system must reproduce, such variations in timing and movement.

Recent efforts in both synthesis and representation have focused on including prosodic features in an effort to improve the naturalness of synthesized Sign [START_REF] Adamo-Villani | Asl-pro: American sign language animation with prosodic elements[END_REF][START_REF] Filhol | Synthesizing sign language by connecting linguistically structured descriptions to a multi-track animation system[END_REF]), but many avatars still do not incorporate such features. In addition, while many posture and motion details are captured by linguistic descriptions, others will necessarily be simplified in the process of encoding linguistic meaning. Yet the avatar must include such details in order to sign naturally.

The principle of the coarser the better

In an effort to increase the communicability of a signing avatar for both the range of Sign processes supported and the subtleties in pose, motion and timing of the avatar, this work builds on a principle, articulated a few years ago for Sign Language synthesis: animation will tend to be more natural when built from larger segments of discourse, e.g. a lexical sign or an entire non-manual process, rather than from a sequence of phonetic or articulatory constraints [START_REF] Filhol | Synthesizing sign language by connecting linguistically structured descriptions to a multi-track animation system[END_REF]). Examples of this can be seen in the two most common methods of synthesis, namely motion capture and keyframe animation.

Motion-capture synthesis, in which signing is recorded from human signers, can provide more natural animation when larger phrases are played back as recorded without alteration. But this is rarely possible when synthesizing novel utterances, and active research is focused on stitching together small segments of motion capture on the entire body, and on discrete parts of the body to layer non-manual processes [START_REF] Gibet | Building french sign language motion capture corpora for signing avatars[END_REF]. Such systems rarely build signing from phonetic descriptions, though generating motion capture based sign from has long been a goal as in [START_REF] Gibet | The signcom system for datadriven animation of interactive virtual signers: methodology and evaluation[END_REF]. Most often, however, this approach is forced to work with very large segments due to the nature and density of the data. Motion capture data does, however have another use in the pursuit of signing avatars. It provides a wealth of highly detailed measurements of human motion that can be used to build procedural models of sign processes. In the present work, motion capture data is used exclusively in this capacity.

Keyframe animation has resulted in highly natural signing when the entire discourse has been directly animated by a human [START_REF] Lombardo | An avatar-based interface for the italian sign language[END_REF]). However, keyframe animation computed directly from phonetic descriptions, while more flexible, is far more robotic with little coordination between body parts [START_REF] Ebling | Building a swiss german sign language avatar with jasigning and evaluating it among the deaf community[END_REF]. The advantage of keyframe data is that it is sparser than that of motion capture, and thus easier to edit and combine larger segments of animation, such as pre-animated fixed signs, into longer discourse [START_REF] Wolfe | Avatar to depict sign language: Building from reusable hand animation[END_REF]. Coupled with a linguistic description that provides grammatical and prosodic context, the resulting synthesis can be more natural than when driven from phonetic descriptions [START_REF] Filhol | Synthesizing sign language by connecting linguistically structured descriptions to a multi-track animation system[END_REF].

While working with larger animation segments is a useful goal, much of signing is highly productive and resists efforts to synthesize with large segments. Proform constructs such as classifier placements and size and shape specifiers have highly context sensitive and flexible movements that begin and end at any point in signing space, with the signer's hand and arm in arbitrary orientations [START_REF] Schembri | Perspectives on Classifier Constructions in Sign Languages, chapter Rethinking 'classifiers' in signed languages[END_REF][START_REF] Woll | The linguistics of sign language classifiers: phonology, morpho-syntax, semantics and discourse[END_REF]. When signers describe entire scenes that incorporate the size, shape, and movement of objects and actors in the scene, it is clearly impossible to either record or pre-animate all of the possibilities. An avatar must animate and combine the movements in such a way that they can be interpreted with all the right linguistic distinctions, for example, between communicating:

• placing or moving an object in sign space;

• placing collections of objects in space relative to each other.

• describing the size and shape of objects or scenery;

Prior work in this area has centered on classifier predicates and has focused mainly on synthesizing positional variability in such constructs [START_REF] Huenerfauth | Generating American Sign Language classifier predicates for English-to-ASL machine translation[END_REF][START_REF] López-Colino | The synthesis of lse classifiers: From representation to evaluation[END_REF][START_REF] Filhol | Extending the azee-paula shortcuts to enable natural proform synthesis[END_REF]. However, capturing the subtleties of context, shape and action in the situations above requires variation in posture, motion and timing that avatars have heretofore struggled to capture.

The present work will build on prior efforts to use the Paula avatar to synthesize signing directly from AZee linguistic descriptions, extending the capabilities of the synthesizer to encompass the cases listed above, by breaking these motions into smaller animatable units. These two systems are appropriate for this study as they allow the flexible specification and scheduling of signing processes in a multilinear fashion, i.e. multiple tracks with arbitrary unsyncrhonized timing, [START_REF] Mcdonald | An improved framework for layering linguistic processes in sign language generation: Why there should never be a "brows" tier[END_REF][START_REF] Filhol | Synthesizing sign language by connecting linguistically structured descriptions to a multi-track animation system[END_REF], however the lessons gleaned from the implementations presented are applicable to any system that strives to animate proform constructs.

Regardless of the size of the animatable units, whether they be single classifier placements, complete recordings of lexical items, or longer discourse, the system must combine and coordinate them using grammatical and prosodic information from a linguistic description coupled with knowledge of human motion. To exemplify these issues, this paper presents a detailed case-study of an actual signed depiction of a scene, and develops a full linguistic description for it by observing the details of the motion that we must reproduce on the avatar.

Case study: describing a dining room table scene

Let us study the full discourse utterance given in the video titled "LSF- The arrangement described is displayed in figure 1, the example excerpt being restricted the description of the rug and table.

Figure 1: Elicitation image with the example table scene [START_REF] Benchiheub | Collecting and analysing a motioncapture corpus of french sign language[END_REF] 3.1 Overview of the example Sign production

Readers familiar with Sign Language will identify six consecutive segments comprising the whole discourse, each introducing one or more objects to the scene. We list and label these segments below, in order of appearance, together with the contents they introduce: For example, S1 begins with a fixed sign (RUG), then presents the shape of the rug by drawing its outline low on the ground and finally reaffirming its position in the scene (see figure 2).

S1
In a similar fashion, the next segment S2 places a table on top of the rug, see figure 3. It is placed using a classifier to indicate a large flat rectangular object positioned and oriented parallel to the floor. To disambiguate it from any other flat rectangular object, such as a painting or a rug, a fixed sign TABLE precedes it, just like the fixed sign RUG at the start of S1. ): fixed sign, placement All 6 segments follow the same construction pattern, made of two parts. The first gives the kind of object about to be placed, while the second explicitly places those objects in the scene, possibly with other information such as orientation, shape, etc.

The first parts in each of these examples consists of frozen signs, e.g. RUG in S1. The only exception is S6, which contains a combination of two of frozen signs (FORK + KNIFE) to mean cutlery more generally. The second halves are more variable in content but all involve one or more placements, often oriented, as follows:

• in S1, the rug is given a shape, drawn with the fingers, and a position low in the scene;

• in S2, the table is placed as a flat shape above the rug (around the point labelled P in the schematic representation of figure 4);

• in S3, two pairs of chairs facing each other are placed, one pair after the other;

• in S4, four plates are placed in a rapid sequence on the table (at P 1 , ..., P 4 );

• in S5, four glasses are placed near the same points, two-by-two in a way similar to the chairs;

• in S6, four pairs of cutlery (introduced as fork & knife) are placed near the same points again, both hands working simultaneously to place each pair. Every "placement" in the description above is performed with a classifier construction. Some placements are performed simultaneously with a one-hand proform on each hand (2 pairs of chairs, 2 pairs of glasses, 4 knife & fork pairs), while some use both hands for a single proform placement (rug, table, each of the 4 plates). Also, some of the placements are oriented in an iconic fashion (chairs facing the table and each other, all cutlery items pointing inwards on the table), whereas others just take a default orientation (rug, table, plates, glasses).

Looking at the production in finer detail, one might notice that eye gaze is directed to signing space in each of the second halves of the segments listed above, in contrast to the respective first part, where it is directed to the addressee. Differences in the dynamics are apparent as well, and will be a focus of later study in the present work. All of these consideration will serve one main goal: to replicate this whole scene through an avatar with an entirely automatic process.

Traditional annotation

Figure 5 shows how the utterance would traditionally be represented, labelling units ("glosses") and assuming "transitions" in between. In the figure, the classifier names have been abbreviated to keep the diagram as short as possible.

Simply animating these units sequentially will produce less than ideal results. Analogous to early speech synthesis systems that chained word recordings, or synthesised phonemes, one after the other and produced unnatural vocalizations, animation systems that have relied on such chaining of lexical productions also result in robotic output. Changes in pacing, timing, interpolation and in the magnitude of motion must occur in the avatar as it expresses these gestural It is these aspects that make up the rhythm of signing that is the hallmark of natural motion as opposed to a uniform robotic production.

AZee

AZee is a formal approach to SL description built entirely on the linking of visible articulations (forms) and semantic functions (meaning). Signed forms in the native AZee representation are expressed using necessary and sufficient constraints on body articulations and on their synchronisation on the time line. The former include any relevant articulator and motion specification (no fixed set of, say, manual parameters); the latter includes duration of movements and transitions, but also between them if relevant and necessary.

In addition to signed forms, AZee allows to write functional values, i.e. functions that can be applied to arguments to produce a return (result) value. A function in AZee is called an AZop ("AZee operator"), and its arguments are named. Like any other value, an AZop can be named for later use.

The AZee approach to formally describing a Sign Language is to identify the meaning-form mappings in the language, and to write an AZop for each one. Such an AZop, parameterised by its meaningful variations, specifies the form part of the mapping as its return value, and is named after its meaning. This creates what is otherwise called a "production rule" for the target language. For example, the previously published production rule "restaurant" is defined as an AZop, with an optional argument loc (for location) since it is relocatable and the induced variation is meaningful. The return value (signed form) is the gestural form of the corresponding sign, possibly relocated according to loc.

If a set of enough production rules is made available to cover the target language, one can write expressions from it to represent signed utterances of any length, from a single sign to an entire discourse. Such AZee expressions all evaluate to forms, and therefore can be input to an animation system to render a video output. They are built with AZops carrying meaning, which is the point we wish to emphasise here.

Building expressions from the rule set indeed enforces that any sequence be generated by an AZop that was given meaning. In other words it becomes impossible to write a sequence for no semantically-grounded reason. Therefore we avoid the loophole described above where avatars seem to transition from one piece to the next without conveying meaning overall. Any so-called transition generated from an AZee expression will be the result of a meaningful operation and its form will be controlled, timed and accompanied by auxiliary gestures as necessary. In short, and referring back to our example, the spaces between the labelled (blue) units in the diagram are no longer blank time fillers, but rather segments under the same degree of motion control.

The next sections explore the commonalities in the forms of our use case video, and in their meaning, introducing AZee production rules where appropriate to represent their association. AZop by AZop, we build an AZee expression that represents the entire use case video. As explained above, it will cover the forms of both the labelled units and the transitions in between. We will see that it is accomplished with a very small set of rules, a statement on the economy of the approach that we return to at the end of the paper.

4 Representing the discourse with AZee for more natural animation

In order to represent this discourse with AZee, we begin with an exploration of how each of the labelled units in figure 5 can be represented as AZops. We then look at the forms and meaning of the transitions to find the linguistic processes that knit those units together into a description of the whole table scene. Encoding these processes with AZops will give meaning and purpose to the transitions while simultaneously defining the necessary non-manual signals and timing adjustments within the labelled units, all of which are needed for the avatar to produce natural signing.

Labelled units

Some of the labelled units have a fixed and almost invariable form that can be encoded as such. We deal with those in a first section. In contrast, others units behave with much more internal composition of highly variable geometric elements, which we address afterwards.

Fixed units

Some of the labelled units are fixed in their articulated form, for example "RUG", "KNIFE", "FORK", etc. The form of each such entry is invariable and has a consistent meaning. This very fact defines an AZee linguistic "production rule", like the restaurant case mentioned above. In such cases the name is comparable to an ID-gloss [START_REF] Johnston | From archive to corpus: Transcription and annotation in the creation of signed language corpora[END_REF], and the form specification is a conjunction of articulatory constraints (orientation, positioning), sometimes expressed around an optional location argument like restaurant or table . For instance, the first sign of the utterance can be represented by an AZop named rug and containing a set of articulatory constraints that produce the (manual) gesture in the left image of figure 2.

Then, the AZee expression below, which is a simple application of rug with no arguments1 , which will result in the correct articulations specified for the avatar to render:

rug()
To animate it however, a direct application of a set of articulatory constrains is precisely what what we are trying to avoid since it tends to produce robotic motion. But since the form of such an expression is fixed, the synthesis system is free to shortcut on them, bypassing the form specification to substitute a pre-existing animation of the sign, resulting in a more natural motion. This mechanism was proposed in earlier work, and called a shortcut [START_REF] Filhol | Synthesizing sign language by connecting linguistically structured descriptions to a multi-track animation system[END_REF].

Geometrically productive units

In contrast to the fixed units, this section addresses those whose contained movement is variable in space, highly dependent on context like the classifier placements. The obvious problem in terms of animation will be that such units are too unlikely to be reused in other discourse exactly to be usefully treated with coarse pre-animated blocks.

The classifier placements are performed through a small downward movement we call "settle" [START_REF] Filhol | Extending the azee-paula shortcuts to enable natural proform synthesis[END_REF], and a chosen proform, for example prf-flat-square-large for large oblong surfaces. While the location changes in space, there is nevertheless a great deal about the form of each placement that is constant, including an eye gaze towards the object that precedes the settling movement. The meaning for each of these cases is consistent: placement of an entity in the scene, of the type indicated by the chosen proform, at the location where the movement settles.

Justified by the form-meaning association above, the AZop place-proform, with arguments prf (proform) and loc (location), was introduced in our work (ibid.). Its block diagram is given in figure 6, where "eg:loc" specifies the eye gaze directed towards loc. For example, the table placement at the end of S2 would be generated by the following expression, where prf-flat-square-large is the two-handed proform with two L-shaped hands in the same plane, and P the table center as illustrated in figure 4, around which the proform settles:

place-proform(prf =prf-flat-square-large, loc =P )

When two placements are performed together like with the chairs (twice), the glasses (twice), or the cutlery (four times), the appropriate AZop to bring into play is simultaneous. Its form is simply to perform its arguments at the same time (in parallel, as shown figure 7), which is iconic of its meaning, namely that they are true or happen at the same time. For example, the expression below places a knife at point P k and a fork at P f , simultaneously as two small elongated objects using proform prf-elongated for both:

simultaneous(sig1 =place-proform(prf =prf-elongated, loc =P k ),sig2 =place-proform(prf =prf-elongated, loc =P f ))
These place-proform expressions are not fully fixed in form, and indeed are infinite in number because their loc argument takes its value from a continuous space. Thus to animate them, full shortcuts as for fixed signs are not possible. But the dynamics being constant, some fixed elements of motion can be triggered with the templating system introduced in the referenced work.

In this templating process, the AZee system is providing the classifier and the point in signing space at which it should be placed. The classifier itself most often indicates the shape that the hand will take2 . But it is important to note that the specification of that handshape also influences overall posture including the height of the elbow, and the shoulder. The avatar synthesis system may draw on a pre-defined artist pose to set this additional posture information before retargeting the settle motion at the specified point.

The final gestural unit that we encounter in this description is the tracing of the outline of the rug. The signer performs this motion with a pointing handshape on each hand, and with the hands starting near the center line of the body. The hands then simultaneously trace, in a mirrored fashion, the four sides of the rug with an accompanying eye-gaze towards the object being depicted. Gestural units such as these are very similar to the shape deployments recently addressed, which make use of an AZop named deploy-shape [START_REF] Filhol | The synthesis of complex shape deployments in sign language[END_REF].

Transitions between labelled units

Now that we have covered the labelled blocks, explaining how they can be individually represented and animated, the next step is to define how these units should be placed on the timeline to build the narrative. The annotated figure 8 reveals a total of 21 transitions, loosely grouped in categories enumerated below, where the indicated markings match those in the figure :   1. the transitions between the segments S1-S6 (5 solid vertical lines); 2. the transition between the two parts of every segment (6 dotted vertical lines in the figure), i.e. between the type/kind of item and the position/orientation of the placed items;

3. transitions between items that are grouped items into collections, e.g. of 4 plates, glasses, etc. (8 transitions marked with a diamond symbol);

4. the last two remaining (marked with a star), i.e. the transition between the size and shape specifier for the rug and its placement, and the transition in the juxtaposition of the fixed signs FORK and KNIFE. As stated earlier, a sequence can only be generated from AZee production rules conveying meaning. We must therefore choose which AZops to combine. We do this by investigating the dynamics, as recorded by motion capture and video data, over the transitions.

Category and Side-Info

Considering the signer's motion during each of the six segments, a clear pattern emerges. The transition between the two parts on either side of the dotted vertical lines in figure 8 is fast (approximately .2 s on average), and the signer systematically raises and tilts her head with an accompanying raising of the eyebrows. This pattern has been consistently observed in other signers as well and is unique to this particular type of transition. If the avatar is to correctly communicate this contextual meaning, it will have to incorporate this head and eyebrow signal, regardless of whatever else is happening simultaneously in the discourse.

The consistency of this form, featuring two juxtaposed parts which we call cat and elt, and the consistent association with the meaning that "elt is to be understood as of category cat", is captured by the AZop category, with arguments cat and elt. A block diagram for category is given in figure 9. For example, S2 is represented by the following expression: This single AZop is sufficient to handle all six of the dotted line transitions in figure 8. Either argument in a category expression may be a single glossable unit, such as a fixed sign like the cat argument in S2 above, or a compound statement, such as the collection of plates in S4 or the pair in the second part of S1, whose inner transition is starred in figure 8.

This transition between the shape depiction of the rug and its placement, is similar in form to that of category. It is a fast one, and involves a similar head/brow movement. However, the head motion is synced differently, as it immediately precedes the second of the two blocks rather than the first. To understand the difference and see what subtleties the avatar will need to capture, consider the actions during the production of the shape specifier and the classifier placement for the rug. We notice several things:

• the signer's gaze is down towards the shape being drawn during the deployment;

• the time between the two signals is about 5 frames (.2 s);

• the signer's head and gaze rise to the addressee for the second signal, and the eyebrows raise;

• the two signals are both signed at a normal pace.

This juxtaposition indicates that additional information is being appended. Note that tracing with the fingers is enough to define the size and shape of the object, which the category rule identified as a rug, but then to firmly anchor that rug in signing space, the signer provides additional information: it is placed "here". This is exactly the form produced by the AZop side-info, whose arguments are focus and info and whose meaning is that "focus is given an additional, although linguistically non-focused, information info". The generic form it produces when applied is represented in the block diagram of figure 10. This interpretation is consistent with the signing in the video. The second half of S1 can therefore be captured with a straight-forward application of the production rule side-info:

Grouping items in collections

Other transitions group multiple items into a collection or list. Our discourse contains several such lists, identified in section 3.1:

L 1 two pairs of chairs facing each other across the table ;   L 2 four plates on the table, one at each place setting; L 3 two pairs of glasses; L 4 four sets of cutlery.

Examining the motion capture data for the signer's wrist height in the first four placement lists L 1 through L 4 , reveals that there are two very different kinds of motion present. The first placement, for the chairs on either side of the table, is deliberate and has a significant pause between the two pairs of classifier placements. The other placements are quicker with a "bouncing" motion between the placements. The motion curves (in height over time) for these actions in figure 11 clearly demonstrate the differences.

The first transition in the chair placement is very different from the others since between the placements is a clear hold or pause, and a hint of a blink, or at least a raising of the eyelids between the placements. In addition, the placement of the chairs is noticeably slower than the others. Measuring the total duration of the placements (the upward and downward motions in these graphs) yields average timings per placement of chairs (13 ms), plates (10 ms), glasses (11 ms) and cutlery (10 ms).

Thus, the placement of the chairs is on average about 25-30% slower in its placement, and feels more deliberate because of both the pacing and the pause between the placements. It is also worth noting that while the performance of the placements of the glasses is a little softer than that of the plates and cutlery, the glass placements are clearly not as slow as the chairs, and the motion curves do not show a clear plateau between the motions. Whether the slightly slower placement of the glasses is due to normal human variation in production or carries linguistic meaning will be investigated in a future study. In all, we see two kinds of collections presented here, one with holds and slower movements, which we will call form A, and the other with squeezed or faster item placement and short transition times with no hold, denoted as form B. Interestingly, the signer also holds and blinks at the end of S3 through S6. This is the same form A as observed for the list of chairs for example, only at a higher level of the discourse structure. Moreover, the meaning is compatible with the interpretation of a collection as well.

The meaning conveyed each time with form A is that of a closed enumeration of items, each given equal specific focus, without precedence or emphasis on any particular one. In AZee, this consistent form-meaning association is supported by an AZop called each-of, whose generic form A can be represented by the block diagram in figure 12. The enumeration made of S3 through S6 is written as follows, where list is the native AZee operator for extensional list construction:

E tbl : each-of(items =list(S3, S4, S5, S6))
In each exhibited occurrence of form B, the meaning is also to form an exhaustive collection like A. In contrast, to A however, none of the individual contents is emphasised, rather it acts to focus on the set. For example, four plates are placed around the table (and no more), and no emphasis is placed on any of them in particular.

The same form B also appears elsewhere, not involving placements, namely in the first half of S6, i.e. between fixed signs FORK and KNIFE on the second starred transition of figure 8. The evidence that these form a list in the pattern of B is two-fold:

• the two signs are performed with no hold between them, as shown in figure 13;

• the duration of each sign is shorter than with isolated signs of the same form (FORK has a similar motion as GLASS but FORK lasts merely 9 frames as compared with 12 for GLASS; KNIFE is even truncated).

Figure 13: height of the wrist during the knives(blue)/forks(red) pair

In terms of meaning, we understand that there are exactly two items (closed enumeration), but the focus is on the set (pair) as a whole, not on the contained items. This is consistent with what follows as only pairs are placed afterwards without any detail on which is which. This consistent meaning-form association is supported by the AZop all-of, whose generic output form B is illustrated in figure 14. The four plates in the second half of S3 can be generated with The first half of S6 is encoded in the following AZee expression:

all-of(items =list(knife(), fork()))

Animating these types of collections requires that we investigate both the linguistically described forms and additional motion controls to transform the repetition of "settle" movements. There remain two transitions left unaccounted for, which we will return to afterwards.

Tuning Motion from Linguistic Descriptions

Our prior work [START_REF] Filhol | Extending the azee-paula shortcuts to enable natural proform synthesis[END_REF] explored the functionality necessary for the Paula avatar to use artist templates to allow natural placement and movement of classifier constructs from these kinds of AZee expressions. This section will explore the motion controls necessary to capture the dynamic differences between these simple placements, such as in the placements of the rug and table, and those in the each-of and all-of lists described in the last section.

The motion controls that we will be using are similar to those animators have been manually using for a long time [START_REF] Thomas | The illusion of life: Disney animation[END_REF], including:

• the shape of the motion path;

• the abruptness or ease in which a body part approaches or leaves a target;

• the synchronization in the timing of torso movement with the rest of the motion;

• other coordinated body motions that affect the perception of the movement.

However, the goal here is to trigger such features automatically from the linguistic descriptions in the previous section.

Isolated placements

In order to highlight the differences here, we recall, in detail, the motion generated from a simple "settle" placement. This is exemplified in the placement of the flat rectangular object to indicate the table's location on top of the rug as in figure 15a. In this situation, the avatar system can shortcut directly on the known "place-proform" process while filling in the necessary body postures and motions from an artist template, i.e. a pre-animated pose built by an artist as described in [START_REF] Filhol | Extending the azee-paula shortcuts to enable natural proform synthesis[END_REF]. It then can use the timing and duration information directly from AZee to coordinate the manual and gaze processes.

As described in the previous work, the artist template provides several important cues for producing a natural posture and settling motion. A schematic plot of the motion is displayed in figure 16 where the horizontal axis is time and the vertical axis is the height of the signer's wrists. It is important to note that the hold at the end here is not intrinsically a part of this settle movement, but the ease, or softness with which the hands settle, is. The hold may come from an AZop that indicates the end of a phrase or clause. This expression takes a list of signed productions as an argument, and conveys the fact that each placement is applied in space, with no importance or precedence. The expression specifies the resulting forms to render, which consists of the expected sequence, with a specific holding time at the end of each item, allowing the interpretation of the above meaning. Figure 15b shows a single frame produced by this rule.

Placements with each-of

Since this process is a repeated application of the same movement at different spatial locations, the avatar system can simply apply the artist template as before several times with the additions of the holds specified by AZee. Figure 17 shows a schematic diagram of the resulting motion in this case.

Placements with all-of

Things get a little more complicated when approaching the placement of the set of four plates. This time the signer uses quicker movements between subsequent items. In terms of meaning, the focus shifts from the individual items to the set formed by all of them together as expressed by the AZee expression already given in §4.2.2, and duplicated below. The resulting form specified by this new rule is a shorter duration or squeeze for each of the items, and does not specify hold blocks between them.

all-of(items =for p in list(P1, P2, P3, P4): place-proform(prf =prf-flat-round-large, loc =p))

In this case, however, analysis of corpus examples shows that the motion is altered in more ways than those provided by AZee. The downward placement actually ceases to "settle" and becomes a distinct bouncing between the placements. The top-down short-cutting system allows the avatar to distinguish the difference between the each-of and all-of. So, Paula is free to alter the motion within the bounds of the linguistic constraints to produce this bounce. This application of the "coarser the better" principle is in fact necessary here to provide the correct motion allowing the avatar to:

1. cause the arm's approach to the target point to be more abrupt instead of easing-in;

2. start the next cycle abruptly to complete the bouncing of the arm at the target point;

3. depending on the geometry of the classifier and the amount of arm motion involved, to shorten the stroke of the cycles to compensate for the squeezed timing;

4. alter the timing of the signer's eye and head movement in synchronization with the actions on the hands, with a more continuous progression.

The effects of all of these can be seen in figure 18 where the path bounces instead of coming in tangentially and the heights of the cycles are somewhat shorter than before. It is important to note here that this bouncing action really only makes

Figure 18: Diagram of movement for all-of sense in the case of placements, the default case being only a squeeze. In fact, it is not at all clear that the all-of list connecting the FORK and KNIFE fixed signs exhibits this kind of discontinuity in velocity. This means that it is up to the animation system to decide when the bounce happens since it cannot be specified for us linguistically. The hierarchical description coupled with the templated shortcut system gives the synthesizer the needed freedom to do this.

The synthesizer knows that it is building an all-of list and also that each of the items is a classifier placement and so can trigger the bouncing action.

6 Animation from the full hierarchical description

In the sections above, we have dealt with almost every transition exhibited in the example discourse. The only two left aside are the first two solid vertical lines following S1 and S2 respectively. They do look much like the other intersegment transitions, and in terms of meaning, they could be interpreted as part of the same list of objects, although not on/around the table but in the room. However, S1 and S2 both contain placements on which the following discourse sections depend:

• S1 provides the anchor point (rug on the floor) for the table that follows, which we interpret as placed on the rug;

• S2 provides the anchor point (table surface delimited around P ) for the list of objects that follow, whose placements are all interpreted as relative to it (on/around the table ).

Besides, while the difference is subtle in form, S1 and S2 each exhibit an ending hold duration that is slightly longer before the next segment begins.

A more appropriate AZee operator to represent the post-S1 and -S2 transitions is context, whose arguments are ctxt and proc. It produces the two in sequence while adding the (somewhat longer) hold at the end of ctxt (see block diagram in figure 19). Its interpretation is that the signed proc is in the context of ctxt. In this case, it is a signing space context. So, in our specific example, • Head Mvt for head, torso and facial movement;

• Blink for scheduling blinks;

• Gaze for the avatar's eye-gaze. It is worth mentioning again that this video has been produced directly from the AZee description with no intervention from an animator other than the construction of the shortcut animation dictionary and templates for the classifier placements. All of the relative timings between elements in the animation are scheduled directly from the AZee output.

To compare the results of the synthesis, figure 22 contains the relevant frames of the avatar's motion that compare to the signer's movement shown initially in figures 2 and 3, in addition to the placement of later objects on the table. Notice that the non-manual signals in the fixed signs are consistent with the signer's as is the raising of the eyebrows for the rug placement after she draws the outline of the rug.

The signed depiction of the table setting is a very rich combination of elements that need to be each animated and then combined using the appropriate prosodic elements. From the description of the table scene in section 3.1, we can see that the overall depiction of the table is organized as several major groups of items, which are placed in relationship to each other. From the synthesis system's perspective, AZee is ideal as a description system for such discourse, because it organizes the description of the signing hierarchically so that the synthesis can shortcut at a variety of levels depending on the animation services at its disposal. In addition, it provides prosodic information including relative timing information for each process that will enable the animation system to coordinate all of the simultaneous signals in the discourse.

This entire scene description has been described linguistically with the following short list of AZops (identified and named linguistic production rules) that connect the labelled units:

• category(cat, elt), meaning elt as an instance of cat (used here at the top-level of every segment S 1..6 );

• side-information(focus, info), meaning focus, about which additional information inf o is provided;

• context(ctxt, proc), meaning proc occurring in the context established by ctxt (in our use case, anchoring a location relative to which items are positioned in proc);

• simultaneous(sig1, sig2 ), meaning sig1 and sig2 occur simultaneously;

• each-of(items), meaning the collection of items in the list, each with equal focus;

• all-of(items), meaning the set of items (most often of the same kind) as a single discourse entity.

This list largely overlaps the one already presented in [START_REF] Filhol | The synthesis of complex shape deployments in sign language[END_REF], which focused on complex shape deployments. The only additions here are category, side-information and context, which have been previously published in [START_REF] Filhol | Juxtaposition as a form feature; syntax captured and explained rather than assumed and modelled[END_REF]. The parsimony of this AZop system is extremely encouraging, as it indicates that a relatively small set of AZee operators can generate extremely sophisticated signing, including the infinitely variable productive units, given the appropriate dictionary of pre-animated signs and proform templates.

Conclusion

The results in this paper expand on the ability of the AZee and Paula systems to represent and synthesize complex discourse through leveraging larger structures in sign language to link smaller units together in the discourse. The results are also not limited to these two systems but may be seen as a case study on how other linguistic and synthesis systems may be structured in order to achieve similar results. This approach takes us from the traditional flat, linear paradigm of "whitespace" transitions between units that follow one another to a hierarchical, recursive approach able to represent connections between arbitrary chunks of signing.

Transitions are now subject to motion control like the rest, and are no longer mere padding in the signing stream between relevant units of an assumed sequence.

The connecting AZee expressions do not just add meaning to transitions, but they also add forms, both of which are crucial to animation. Every rule that places one chunk after another is accompanied with a combination of gaze, head tilts, blinks, etc. All of these processes participate in the naturalness of the animation since they link various parts of the body during the discourse, so the avatar does not have a fixed stare at the camera or a rigid torso/shoulder line. Access to the meaning also gives the animation system necessary hints to add bio-mechanical forms when appropriate, for instance if there is a so-called head tilt, there might be spine involved as well, or in the case of the all-of list where motion controls are added to give the feel of a bounce when necessary.

In the future, we will be working to expand Paula's capabilities to leverage such hierarchical descriptions, and thus the types of discourse that can be synthesized with the combined systems. This will include expanding the rules that AZee offers and the types of templates and shortcuts that Paula can implement to animate them naturally.

In this study, our methodology was to synthesize signing that replicated that of a real signer, and it has succeeded in producing all of the linguistic elements present in the source Sign Language discourse. But it is important to note that the output of such a synthesis system must be tested for understanding, grammatically and naturalness with native signers and testing these AZee rules. Thus, testing the resulting animations with native signers will be critical to ongoing development and refinement of the system.

Figure 2 :

 2 Figure 2: S1 sequence (rug): fixed sign, shape, placement

Figure 3 :

 3 Figure 3: S2 sequence (table): fixed sign, placement

Figure 4 :

 4 Figure 4: Diagram of the layout of the table scene

Figure 5 :

 5 Figure 5: Traditional linear arrangement of labeled units

Figure 6 :

 6 Figure 6: Block diagram for place-proform(prf,loc )

Figure 7 :

 7 Figure 7: Block diagram for AZop simultaneous(sig1,sig2 )

Figure 8 :

 8 Figure 8: Annotated linear arrangement

  Figure 9: Block diagram for AZop category(cat,elt )

  Figure 10: Block diagram for AZop side-info(focus,info )

Figure 11 :

 11 Figure 11: Vertical movement during classifier placements

Figure 12 :

 12 Figure 12: Block diagram for AZop each-of

Figure 14 :

 14 Figure 14: Block diagram for AZop all-of

Figure 15 :

 15 Figure 15: Single Frame from placements of objects

Figure 17 :

 17 Figure 17: Diagram of movement for "each-of"

Figure 19 :

 19 Figure 19: Block diagram for AZop context(ctxt,proc )

Figure 20 :

 20 Figure 20: Table scene description in tree layout

Figure 21 :

 21 Figure 21: Paula avatar score built from Azee tree

The sign being relocatable, rug would accept a loc argument like restaurant. But in our video, it is applied without relocation as it is performed generically. The rug entity is nonetheless placed with what follows in the utterance.

Classifiers also can specify wider shapes on the body as, for example, when placing a tree. In that case the entire forearm and hand become the tree to be placed.

The each-of list can be exemplified with a set of four plates on a table, say at points p 1 ..p 4 , each anchored in its own positions using the AZee expression below. Note that this is a constructed AZee example, not faithful to the video content.


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

How can one explain "deviant" linguistic functioning in terminology?

Keywords: Cognitive approach, Deviancy, Discourse community, Experience, Specialized Discourse, Terminology 1-Introduction

This article looks at so-called "deviant" functioning in terminology. The notion of deviancy seems to be situated in relation to a "neutral" functioning of the language, which does not take any particular communication situation into account. The article aims to show that this supposed deviancy has to be related to the communication situation itself, which, in this case, implies specialized knowledge. Rather than just being deviancies, it is argued that these linguistic formulations are tangible manifestation of the specificity of the communication situation. Three types of explanation are put forward: linguistic (linguistic prolixity and linguistic economy), sociolinguistic, and cognitive. Each type is exemplified by various studies.

described in a field, or by the sharing of knowledge between experts. The case of nominalizations, particularly present in specialized texts, is also studied and explained.

Finally, the article proposes an aspect that is more rarely evoked, which is of a cognitive nature. This aspect makes it possible to suggest an explanation for the direct construction of rental complements in certain situations related to sports and leisure activities.

Part 2 evokes the works that, in linguistics and sociolinguistics, evoke lexical deviance in specialized languages.

Part 3 is based on corpus studies to propose an analysis and explanation of certain phenomena from a linguistic point of view (prolixity, economy and the case of nominalization) or cognitive point of view.

2-Linguistic deviancy in specialized contexts Many authors have referred to the specificities of specialized languages (and especially of the lexicon) in relation to the general language (among others, [START_REF] Cabré | Terminology: theory, methods and applications[END_REF], [START_REF] Sager | A practical Course in Terminology Processing[END_REF]). Several spoke of « deviations », with the general language then being considered the norm.

2-1 Major studies on the deviancies in LSPs

Deviant functioning in terminology has been discussed by various authors, especially from the perspective of the theory sublanguage.

«Grammatical usage of words not only is restricted, but also deviates occasionally from normal usage outside the sublanguage. » (Lehrberger, 1986, 27).

« Sublanguages differ from standard language because the lexis and semantics are more restricted than in standard language, and the syntax may deviate in some respects from the syntax of standard language » (Pearson, 1998, 30-31).

The term "deviant", refers to the fact that certain lexical, semantic or syntactic phenomena in specialized discourses are not expected in standard usage. It is then necessary to refer to a linguistic competence assumed to be shared by all the speakers in a non-specialized, i.e neutral, context. Such a perspective, even if it is not called deviant, is shared by all the scholars who study specialized language characteristics. In fact, a sense of strangeness is common to any terminologist who begins working in a new field and it is even fundamental to the identification of terms.

Here are some examples of deviancy for French [START_REF] Condamines | Terminology: New needs, new perspectives[END_REF]:

-Use of specialized words unknown to non-specialists: actionneur (actuator), étagiste (stage integrator). -Preposition deletion in the nominal group. This case is very frequent, particularly in technical domains: contrôle commande (command control), banc simulation (simulation bench). -Unusual argument: alimenter une batterie (to feed a battery).

-Argument deletion: déposer (to deposit; banking domain), monter (to ride; equestrian domain); -Preposition deletion in the verbal group (see below): pêcher une rivière (to fish a river) in angling.

Note that some of these « deviancies » have also been described for language in general. For example, an unusual argument may be a way of describing a metaphorical phenomenon which, although frequent in specialized corpora [START_REF] Oliveira | La métaphore terminologique sous un angle cognitif[END_REF], is not specific to them but also appears in poetry, for example, or, more generally, with a rhetorical intent [START_REF] Ricoeur | The Rule of Metaphor: The Creation of Meaning in Language[END_REF]. Argument deletion is also examined in general language studies under the name of « absolute transitive verb », « intransitive use of transitive verbs » or « Definite object deletion » (when the object is implied but not stated) [START_REF] Fillmore | Pragmatically Controlled Zero Anaphora[END_REF], for example Paul has eaten, gave to the United Fund.

In this paper, what we wish to study is the role of the specialized situation in this deviant phenomenon. In the analysis of specialized languages, what is first considered is the extralinguistic elements and, especially, the shared knowledge, or at least the shared interest in a domain, between speakers and listeners. This shared knowledge is assumed to facilitate communication and to limit misunderstanding, so this element must be taken into account in describing deviancy. It is crucial to understand how it intervenes and what its limits in the supposed transparency of the communication are.

Three categories of explanations for deviancy in specialized languages are examined in the rest of the article: first, explanations pertaining to linguistic and sociolinguistic aspects, and then explanations pertaining to cognitive aspects.

2-2 Sociolinguistics aspects of the deviancies in LSPs

WEWE

The sociolinguistic explanation of terminological deviancy is linked to the existence of a community that shares certain interests:

« All like interests are potential common interests; in so far as that potentiality is realized community exists » (Maclver, 1970, 108) « Specialized communities [are] defined not only by their specialized knowledge, but also by their specialized social practices, including their specialized discourse and communication, as well as by a complex network of organizations and institutions such as universities, laboratories and associations » (Dijk, 2011, 27) Swales (2016) 455proposes 8 characteristics of a discourse community. It presents the linguistic and sociolinguistic stakes involved in this concept.

One of the main manifestations of this community of interests is the use of a common language that defines a speech community. Hymes defined the notion of speech community as follows: « a community sharing rules for the conduct and interpretation of speech » (Hymes, 1972, 54). In the case of specialized languages, the common interests are linked to shared knowledge, shared actions and/or shared purposes.

Linguistic deviancy in specialized communities is not just the common way of speaking but it is also a way to be integrated in the community. Conversely, if one wants to be recognized by a community, one has to adopt the language codes of that community. These language codes are sometimes considered as "jargons".

2-3 The role and the implications of the jargon

The word 'jargon' originally referred to the coded language of thieves, who used it to isolate their community from the rest of the world [START_REF] Gotti | The Language of Thieves and Vagabonds[END_REF]. This role of identifying insiders and outsiders of a particular community subsists in its modern meaning. As defined by the Collins dictionary, the use of jargon allows a locutor to feel part of a community:

"You use jargon to refer to words and expressions that are used in special or technical ways by particular groups of people, often making the language difficult to understand." (https://www.collinsdictionary.com/dictionary/english/jargon).

Consequently, comprehending the jargon is often restricted to community members.

One of the consequences of using jargon, however, may be to lock speakers into a world view (De Vecchi, 2002). This can limit creativity, for instance within firms:

"Companies that evolve a highly context specific language […] ultimately find that their language traps them in their existing business domain." (Brannen and Doz, 2012, 82) The use of jargon can also put off non-specialists even when reading popularized texts. A very recent experiment is described in [START_REF] Shulman | The Effects of Jargon on Processing Fluency, Self-Perceptions, and Scientific Engagement[END_REF]. It consisted in a consultation via internet. Three corpora concerning three specialized domains were constituted: one contained only domain-specific words (terms), another contained specific terms and their definitions, and the third was a popularized text. The 650 participants in the study were asked to read the texts and answer questions online about their comprehension. From the responses, the authors of the studied concluded that:

« The presence of jargon disrupts people's ability to fluently process scientific information, even when definitions for the jargon terms are provided » [START_REF] Shulman | The Effects of Jargon on Processing Fluency, Self-Perceptions, and Scientific Engagement[END_REF] While the language constitutes one of the main building blocks of the community, it may also be a highly constraining element. When a non-expert speaker encounters what seem to him/her to be deviancies, s/he may adopt a position of rejection.

3 Studies in the corpus of lexical deviances in specialized languages

The aspects of deviance presented in this part are proposed from studies carried out in corpus. We focus on the results obtained rather than on the details of the methods used, which are described in the references referred to in the article. For all the aspects discussed, we insist on the fact that the deviancy discussed can have an explanation, in connection with the involvement of experts in the situation.

3-1 Deviances caused by linguistic factors

Two apparently opposing processes are first presented, prolixity and economy, then the case of nominalizations is discussed in greater detail.

3-1-1 Linguistic « prolixity »

We use the term « prolixity » to refer (at least partly) to the opposite phenomenon to the one expressed by linguistic « economy » examined in part 3-2-2. It may seem odd to talk about prolixity in relation to specialized discourse, which is generally reputed to seek brevity. In fact, the nominal groups in the LSP we are going to talk about are often already shortening compared to a more natural language, but they still often remain very long. While linguistic economy is achieved mainly through the deletion of elements, linguistic prolixity concerns mainly the addition of modifiers in nominal groups. It is well-known that nouns are very frequent in LSP (Language for Specific Purposes). Antia (Antia, 2000, 159) cites Hoffmann's study on the distribution of grammatical categories in specialized texts compared to language for general purposes [LGP): « for several West European languages and Russian: Nouns constitute up to 40% of LSPs while accounting for 28% in LGP, adjectives account for over 16% in LSP texts compared to 10 % in LGP […] verbs are anywhere between a half and a third less frequent in LSP compared to LGP ». In part 3-2-3, we will examine the case of nominalization as its high frequency can explain, to some extent, the high percentage of nouns. The overuse of adjectives and also of nouns can be explained by the fact that, within a specialized discourse, terms denominate increasingly refined concepts. One of the linguistic ways to specify a noun is to add a modifier, for example a qualifying adjective, a noun or a prepositional complement. This can lead to much lengthier nominal compounds than those found in the standard language, for example Low-energy charged particle detector. Where a non-specialist speaker will only be able to imagine a single concept, an expert will be able to imagine several finer-grained concepts and this perception will be manifested linguistically by the addition of modifiers. For example, in space research, one can find satellite, observation satellite, earth observation satellite. For a non-expert, the three terms are considered as belonging to the same semantic category; (s)he has an approximate idea of what a satellite is and the two modifiers (observation and earth observation) are not significant and perhaps even appear superflous. In contrast, these same modifiers are highly significant for a space expert and allow him/her to express all the finesse of the field.

In disciplines that share the same field of observation, differences in noun modifiers mainly indicate differences in the perspectives of each of the domains. Let's take the case of exobiology. This is a recent field that involves four disciplines: biology, astronomy, chemistry and physics. They are all interested in life outside the solar system. The possible role of linguistic description in the definition of this new field gave rise to a project that was reported in a previous paper [START_REF] Condamines | How Can Linguistics Help To Structure A Multidisciplinary Neo-Domain Such As Exobiology?[END_REF]. Here, we will illustrate the case of one term, atmosphère (atmosphere). Even if it is a word known by every French speaker (probably with, as for satellite, an approximate definition), it is also a term and it even started out as a term. In the corpus built for the study on exobiology (310,000 words), composed of four subcorpora (one for each field), Among the non-grammatical terms, athmosphère had the highest frequency with 1004 occurrences (though less abundant in biology). Concerning the adjectives following atmosphère(s), a total of 51 different adjectives were found (for example: riche (rich), pauvre (poor), primordiale (primal), anoxique (anoxic), prébiotique (prebiotic), etc. Among these adjectives, only two were shared by all the disciplines: primitive (primitive) and terrestre (terrestrial). This abundance and diversity are a reflection of the fine conceptualization of each of the sciences. In this case, the prolixity is not a problem because when a neo-discipline such exobiology emerges, all the points of view are important and contribute to the creation of new concepts. It may become a problem if the same concept has two denominations or if one view is not compatible with that of another discipline.

Astronomy

This linguistic prolixity, which may seem obscure and superfluous to a non-expert, is, in some cases, perfectly justified and even essential for domain experts.

3-1-2 Linguistic economy Linguistic economy was first defined by the French linguist Martinet [START_REF] Martinet | Economie des changements phonétiques[END_REF] who drew on Zipf's « principle of least effort » [START_REF] Zipf | Human Behavior and the Principle of Least Effort[END_REF], and may be described as: « …consisting in tending towards the minimum amount of effort that is necessary to achieve the maximum result, so that nothing is wasted ». (Vicentini, 2003: 38) As noted by Andersen, one of the consequences of the principle of linguistic economy is that: « Special concepts pertaining to a specific knowledge area may be quite short, and this is often recommended in term formation ». (Andersen, 2007, 7) Most of time, the economy in the expression is justified by the fact that both speakers and hearers are assumed to share a common knowledge of the situation and they are capable of reconstructing missing information if necessary, as Sager pointed out:

« Accuracy and economy of expression can only be assured if we accept that a text containing terms presuppose the participants' prior familiarity with the appropiate definition of concepts » (Sager, 1990, 108) While this point may seem contradictory with the previous one (prolixity), a long term is not necessarily opposed to linguistic economy (because in a nominal group, the modifier seems to be necessary to express the finesse of a point of view). Moreover, the two kinds of phenomena (economy and prolixity) may coexist within specialized discourses. Acronyms are a perfect example of this apparent contradiction. Acronyms are used instead of developed forms (which can be very long), for example, in space engineering: Attitude and Orbit Control System (AOCS). Sometimes, in technical texts, the two forms co-exist, for example the long form is used first, at the beginning of the text, and then only the acronym is used.

In general, however, linguistic economy is described in terms of the deletion of prepositions, determiners, arguments, etc.

3-1-3 The case of nominalizations

The case of nominalizations is very interesting as they are reputed to be heavily used in specialized discourses. They are considered as a way of packaging the description of reality concisely. In Halliday's terms, they are a form of « grammatical metaphor »:

« Grammatical metaphor can take many forms […] but the form which has received the greatest attention, and the one which seems to be the most significant in terms of scientific discourse is that of processes encoded in nominal form ». (Banks, 1999, 7) « The grammatical metaphor allows any observation, or series of observation, to be restated in summary formcompressed, as it were, and packaged by the grammar » (Halliday, 1995, 20).

As we have seen, the concern for concision is constant in LSP and especially in technical discourses, as recalled by Kocourek: « Le soucWede concision constitue un facteur puissant dans la formation des phrases technoscientifiques » (Brevity is a powerful factor in the formation of techno-scientific statements.). This concern often leads to « condensation syntaxique» (syntactic condensation ) and to a « complexité concise des phrases» (concise complexity of sentences). (Kocourek, 1991, 79).

The use of nominalizations contributes to concision as they allow for the integration of two statements, resulting in a shorter but also more complex sentence. As noted by Vendler: « The device of nominalization transforms a sentence into a noun phrase, which can then be inserted into a bundle that fits into other sentences » (Vendler, 1967, 125).

In a previous study [START_REF] Condamines | Des communiqués de presse du Cnes à la presse généraliste. Vers un observatoire de la diffusion des termes[END_REF], we examined two points concerning nominalizations in space engineering, in a project concerning determinologisation. First of all, we built a French corpus of documents on space engineering organized in subcorpora belonging to different genres (from highly specialized to popularized). For the study of nominalizations, we compared two of these subcorpora: the most scientific one, comprising technical documents from CNES (Centre National d'Etudes Spatiales) and the one composed of articles from the general press. These general press articles were selected because they contained candidate-terms obtained with Termostat.1 from the scientific corpus. The two corpora could therefore be assumed to concern the same field.

In order to spot nominalizations systematically, we used Verbaction2 . Verbaction contains 10,000 pairs of French verbs and their nominalizations. We also added nominalizations that were not listed in the resource by searching for nouns with suffixes such as -ment, -tion, andage that are known to be characteristic of French nominalizations. This study confirmed the overuse of nominalizations within a scientific corpus in relation to a non-specialized one in the same field: there are almost twice as many nominalizations in the scientific corpus as in the news corpus (considered close to standard usage). Verbs are also less frequently used in the scientific corpus, which could suggest that nominalizations are used, in some cases, instead of verbs.

We also examined the presence vs absence of prepositions in nominal groups with a nominalization. Ten nominalizations were selected: absorption, acquisition, alimentation, application, conception, émission, gain, sortie, télémesure, verrouillage (absorption, acquisition, feed, application, design, emission, gain, output, telemetry, locking). Then, in each corpus, we looked for cases where these nominalizations were preceded or followed by a preposition, and those where no proposition preceded or followed the nominalization. For example: Application (de défense, de géolocalisation) vs Application (radar, satellite) (Domaine d', responsable d') application, vs sous-directeur application. The differences between the scientific and news corpora are clear concerning the nominalizations examined. The Scientific corpus contains twice as many occurrences of a nominal group (with a nominalization) without preposition as the News corpus. If we consider the language used in news texts to be non-specialized, then we can say that prepositions in a nominal group containing a nominalization are less frequent in scientific language than in general usage.

While the overuse of nominalizations is no doubt partly due to the search for concision, this phenomenon may also be linked to a desire to present things in a stable form. Nouns are considered as linguistic forms that express completed entities, so they are frequently used as labels or names, for paintings for example, whereas verbs are, in principle, the forms dedicated to the expression of actions. Nominalizations may be used with the aim of producing discourses that appear to be scientific.

« La nominalisation est fort utilisée pour créer un effet d'objectivation : c'est pourquoWeelle est massivement attestée dans les textes scientifiques (notamment positivistes) et dans les discours quWeles imitent (langue de bois) » (Nominalization is widely used to create an objectification effect: this is why it is massively attested in scientific texts (especially positivist ones) and in discourses that imitate them) (Rastier, 1995, 51).

The overuse of nominalizations compared to the use of verbs has at least two unintended consequences. First, contrary to verbs, nominalizations may be used without any of the obligatory arguments of the conjugated verb. For example, very often, the subject disappears with the nominalized form as in (1):

1

) The installation of a component shall not exceed 60 seconds

In some cases, this can lead to inaccuracies, which is not what is generally sought in specialized discourses, in particular in technical texts. Second, it is well known that nominalizations may be polysemic. Generally speaking, the two possible meanings concern the process on the one hand and the result on the other. As a consequence, the overuse of nominalizations may also create ambiguity. For example in (2):

2) The project leader will be responsible for the documentation it is not clear whether documentation concerns the writing of the documentation or the set of documents written during the project. In some cases, this ambiguity may lead to confusion.

These two phenomena, the overuse of nominalization and the underuse of prepositions, are two very frequent examples of linguistic economy in specialized languages but they can have unwanted consequences on readers' comprehension. Linguistic economy results from a tension between standard linguistic functioning and supposedly shared knowledge. In the case of specialised languages, the balance seems to tip in favour of belonging to a community of knowledge.

3 3-2 Deviancies caused by cognitive factors « Specialized language is an interesting area of application for Cognitive Linguistics. One might ask what is so special about specialized language, why it is different from general language, and why it is worth studying in itself » (Faber, 2012, XV) In this part, WEaddress a type of explanation for deviancy that is not commonly proposed, borrowing from both cognitive semantics and construction grammar. It concerns a phenomenon found in the fields of sports and hobbies (which have their own experts), namely the possibility of transitivizing the location complement and putting it in the position of a direct argument, for example to fish (a) river(s).

3) To fish rivers well requires a little more experience than to fish still waters well (https://fishingmagic.com/forums/threads/)

This possibility is well known in English and is registered in dictionaries (Collins for example), whereas in French, sentences such as (4) seem very odd to a non-angler and the construction is not mentioned in grammars or dictionaries: 4) J'aWedéjà pêché cette rivière (I've fished this river before) (private discussion)

Intrigued by this type of sentence in French, WEwanted to understand whether it was associated with a change of meaning that was more or less conscious for the speakers. It seemed to me that it could be possible that, with this direct construction, anglers express a feeling of proximity with the river, or in other words, whether it was the connection with the river that they preferred and that gave them the strongest emotions.

My first study on this question consisted in searching on the web, via Google, all the contexts in which "pêcher" (to fish) and "rivière(s)" (river(s)) were used, either linked with or without a preposition (dans, en, sur). For each identified context, WEnoted whether the site concerned was a site dedicated to fishing or not, and whether it was marked by a certain emotion (blog, forum...) (see [START_REF] Condamines | Quand la passion autorise la transitivation d'un circonstanciel de lieu[END_REF][START_REF] Condamines | The emotional dimension in terminological variation: the example of transitivization of the locative complement in fishing[END_REF] for more details). For the two languages, the chi-squared test showed a significant difference (p <.001) in the use of the preposition. Direct constructions (without a preposition) seemed to characterize fishing websites with an emotional dimension. These results appeared to confirm my hypothesis that there is a link between the direct construction and the fact that it is encountered mainly in angling websites with an emotional dimension.

One of the French extracts was very interesting from my point of view: 5) Il est bien plus intéressant de pêcher des rivières que des poissons (It is much more interesting to fish rivers than fish.) WE. (message published on October 28, 2009 on the website www.mouche-fr.com. The message is no longer accessible)

This extract encapsulates my argument exactly. When the preposition is deleted it is not just for reasons of linguistic economy but rather because the angler expresses a close relation with the river. Basically, what anglers like is the contact with the river more than just catching fish.

Associating a meaning to any change of form is part of the perspective of construction grammar:

"Grammatical constructions, like traditional lexical items, are pairings of form and meaning." (Goldberg 1995: 4) And for some of the proponents of this approach, linguistic constructions may be chosen in order to express an experience:

"A conceptualization or construal is simply a semantic structure for an experience. WEwill take 'experience' to refer to some aspect of the real world, or more accurately our human apprehension of it, and 'meaning' or 'semantic structure' to refer to a way of representing that experience that is relevant to linguistic formulations for that experience". (Croft 2012: 13).

The following study focused on the lexicon present in the environment of English structures for to fish, whether followed by a preposition or not [START_REF] Condamines | Is "to fish in a river" equivalent to "to fish a river"? A study at the crossroads of cognitive sociolinguistics and corpus linguistics[END_REF]. For this comparative analysis of the lexicon, all the contexts collected in the study of the two constructions (with or without a preposition) were used to build two corpora. The number of the two constructions was practically the same: 1108 without a preposition and 1094 with a preposition. Unsurprisingly, therefore, the two corpora contained almost the same number of words, making a good basis for the comparison. From here on, the complete contexts in which [To fish (det) river(s)] and [To fish prep (det) river(s)] occurred will be called "without-prep corpus" and "with-prep corpus" respectively. My hypothesis was that the lexical environment would provide clues concerning the meaning of the two constructions. This approach constitutes a kind of distributional analysis but it does not take syntax into account; while applied to small corpora in the present case, it can be likened to the ones described in Kilgariff (1997) and [START_REF] Rayson | Comparing Corpora using Frequency Profiling[END_REF]. This also corresponds to the corpus-based approaches recommended by cognitive linguistics [START_REF] Gries | The role of quantitative methods in Cognitive Linguistics: corpus and experimental data on (relative) frequency and contingency of words and constructions[END_REF].

Number of structures

After lemmatization with TreeTagger3 , the two corpora were explored with AntConc4 , using in particular the keyness function that gives the lexicon specific to one corpus compared to that of another. The Log-likelihood measure was selected. Following the recommendation of the AntConc toolkit, only the lemmas obtaining at least 3.84 (with p< 0.05) as a keyness score were considered. It was a little more difficult with some others such as, for the without-prep corpus:

Positive vocabulary: inspiring, ideal, beautiful, clarity, peacefulness… and for the with-prep corpus: Vocabulary related to hazards : chemical, lethal, polluted, danger, arsenic, decrease, threat... Most of the results are in line with what WEexpected, particularly the ones concerning positive vocabulary (more frequent in the "without-prep corpus") and the ones concerning legal and economic aspects (more frequent in the "with-prep corpus"). Some results are more surprising, for example natural elements and family relationshsips are more abundant in the "with-prep corpus".

It seems that what is important for expert anglers is the relationship with the river and different aspects favouring it: the time, the region, the method of fishing and the species of fish caught. They are not preoccupied by the risks, nor by the economic and legal aspects. And lastly, they prefer to fish alone rather than accompanied by a family member.

Here are some representative examples containing the two structures 6) It's not an exact science to fish a river, but in general terms WEprefer to run through for roach, and hold back for bream or other big fish (http://www.maggotdrowning.com/forum/topic.asp) 7) As a young child growing up in Alabama, WEremember the excitement and anticipation of planning trips to fish the rivers (http://forgottencoastadventures.com/meet-the-captain/) 8) In addition to holding a licence, you will also need to obtain the landowner's permission to fish in rivers and lakes (http://www.rodlicence.net/) 9) Reluctant to fish in the river and having no reassuring contact from authorities that the river's fish are not contaminated by either arsenic or antimony.

(http://mncgreens.blogspot.fr/2011/09/just-how-polluted-is-macleay-river.html)

Sometimes, anglers describe a close relationship with the river, either to fight it or to appreciate it. They then use the construction without a preposition.

10) It is hard to fish the river when the water is up high like it usually is in the spring from all the ice melting (http://walleyewarrior.20fr.com/) 11) On a whim, WEdecided to fish a river in Oregon WEhad visited before, and fell in love with.

(http://www.sweetgrassrods.com/boo-news/on-the-road-and-theriver.html?start=30) Of course, these are tendencies, not hard-and-fast rules. Anglers using with-prep constructions in legal websites can also use the without-prep construction. Nonetheless, there are several elements in favour of a difference of meaning in extracts with a preposition before river and extracts without a preposition.

Note that the direct construction with to fish, can also concern other names referring to bodies of water: lake, sea, ocean etc.

The field of fishing is not the only one concerned by this phenomenon. Other verbs also allow the direct construction; here are some examples found on the web: 12) How long before you're ready to sky the mountain?

(https://www.thesnowcentre.com/snowsure/news/howlongdoesittaketogetgoodatskiing) 13) We got in with them soon after they left the meet and tried to hunt a wood and some rough ground left for shooting (https://www.facebook.com/Bedsandbuckssabs/posts/)

On the contrary some verbs do not accept the direct construction, for example, no occurrences of « to dance a room » or « to box a ring » were found on the internet. Further work will be necessary to understand these impossibilities, taking into account the nature of the place (closed or not, moving or fixed etc...)…

Adopting the constructional point of view, we can say that the presence vs absence of a preposition corresponds to a difference in meaning, in the field of angling. In this case, and from a cognitive point of view, the difference seems to be due, in large part, to the presence vs. absence of emotion. Fishing, for a non-angler, means catching fish, legally, for eating or selling it. For an angler (i.e., a domain expert), it is a question of experiencing strong emotions when in contact with the river; the fish is only an intermediary.

Emotion is very rarely mentioned as playing a role in specialised languages:

"[…] theories of emotion which have been ignored by LSP research for a long time are of increasing methodological and methodical significance because they offer far-reaching strategic orientations for the communicative-cognitive analysis of information processing in LSP texts". (Baumann 2007, 322).

Yet, even in technical or scientific situations, it is difficult to imagine that there is never any emotion (excitement, stress, satisfaction...), nor any impact on language structures, in particular in oral exchanges. For example, in French, it seems very odd to say "voler un avion" ("flying an airplane") in the sense of "piloting an airplane". Yet it seems quite possible for example for an Airbus test pilot, enthusiastic about his/her job, to ask a colleague, "tu as déjà volé l'A-350 ce matin ?" ("you already flew the A-350 this morning?").

Put briefly, in this case, one can argue that emotion is the cause of the deviancy.

It can be noted that in the case of language economics (see section XX), it is also often the prepositions that are removed. In the case of to fish, it may be that the will to "keep it short" plays a role in the suppression of the preposition, but the cognitive explanation seems more likely. But in the fishing community, suppressing the preposition also becomes a sign of belonging. The sociolinguistic explanation is therefore also to be considered.

Another element that should also be taken into account is geographical variation. In his work, M. Cailles showed that the removal of the preposition was increasingly frequent in American English (for example, He graduated Harvard summa cum laude). it would probably be necessary to redo the study taking into account the geographical origin of the websites (when available) in order to see if this aspect plays a role in the removal of the preposition.

The question might then arise as to how emotion is taken into account in terminology studies.

Conclusion

Linguistic deviancy in terminology is a reality if one compares discourse produced within LSP with discourse produced in general contexts (if such exist). But as soon as we look closely at the situation in which these deviancies appear, we can show that there is a link between these linguistic structures and the situation in which they are produced.

Three aspects of this correlation have been proposed: linguistic, sociolinguistic and cognitive ones. In a sociolinguistic perspective, the notion of community of discourse plays a major role. Language plays a decisive role in that it is recognised as belonging to a community. This can go as far as creating jargon, sometimes only understandable by the members of the community. The linguistic perspective is twofold. In the case of linguistic prolixity, linguistic constructions are longer and more frequent than in the standard language. This is the case for compound nouns, as the addition of modifiers makes it possible to account for finer and more diversified concepts. In the case of language economy, some syntactic elements are removed because they are considered to be useless in a communication situation involving shared knowledge. Another example is the overuse of deverbal nominalizations in specialized corpora. Their use makes it possible to package more than one piece of information in the same form.

The cognitive perspective aims to show that "deviancies" often make sense and are not simply an alternative to the standard structure. The case of the removal of the preposition in front of the complements of place, which leads to putting them in the position of object is, in this respect, quite telling. The case of « to fish (prep) (det) river(s) » has thus been examined at length.

What the studies presented show is that specialized situations, which imply a shared interest in a domain, lead to language functionings that may themselves be specific ("deviant") because they are part of a particular experience. In Johnson'words:

« Empirical studies indicate […] that most human concepts are defined and understood only within conceptual frameworks that depend on the nature of human experience in given cultures » (Johnson, 1987, xi-xii).

The question that remains to be answered is to what extent these specificities should be taken into account when building terminology repositories.

Table 1 :

 1 Number of adjectives in [atmosphère(s) adjective] for each of the 4 sub-corpora in exobiology

	Biology	Chemistry	Geology

Table 2 :

 2 Table 2 presents the results concerning the proportion of nominalizations in each corpus. Distribution of Nominalizations, other nouns and verbs in a scientific vs news corpus in space research

		Scientific corpus News Corpus
	Nominalizations 19 %	10 %
	Other nouns	50 %	54 %
	Verbs	31 %	36 %
	Total	100 %	100 %

Table 3 :

 3 Table 3 presents the results obtained for the two corpora. Nominal groups containing a nominalization without a preposition, in the Scientific corpus and the News corpus

	Scientific Corpus	News corpus

Table 4

 4 summarizes the results for the 1474 cases encountered for French.

			Pêcher dans	Pêcher sur	Pêcher	Pêcher
			DET	(DET)	en	DET
			rivière(s)	rivière(s)	rivière(s)	rivière(s)
	All	the	45.6	9.2	27.5	17.7
	websites

Table 4 :

 4 Distribution of the French structures in all the Internet data and in angling websites that have an emotional dimension (percentages) WEhad the same study done for English. Table5summarizes the results for the 2202 cases encountered in English.

			To fish in (DET)	To fish on	To fish within	To fish (DET)
			river(s)	(DET) river(s)	(DET) river(s)	river(s)
	All	the	29.2	17.7	2.8	50.3
	websites
	Angling		9.9	16.5	4.4	69.2
	websites with
	an emotional
	dimension

Table 5 :

 5 Distribution of the English structures in all the Internet data and in angling websites that have an emotional dimension (percentages)

Table 6 :

 6 The corpus constituted by the contexts in which the structures with or without a preposition appear

	Number of
	words

Table 7 :

 7 Table7summarizes the data concerning the two corpora. The lexicon in the corpus containing the structures with or without a preposition Then the most specific lexicon occurring in each corpus was semantically categorized. The results are presented in Table8.

			Without prep	With	prep
			corpus	corpus
	Number of words 41361	40365
	Number	of	2715	2440
	lemmas
	Number of		322	319
	significant
	lemmas
	(with keyness >
	3.84)

Table 8 :

 8 Semantic categories in each corpus. In parentheses, the number of occurrences Some categories were easy to identify; here are a few examples for the without-prep corpus: Names of months or seasons: January, February, spring, summer… Names of States, regions or countries: Normandy, Nevada, Alabama … Names of fish: trout, pike, grayling, walleye, bream… Fishing tackle: tackle, accessories, wader, bait, nymphs, line, braids… And here are examples for the with-prep corpus: Natural elements: cormorant, flower, animal, reef, plant, crocodile… Names of family relationships: grandchildren, husband, ancestors, family… Vocabulary belonging to the legal field: permission, unlawful, license, permit, law… Vocabulary related to the economy: property, owner, landowner, leaseholder…

Termostat is a term-candidate extractor created by P. Drouin, OLST, Université de Montréal.it is available on the site http://olst.ling.umontreal.ca/?page_id=91&lang_pref=en

Verbaction was builts by N. Hathout (Univercity of Nancy) and N. Hathout (University of Toulouse). http://redac.univ-tlse2.fr/lexiques/verbaction_en.html

The TreeTagger is a tool for annotating text with part-of-speech and lemma information. It was developed by H.Schmid in the TC project at the Institute for Computational Linguistics of the University of Stuttgart. (https://cental.uclouvain.be/treetagger/)

Antconc is freeware corpus analysis toolkit for concordancing and text analysis developped by L. Anthony, Waseda University, Japan. (http://www.laurenceanthony.net/software/antconc/)