Member IEEE George Kariniotakis
email: kariniotakis@cenerg.cma.fr

Forecasting of Wind Parks Production by Dynamic Fuzzy Models with Optimal Generalisation Capacity

Keywords: Wind power, time-series forecasting, numerical weather predictions, on-line software, adaptive fuzzy-neural networks

On-line forecasting of the power output of wind farms is of major importance for a reliable and secure large-scale integration of wind power, especially under liberalized energy market environment. This paper presents such a prediction tool that receives on-line SCADA measurements, as well as numerical weather predictions as input, to predict the power production of wind parks 48 hours ahead. The prediction tool integrates models based on adaptive fuzzy-neural networks configured either for short-term or long-term forecasting. In each case, the model architecture is selected through non-linear optimization techniques. By this way the accuracy of the model on out of sample data (generalization) is optimized. The forecasting models are integrated in the MORE-CARE Energy Management Software (EMS) software developed in the frame of a European research project. In this EMS platform, wind forecasts and confidence intervals are used by economic dispatch and unit commitment functions. The paper presents detailed results on the performance of the developed models on a real wind farm using HIRLAM numerical weather predictions as input.

I. INTRODUCTION

Nowadays, wind park installations in Europe exceed 23 GW, while the motivated by the Kyoto Protocol targets of the E.U. for 12% energy demand covered by renewables by year 2010, are translated to 21% electricity generation by renewables. To achieve these targets, wind power in the Member States should arise up to 45-60 GW. Such a large-scale integration of wind power emerges the development of appropriate tools to assist the wind farm operators on their management task.

Of major importance are tools that forecast wind parks production for the next 24-48 hours. In a liberalised market environment, prediction tools enhance the competitiveness of wind power, since they reduce the penalties resulting from the wind resource intermittence.

Research on wind speed forecasting and, correspondingly the forecast of power output from a wind park, is actively pursued by several research centres in Europe.

Actually there are two main state-of-the art approaches; one based on physical modelling and a second one based on timeseries modelling.

The "physical" approach for wind power forecasting is based on a detailed description of the site (orography, roughness, obstacles), a description of the wind turbines (hub height, power curve, thrust curve) and a description of the wind plant. The main input is numerical weather predictions (NWP). Model output statistics are developed to account for systematic errors [START_REF] Focken | Reduction of wind power prediction error by spatial smoothing effects[END_REF]. Weather predictions are however updated only a limited number of times per day by meteorological services. For this reason, the performance of these models is often satisfactory for rather longer (>6 hours ahead) than short-term horizons.

The alternative "timeseries", or statistical, approach includes typical linear models (ARMA, ARX etc) [START_REF] Fukuda | The development of a wind velocity prediction method based on a Data-Mining type Auto-Regressive model[END_REF] and non-linear ones (i.e. neural networks, conditional parametric models, etc) [START_REF] Nielsen | Prediction of regional wind power[END_REF][START_REF] Kariniotakis | Wind power forecasting using advanced neural network models[END_REF]. These models aim to predict the future by capturing temporal and spatial dependencies in the data. The input to these models can be on-line SCADA data and numerical weather predictions (NWP). For look-ahead times more than ~10 hours, NWPs are indispensable for an acceptable performance, since they represent weather dynamics that cannot be modelled using only recent on-line data. For shorter horizons, up to ~10 hours ahead, timeseries models can be based exclusively on recent measurements; however even in this case, NWPs as explanatory input improves results. It is noted that the threshold of 10 hours is mentioned as an example rather than a rule, since it depends on the characteristics of a specific wind profile.

The models presented in this paper belong to the timeseries approach. In previous work, several types of models have been benchmarked on the wind power prediction problem [START_REF] Kariniotakis | Advanced Short-Term Forecasting of Wind Power Production[END_REF][START_REF] Dutton | Load and Wind Power Forecasting Methods for the Optimal Management of Isolated Power Systems with High Wind Penetration[END_REF][START_REF] Kariniotakis | Evaluation of advanced wind power and load forecasting methods for the o ptimal management of isolated power systems[END_REF]. Linear autoregressive models, radial basis functions, wavelet networks, feed-forward and recurrent neural networks, and finally adaptive fuzzy-neural network (F-NN) models were compared for the task of short-term prediction. Fuzzy neural networks, originally used here for wind forecasting, were found to outperform the other approaches in both short-term and long-term wind prediction.

This paper presents an advanced wind power forecasting tool developed at Ecole des Mines de Paris. This tool has been i ntegrated in the MORE-CARE Energy Management System (EMS) developed in the frame of a EU project. It is installed at the islands of Crete and Madeira, where it optimizes the operation of these power systems, and also in Ireland, where it operates as a stand-alone wind-forecasting platform for 11 wind farms. The system provides optimal forecasts for a horizon up to 48-72 hours ahead.

II. DESCRIPTION OF THE PREDICTION MODEL.

Adaptive fuzzy-neural networks are applied here for both short-term (<10 hours) and long-term (1-48 hours) prediction.

The adaptivity property stands for the capacity of the model to fine-tune its parameters during on-line operation. This is an important requirement for a non-stationary process like wind speed or power. The model adaptivity compensates changes in the environment of the application that may happen during the lifetime of a wind farm. Such changes can be changes in the number of wind turbines (extension of the wind farm, maintenance or availability of the machines that is usually not available through SCADA), in the performance of the wind turbines due to aging, changes in the surrounding of the wind park (i.e. vegetation), or changes in the configuration of the model used to produce the NWPs.

The core F-NN model is generic and can be trained on appropriate input depending on the final use, which can be either short-term or long-term prediction.

A. Short-term models.

Short-term models receive historic values of wind power as input, as well as explanatory data, such as wind speed and direction, to predict wind power. The general form of a simple model with input only past values of power is:

( ) ( ) ( ) ( ) ( ) m t P t P t P f t P - - = + , , 1 , 1 ˆK
The generic fuzzy-neural function f(.) is described in Section III. Multi-step ahead forecasts are generated using the model in an iterative way. I.e., in order to produce a forecast for t+2, the forecast for t+1 is fed back as input to the model. This approach presents the drawback that does not permit to iterate explanatory input, since no forecasts can be available for such quantities. To handle this problem, models using the lookahead time k as input variable can be considered.

An alternative approach is to develop multi-output models, or to tune a different model for each time-step. The implementation of this approach is complex and requires high development effort, which can be prohibitive in case of system with a large number of wind farms.

The short-term models are found to outperform persistence up to 20% according to the time-step [START_REF] Kariniotakis | Wind power forecasting using advanced neural network models[END_REF][START_REF] Kariniotakis | Advanced Short-Term Forecasting of Wind Power Production[END_REF][START_REF] Dutton | Load and Wind Power Forecasting Methods for the Optimal Management of Isolated Power Systems with High Wind Penetration[END_REF]. Such predictions are adequate for small applications, for which NWPs are not available, e.g. in the case of islands [START_REF] Hatziargyriou | The CARE system overview: advanced control advice for power systems with large-scale integration of renewable energy sources[END_REF]. In larger systems, timeseries models based on meteorological information, as the one presented below, outperform short-term models.

B. "Long-Term" models (1-48 hours ahead).

For "long-term" horizons up to 24-48 hours ahead, it is necessary to include numerical weather forecasts as explanatory input to the model in order to have an acceptable performance. NWPs include usually wind speed, direction and temperature at 10 m, as well as at several levels related to levels of atmospheric pressure. They can be provided for the geographical coordinates of the wind farm or for a grid of four points surrounding the farm. In the second case, the spatial resolution of the NWP model is of primary importance. Meteorological models with high resolution are often more accurate but require high computation time to produce forecasts, and as a consequence, they do not update frequently their output (i.e. 1-4 times per day). In contrast, forecasts from low-resolution NWP models are more frequently available.

The developed forecasting tool is able to operate with input from different NWP systems. In the frame of this study it was tested and gave satisfactory results with input from the SKIRON system for the case-study of Crete, and also from the HIRLAM NWP system for the case of Ireland. SKIRON forecasts were provided for a grid of 15x15 km (System B in Figure 1), while HIRLAM predictions were provided at the level of the wind farm (System A in Figure 1).

Forecasts are generated every hour for the next 2 days. At the moment of update, the most recent available NWPs are used as input to the model together with measurements of wind power. Eventually measurements of wind speed or direction can be used as input. Model configurations that do not include such online information as input were found to perform worse than persistence in look-ahead times up to 6 hours ahead. Wind power data are necessary for the on-line updating procedure, independently if they are used or not as input variables to the model. The general scheme of the model is shown in Figure 1.

Figure 1 : General scheme of the "long-term" prediction model with examples of two configurations of NWPs used as input (SKIRON,HIRLAM).

The aim of the prediction model is to capture the relations between input (meteorological information, on-line data) and output (future total wind park power). Such mapping includes the following implicit relations:

• Temporal correlations between past and future data of the process (autoregressive aspect of the model).

• Conversion of wind speed (meteorological predictions) from the height or the atmospheric level of the NWP model to the hub height.

• Spatial projection of the meteorological wind speed forecasts from the NWP grid points (eg. 15x15 km) to the level of the wind farm.

• Correction of the wind park output for factors affecting the total production (i.e. array effects, effect of wind direction etc). The advantage of a model like fuzzy neural networks compared to a physical one is that it permits to avoid all the above intermediate modeling steps. Moreover, its adaptive mode can compensate situations like the ones explained in the previous Section.

The above mapping relations introduce inaccuracy in the modeling procedure. Among the difficulties, one should add the error of weather forecasts, without neglecting the intermittent nature of wind itself. Wind speed is a nonstationary process both in the mean and in the variance. Wind power is nonlinear w.r.t. speed with a major difficulty in the area of cut-off speed, where prediction intervals can extend from maximum to zero wind power.

III. MODEL DEVELOPMENT AND GENERALIZATION.

A. General description of the fuzzy-neural network model.

The fuzzy model can be expressed in the form of rules of the type:

"IF x is A THEN y is B"
where x, y are linguistic variables and A, B are fuzzy sets. In the case of time-series prediction rules may have the form:

( ) R x A x A y g x x n n n
: IF is and and is THEN

1 1 1 , , , , K K =
where: x 1 ,…,x n are real-valued variables representing input variables of the system defined in the universes of discourse X 1 ,…,X n respectively. A 1 ,…,A n are fuzzy sets. y is variable of the consequence whose value is inferred. In the specific problem it represents future wind power ( )

( ) K ), 2 ( , 1 ˆ+ + t P t P . g(.)
is a function that implies the value of y when x 1 ,…,x n satisfy the premise. The function g(.) in the consequent part of the rules may be a linear or a non-linear one or even a constant. In the case of a linear function the fuzzy rule-base takes the form:

R x A x A y p p x p x R x A x A y p p x p x n n n n m m n n m m m m n m n 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 : IF is and is THEN : IF is and is THEN , , , , K K M M K K = + + + = + + +
Each rule gives an estimation of the output y i according to the conditions defined by the fuzzy sets in the premises. In the context of timeseries prediction, each variable x i in the premise corresponds to a past value of the process (i.e. power: P(t), P(t-1)…), or past values of explanatory input (i.e. wind speed: WS(t), WS(t-1)…) or meteorological forecasts (WS m (t+1), WS m (t+2), …).

A linear function in the consequence is indeed an ARX (autoregressive with exogenous variables) model. It is clear that with the above definitions, the rule-base consists of an ensemble of "local" models. Local modeling is a desired property of the model, especially in the case of a nonstationary process such as wind generation.

Fuzzy sets in the premises are modeled here using Gaussian functions:

( )               - - = 2 exp i j i j j j A b a x x i j µ 1 0 5 10 15 slow medium f ast speed (m/s) 0 µ(speed )
Figure 2: Representation of fuzzy wind speeds. "Speed" is a linguistic variable with three terms "slow", "medium", and "fast" represented as fuzzy sets with the membership functions shown in the Figure.

In the case of a linear function in the consequence, the model may be written analytically as following:

( ) ( ) ∑ ∑∏ ∑ ∏ ∑ = = = = = = = ⋅       + = r i i i r i m j j A r i m j j A m j j i j i y w x x x p p y i j i j 1 1 1 1 1 1 0 μ µ B. Learning and Generalization.
Model building is characterized by two phases: optimization of the model architecture and tuning of the model internal parameters (learning).

These two phases are driven by the requirement for good "generalization". Generalization is the capacity of the model to perform well when it predicts new data (data not used during the two phases of model development). Consequently, it is a primary requirement for the on-line use of a model.

The tuning of the model parameters is performed taking into account [START_REF] Kariniotakis | Contribution to the development of an advanced control system for the optimal management of autonomous winddiesel systems[END_REF]:

• Learning rules based on stochastic gradient for tuning the parameters a, b, p of the model.

• Learning rules are appropriately developed to minimize simultaneously prediction error and the Information content of the model (max entropy). This acts as a selfregularization process that permits to avoid overfitting of the data.

• Simulated annealing is performed for controlling the evolution of the learning process through appropriate adaptation of the learning rate.

• Early-stopping is applied to the learning process in order to avoid overfitting.

• Cross-validation is applied to terminate learning. For this purpose, a subset of the data (validation set) is reserved.

• The cross-validation criterion is expressed as a weighted function of the performance of the model over the whole prediction horizon. By this way, generalization is optimized for multi-step ahead prediction. The above process permits to tune optimally a model with a specific architecture. The architecture of a model is defined by the types of input variables and the number of fuzzy sets associated to each one. For each type of measured data it is needed to decide the number of past values to be used as input. When NWPs are considered as input it is necessary to select the relevant information (forecasts of wind speed, direction, etc) for the model.

This selection procedure, which is also similar to other types of models like neural networks, is a time consuming one due to the infinite number of combinations that can be evaluated. Often it is performed by trial-and-error, where several candidate configurations are tested. It is noted that the evaluation of each candidate model requires carrying out the above-described learning process.

In this work, the trial-and-error has been replaced by a fully automated process for model architecture optimization. The constrained nonlinear simplex ("Complex") optimization algorithm is used for this purpose. The algorithm has been modified for handling both discreet and continuous decision variables. The optimization process is based on the evaluation of the surface of the generalization function (defined as the performance of a model on the validation set) using a complex of points. Each point corresponds to a candidate model. The computational cost is high due to the necessity of the algorithm to tune each candidate model. However, in global, the automatic nature of the process permits to save considerable engineering time compared to the trial-and-error.

An alternative genetic algorithm approach did not present any advantages with respect to the simpler "Complex" algorithm. Genetic algorithms appeared to be less parsimonious w.r.t the number of models they need to test in order to converge compared to the Complex algorithm. Each decision variable in Complex represents the number of fuzzy sets associated to each type of input data. In the special case, when the algorithm converges to zero-number of fuzzy sets for a specific type of data, then this input is excluded from the model as non-significant. By this way the algorithm performs input selection. When the number of fuzzy sets is converging to one, then the variable does not participate in the premises, but appears only in the function of the consequent part. Parsimony in the selection of input is critical to avoid overfitting by overparametrized models. When the number of fuzzy sets for all variables is either one or zero then a single "rule" is obtained. The premise has no significance and the model corresponds to a simple linear function of the input variables. This limit case corresponds to the ARX class of models. Consequently, the optimization process can indeed exclude the use of a nonlinear fuzzy model and lead to a classical linear one. In this way, a selection between linear and nonlinear models is performed.

IV. RESULTS

The case study of a real wind farm in Ireland is presented. This farm contains 20 wind turbines of 300 kW each and 1 turbine of 450 kW. Online data and Hirlam forecasts have been used covering the period between 5 th February 2001 to 31 st March 2002. The time series cover a period of 10000 hours from which 6600 were used for training (learning set), 1000 for cross-validation and 2400 (100 days) for testing the performance of the model. The results presented here are on the testing set. Figure 4 shows the distribution of prediction errors for various look-ahead times. Figure 5 compares the performance of the fuzzy model with that of "Persistence" ("wind park production in the future is same as wind park production now", ( ) ( )

t P k t P = +
ˆ) on the testing set. Persistence is a simple method that is used as reference to evaluate advanced models. An advanced method is worth to implement for on-line operation only if it manages to beat Persistence. The F-NN model outperforms Persistence for all time steps. The Improvement or Skill of the F-NN model w.r.t. Persistence rises up to 55% according to the time step. The improvement for look-ahead time k is given by (i.e. for the NMAE criterion):

( ) ( ) % 100 ) ( ) ( ⋅ - = - k NMAE k NMAE k NMAE k skill P NN F P
An important issue is that this performance is positive for all time steps. This is one of the main benefits of this approach compared to other approaches like the "physical" one, which starts to have an improvement w.r.t. Persistence only after 3-5 hours ahead. A good performance in the shortterm is required in several applications; i.e. in the case of a spot market where a bid for the wind production has to be made 1-3 hours in advance.

A practical problem that arises is related to the fact that there is a delay on the release of the NWPs by the meteo service. This delay can be in the order of 3-6 hours. One could argue that due to this delay the advantage of a statistical model over the first hours is lost since after 3-6 hours physical models start also to outperform persistence. This is not however true; the evaluation against Persistence is often done in a different way by statistical and physical models and thus the statistical criteria in the two cases are not directly comparable. Statistical models, as the ones presented here, produce new predictions every hour and operate with a sliding window scheme, while physical models usually produce predictions only when new NWPs arrive (i.e. every 6 hours). A fair comparison would require to simulate predictions by the physical model using a sliding window scheme and to compare with appropriate persistence forecasts.

In the short-term (1-6 hours) the inclusion of meteorological forecasts contributes to a better performance (twice as good) compared to that of the short-term models that do not consider meteorological forecasts as input. Figure 6 shows the performance of the model for the examined wind farm on a specific situation. This is a typical situation where for the first 24 hours the performance of the model is good, while it becomes lower for horizons more than 24 hours. In Figure 7, it is demonstrated how the model exploits explanatory input information to improve its performance. As shown in the upper figure, the model predicts the same level of power (~5 MW) for the look-ahead times 1-3 and 37-43 although wind speed in the second case is higher. On the other hand, for look-ahead times 6-13, the predicted power decreases despite the speed increase. This means that during the two periods of 7-13 and 37-43, the model predicts less power than what would be expected by a direct consideration of Hirlam speed. This is in fact due to the influence of the wind direction on the wind farm production (in both cases wind direction is around 120 degrees).

Figure 8 shows a comparison between the performance of the fuzzy model and a simple model based on the conversion of the Hirlam wind speed at 10 m to the hub height and then to power using the machine power curve. The simple model under-predicts significantly power during the first hours. In contrast the fuzzy model predicts a higher power for the first lower peak of the Hirlam wind speed and captures the shape of the real curve.

V. CONCLUSIONS

The paper presents an adaptive fuzzy-neural network model developed for the prediction of the power output of a wind farm. The architecture of the model is optimized using the nonlinear simplex algorithm. The paper presents evaluation results on a wind farm in Ireland. Online and Hirlam data of one year were used for this purpose. The performance of the model is found to outperform both persistence as well as simple methods. The prediction module was developed in C++ using ODBC functionality and integrated in the More-Care EMS software -Figure 9. Several methods were tested and implemented for estimating the uncertainty of the predictions [START_REF] Pinson | Wind Power Forecasting using Fuzzy Neural Networks Enhanced with On-line Prediction Risk Assessment[END_REF]. The software is installed for on-line operation at the islands of Crete and Madeira, where its performance is currently under evaluation. Since December 2002 it is also installed for on-line operation in Ireland for predicting the output of 11 wind farms. Figure 10 shows a display of the platform as configured to run in Ireland.

The above work permitted to assess the performance of advanced prediction techniques for wind power forecasting. In order to achieve higher levels of accuracy, substantial R&D efforts are necessary. In the frame of the European Project Anemos (ENK5-CT-2002-00665) which started in October 2002 (http://anemos.cma.fr) several fields of research in the area will be addressed for both onshore and offshore wind power prediction.

Figure 3

 3 shows an example of a run of the Complex algorithm. 115 candidate models are totally examined. The input selection is performed among past values of wind power and Hirlam wind speed, direction and temperature forecasts. The upper left figure shows the evolution of the Complex objective function. Each point in the figure corresponds to the "generalization" performance of a candidate model on the validation set. The rest of Figures show the number

  of fuzzy sets associated by the algorithm to some of the input variables

Figure 3 :

 3 Figure 3: Evolution of the algorithm for the model architecture optimization.

Figure 4 :

 4 Figure 4: Distributions of prediction errors for various look ahead times (+1h, +2h, +3h, +4h, +36h, +42h).

Figure 5 :Figure 6 :

 56 Figure 5: Normalized Mean Absolute Error (%) and Root Mean Square Error (%) as a function of look ahead time. Comparison between Persistence and the advanced model. Normalization is made using the wind farms nominal power.

Figure 7 :

 7 Figure 7: Role of wind direction for adapting predictions.

Figure 8 :

 8 Figure 8: Comparison of predictions obtained by the F-NN models and a simple model that converts Hirlam forecasts at 10 m to the hub height.

FORECASTINGFigure 9 :

 9 Figure 9: Architecture of the forecasting software.

ACKNOWLEDGMENT

This work has been performed in the frame of the MORE-CARE project (ERK5-CT1999-00019) supported by the European Commission. The authors would like to thank ESB National Grid for making available the data for the case studies.

BIOGRAPHY

George Kariniotakis was born in Athens, Greece. He received his production and management engineering and MSc degrees from the Technical University of Crete, Greece and his PhD degree from Ecole des Mines de Paris in 1996. He is currently with the Center of Energy Studies of Ecole des Mines de Paris as a scientific project manager. He is a member of IEEE. His research interests include renewable energies, distributed generation and artificial intelligence.


Macrosyntactic corpus annotation. The case of Zaar

Keywords: Oral Corpus Annotation, Macrosyntax, Universal Dependency Grammar, Identifying Clause, Topic

teaching and research institutions in France or abroad, or from public or private research centers.

Macrosyntactic corpus annotation.

Introduction

This article argues that morphosyntactic glossing of oral corpora is not sufficient for languages with little morphology. A minimal annotation system must be introduced to represent in a simple and concise way the interface between information structure and syntax.

The article introduces the concept of macrosyntax, based on illocutionary units, for this specific level of annotation using morphosyntactic tiers in Elan. With the corresponding annotation script, a pilot 90 min (15,000 words) corpus has been annotated for Zaar, a Chadic language spoken in Nigeria and a preliminary study of peripheries in this language has been done on this annotated corpus.

The article is organised as follows: after this introduction, the second section presents the Zaar language and the corpus collected; the third sections highlights three properties of oral data (dysfluencies, afterthoughts and coordination over turn-taking) that support the need for a different approach to the syntax of oral corpora. Section 4 introduces the concept of macrosyntax and illocutionary units as the foundation of this different approach. Section 5 shows how this approach enables us to annotate, analyse and represent a morphosyntactic ambiguity between left-dislocated topics and unmarked identifying clauses in Zaar. Section 6 introduces an attempt at tagging the Information Structure function of Illocutionary components in Elan. Finally, section 7 establishes a typology of illocutionary components in a 15Kw (15,000 word) oral corpus of Zaar.

Zaar and the Zaar corpus

Zaar, also known as Saya, is spoken by about 150,000 speakers in the South of Bauchi State (Nigeria), in the Tafawa Ɓalewa and Ɓogoro Local Government Areas. Together with 30 or so other related languages first identified by [START_REF] Shimizu | The Southern Bauchi Group of Chadic Languages: A Survey Report[END_REF], Zaar forms a sub-branch of West Chadic languages named the South-Bauchi languages. 1 Apart from the dominant languages, i.e. English (official national language) and Hausa (dominant all over Northern half of Nigeria), South Bauchi languages are surrounded by Niger-Congo languages. Two isolates inside South-Bauchi languages are Bankal in the North and Ɓoi in the South. Most Zaar 1 [START_REF] Newman | Nominal and Verbal Plurality in Chadic[END_REF] classified South-Bauchi languages as the B3 sub-branch of West Chadic, but he now treats these languages as a third sub-branch (West-C) within West Chadic [START_REF] Newman | Comparative Chadic revisited[END_REF]2013). people of the younger generation are Hausa-Zaar bilinguals. They are schooled in Hausa in primary school, before learning English. From a typological point of view, Zaar is a SVO language where TAM is conflated with the exponent of the subject function into a pre-verbal pronominal clitic, as in Example 1:

(1) ka ɓəl ɬərtín // ka ɓəl ɬərti -ín 2SG.FUT dig root PROX

Dysfluencies

A common configuration of oral corpora that needs to be accounted for concerns dysfluencies, as in (2). The hesitations of the speaker result in repetitions: kə ́ | kə , separated by a pause (#), and teː | teː, separated by a pause filler (ɣə ):

(2) a Tôː kə ́ # kə ́ ɗû teː ɣə ́ teː gə ̀ʃi tsə ́n ŋ.

This type of dysfluencies is pervading oral performances, and has to be taken into account in our description of African languages. An easy solution would be to tap into the speakers'

"competence" and ask them to rephrase the sentence, removing the "mistakes" so that it can 3 Zaar is transcribed using the International Phonetic Alphabet, except for /j/ which is transcribed /y/.

Vocalic phonemic length is marked after the vowel by single colon (ː). Phonetic length (in fillers, emphasis, etc.) is marked with three colons (ːːː). Phonemic tone is marked with diacritics: á, à, â and ǎ for High, Low, Falling and Rising respectively. Mid tone is left unmarked. fit into our descriptive frameworks. However, these so-called mistakes are traces of cognitive processes (reformulation, etc.) that are meaningful and need to be documented.

Such a need was integrated into the work that was initiated in the 1970's in France by Claire Blanche-Benveniste and the Groupe aixois de recherches en syntaxe (GARS) with the introduction of the concept of piles (entassements). [START_REF] Blanche-Benveniste | Le français parlé: études grammaticales[END_REF] analysed dysfluencies and reformulations as a paradigm occupying one syntactic slot, and working like coordination structures. A visual representation using their annotation system is used for our Zaar example in (2b):

( This presents the advantage of reintegrating dysfluencies into syntax, and account for their role in discourse (reformulation, specification, elaboration of thought). The syntactic structure can now be represented e.g. by a dependency graph, as in Figure 1. A GU has a head, which is not governed, e.g. the verb ɗu, 'beat' in (2), and all the elements of the GU are dominated by this head. In other words, a GU is the maximal projection of a non-governed lexeme.

This syntactic analysis includes elements (e.g. dysfluencies and reformulations, tagged "conj:dicto"5 ) that are usually discarded, which justifies the name "macrosyntax", and the decision to take this all-encompassing GU as the basic unit of our description.

Afterthoughts

Afterthoughts are another example of the specificities of oral language data, as exemplified in

(3):

(3) In this example, the first intonation unit finishes with the final adverbial adjunct ɗaŋ, 'too' and the end of the unit is marked with a terminal prosodic break. Then, as an afterthought, two nouns are added, forming with the direct object of the previous intonation unit a discontinuous chain of three coordinated direct objects (gyáː gàːl, 'cows; gèri, 'hens' and maːt, 'goat') of the verb ngyáːr 'slaughter'. The afterthought forms a second intonation unit starting with a pitch reset and finishing with its own terminal prosodic break.

As indicated in section 3.1, the GARS representation of dysfluencies with piles is used for coordination as well. The coordination operating over the final break of the first intonation unit can be represented as in (3b), where the coordinated elements are in a paradigmatic This however poses a dilemma: if one follows the prosodic cues, the constituents of the second intonation unit are syntactic orphans without governor. If one follows the macrosyntactic structure, they are coordinated to gàːl, 'cow', and inherit their syntactic function from this link, but there is a discrepancy between the intonation and syntactic units. This militates in favour of an analysis called macrosyntax that can account for the phenomena characterising oral corpora. The macrosyntactic level describes the whole set of relations holding between all the segments that make up one and only one illocutionary act:

The accomplishment of an illocutionary act is the main property that a language event must have in order to be considered an utterance. […] From an operational point of view the utterance can be defined as the minimal linguistic unit such that it allows a pragmatic interpretation in the world. Cresti & Moneglia (2005: 16) Figure 2 represents this analysis in a UD graph. NB: the governing link that goes from gàːl 'cow', the first element of the pile, to geri 'chicken', the second one, cuts across the advmod link linking ɗan 'too' to its governor, gyáːr 'slaughter', the verbal root of the GU. This feature translates the anomalous position of the adverbial modifier ɗan which should occur in final position, and represents the anomalous syntactic structure of the afterthought.

Syntactic relations over turn-taking

Coordinated piles can occur across turn-taking and result in elliptic structures. But instead of considering those as either incomplete structures or structures where most of the elements have been omitted, they are represented as a special case of coordination across turn-taking, and another case of syntactic relation outside the sentence, hence the use of the term macrosyntax.

It is illustrated in (4) below, part of a passage where the first speaker [S1] is interviewed by

[S2] about funeral rites. In this example, the nouns gə ̀t 'woman' in (4a and e), and ŋaː gə ̀t 'girl' in (4c) are part of the same pile that spreads over several turn-takings, and share the same syntactic properties as initially stated in (4a). This utterance in (4a) is divided in two parts: the nucleus tá gìː tə ̀ gòs ɗòː? 'where will they bury her?' and the pre-nucleus tô gə ̀t kən yáː mə̂s kúmá 'well if it is a woman that dies', a conditional dependant clause whose subject gə ̀t, 'woman' is identified by the copula nə, 'be'. The identified element is coordinated over several turns of conversation without repeating the rest of the (4a) initial utterance.

( The different macrosyntactic models acknowledge that sequences such as tá tûːr gyáː ɗûː and tá tûːr gyáː náɣat in (5) have to be considered as forming a cohesive unit at some level of linguistic description that should be accounted for.

Macrosyntactic corpus annotation

The elements that make up the specificity of oral corpora need to be annotated so as to be retrieved when analysing the corpora and incidentally in order to improve the training of automatic taggers and parsers. To do this, macrosyntactic units in Zaar are annotated with the script developed in the ANR Rhapsodie project (Corpus de français parlé annoté pour la prosodie et la syntaxe; [START_REF] Lacheret | Rhapsodie: a Prosodic-Syntactic Treebank for Spoken French[END_REF]. This script has proved to be particularly efficient in dealing with the specificities of oral corpora, e.g. piles, dysfluencies, repetitions, discourse markers, overlaps, co-enunciation, false starts, self-repairs and truncations. This method is data-driven, inductive (the relevant units are identified through annotation) and modular.

The macrosyntactic level describes the whole set of relations holding between all the segments that make up one and only one illocutionary act. The macrosyntactic punctuation marks macro-syntactic boundaries (i.e. illocutionary units and their main components: nuclei, pre nuclei and post nuclei, including discourse markers) and limits between pile layers (dysfluencies, reformulation, coordination).

Illocutionary Units and basic Illocutionary Components

Each text is segmented into a string of illocutionary units (IlU); each IlU is composed of 3 kinds of Illocutionary Components (IlC): a nucleus (obligatory), pre-nuclei (optional) and post nuclei (optional). Heuristically, in order to identify IlUs and IlCs, annotators rely on prosodic cues perceived while listening to the data that is annotated. Perceptively relevant prosodic cues enable them to identify terminal and non-terminal breaks, the former constituting the IlU limits. They are defined as follows (Cresti & Moneglia 2005:17):

a. Prosodic break: perceptively relevant prosodic variation in the speech continuum such as to cause the parsing of the continuum into discrete prosodic units.

b. Terminal prosodic break: given a sequence of one or more prosodic units, a prosodic break is considered terminal if a competent speaker assigns to it, according to his perception, the quality of concluding the sequence.

c. Non-terminal prosodic break: given a sequence of one or more prosodic units, a prosodic break is considered non-terminal if a competent speaker assigns to it, according to his perception, the quality of being non-conclusive.

The basic prosodic distinction in Zaar is between pre-nucleus units whose boundary is characterised by a level intoneme followed by an initial step-up (pitch reset) at the onset of the following unit; and final prosodic breaks signalling the end of an IlU by a fall. The final fall can be replaced by or combined with other intonemes (e.g. rise and high-rise) in case of emphasis or exclamation (see Caron 2015b:17).6

IlCs are annotated as follows: "<" follows a pre-nucleus and precedes a nucleus or another pre-nucleus; ">" precedes a post-nucleus and follows a nucleus or a previous post-nucleus;

and "//" indicates the right boundary of an IlU.

Nuclei

Nuclei bear the main prosodic prominence in the utterance. They are identified as the locus of the illocutionary force. Nuclei are usually governed by a tensed verb, as in ( 6), but not always.

See e.g. ( 7) where a noun (laː, 'work') is governing the nucleus, and (8) where the whole nucleus of the second IlU is an exclamation (kâːy 'hey').

(6) féːlêks < kyâːn máː < káː rigá kə yisə ́n tíː "áy" //

Pre-and post-nuclei

Pre-nuclei include topics, left-dislocated adjuncts, IlU introducers and associated IlUs.

Example 9 shows an instance of a pre-nucleus topic and a post-nucleus associated IlU:

(9) ndàːɗə̂m máː < má ɬə ́ yéltə ̀ > "áy" // ndàːɗə̂m máː má ɬə yel =tə áy Ndadəm even 1PL.FUT go see =3SG.OBJ eh 'Ndadem too < I will go and see him > "eh". //' (Girls_A_005)

Post-nuclei include right-edged topics, afterthoughts, associated IlUs and backgrounded elements in marked identifying clauses (see Section 5).

IlU introducers

Illocutionary Unit introducers are conjunctions like 'but', 'then', 'since', etc. They specify the nature of the relation between the IlUs they introduce and other IlUs in the discourse, especially the preceding one; they have no syntactic dependency with any other elements in the IlU; they are blocked to the initial position of the IlU. Subordinating conjunctions can work as an IlU introducer when the clause that follows is not integrated into a higher IlU. In the macrosyntactic annotation, they are preceded by a circumflex accent.

(10) ^séː ^ɗan tə ́ mǎni // séː ɗan tə ́ mán -i then then 3PL.AOR come -INDF 'Then, they came back.' (Mbrt_S1_114)

Associated Illocutionary Units

These are expressions like 'well, you know, I think', etc. They are not autonomous IlUs but they are not part of the dependency frame of the predicate in the nucleus. 7 Together with pause fillers, the most frequent associated IlU in Zaar is tôː, 'well'. In the macrosyntactic annotation, they are written between quotes: (11) "tôː" < zəgì < ʧi gòs < dzàŋ gyòː // tôː zəgì ʧi gòs dzàŋ gyôː well Ziggy 3SG.be 3SG.POS day which ' "Well" < Ziggy < as for him < when was that?' (Boys-A_457)

Piling

Kahane & Pietrandrea (2012) propose a complete annotation and a functional tagging of piles.

The format is: { -| -}. See (2c) below for the annotation of Example 2 in Section 3:

(

Non-alignment of Illocutionary Components and Governing

When all the constituents belonging to the dependency frame of the root of the IlU are contained inside the nucleus, IlUs and GUs are aligned. When they are not aligned and dependency links cross illocutionary boundaries, these are followed by a "+", e.g. "//+", "<+"

and ">+". In these cases, neither Intonation Unit (IU) boundaries nor turn-taking work as boundaries of syntactic dependencies. The annotation of piles is modified to mark the resulting discontinuous piling as follows: { ---|} {| ---}.

Piling across Intonation Unit boundaries

Example 3 of Section 3.2 on afterthoughts showed an example of piling across IU boundaries.

It is annotated in (3c) to represent the piling that links the nouns in the afterthought to the direct object of the nucleus across the IU boundary:

( '"Well" it's that day>+ they will do matang. //+ If it is me. //' (Bury_Ha_201) NB: ( 13) is an utterance with an identified structure, where the ">+" sign indicates that the post-nucleus unit is in a dependency relationship with an element of the nucleus.

Piling across turn-taking

In the so-called elliptic constructions seen in section 3. The adjunct is dependent on the verb it modifies. It contributes to the semantic component of the nucleus by restricting the circumstantial scope of its referential value. When a circumstantial adjunct appears in pre-nucleus position (left-dislocation, PR-Adv), it keeps a direct dependency relation with the verb, and no clitic or resumptive element is needed. This is marked in our macrosyntactic punctuation by the plus sign added to the chevron (<+).

Another tests is given by their ability to be cleft. Clausal adjuncts, whether circumstantial or conditional share the prosodic and syntactic properties of adverbial adjuncts, and are annotated in the same way (<+).

(

) 16

20

'If she comes <+ she will suffer > indeed. //' (Boys_B_289) 5 Left-dislocation and marked identifying clauses in Zaar I have argued in the preceding section that macrosyntactic corpus annotation is necessary to account for specific phenomena pertaining to oral corpora. In this section I argue that macrosyntactic analysis, annotation and representation of illocutionary units provide the tools to disambiguate utterances where morphosyntactic tagging alone cannot differentiate between e.g. left-dislocated topics and marked identifying clauses, also known as "it-clefts" in English syntax.

Topics

Topics are pre-nucleus IlCs that introduce a referent, selected out of the on-going conversation, or of the common knowledge of the speakers. These referents provide the necessary pragmatic information for the illocutionary act carried by the following nucleus.

Topics do not enter in a microsyntactic relationship with the verbs of the nucleus. 9 When a topic is in a pragmatic relation with the verb, and this relation is part of the dependency frame of the verb (the element could be an argument of the verb), the syntactic relation must be realised as a clitic so that the valency of the verb is saturated. In (18), the topic gə ̀ːrí raːs, 'old locust-bean tree' is co-referential with the adverb ɗanì, 'there', an adjunct of the verb gìː, 'bury '. In (19), a clitic (the direct object pronoun ʃí, 'them') saturates the dependency frame of the verb gìː, 'bury'. 9 Microsyntax concerns dependency between a head and elements instantiating its dependency frame, e.g. the complements of a verb. Microsyntax is a subdomain of macrosyntax. This points to the fact that topics, separated from the nucleus by a prosodic break, do not enter in a microsyntactic relation with the verb or the predicate of the nucleus, and their relation to the nucleus is not microsyntactic but pragmatic. In other words, the repetition of the topic or the presence of a resumptive pronoun confirms the non-compositional nature of topics, working as a syntactic island (see Cresti & Moneglia 2005:34-38). Their relation to the nucleus is pragmatic, and is best defined by the notion of "aboutness" (see [START_REF] Sperber | Relevance: communication and cognition[END_REF][START_REF] Cresti | The definition of Focus in Language into Act Theory (LACT)[END_REF]). The information function of the topic is to identify, through linguistic means, the domain of relevance for the illocutionary force carried by the nucleus, its pragmatic domain of identification. This is conveyed by the name "aboutness topic" commonly used to refer to this construction (see [START_REF] Krifka | Information structure: Overview and linguistic issues[END_REF][START_REF] Schultze-Berndt | About the shifty notion of contrast. Identifying subtypes of topics in corpus data of two Australian languages. Oral presentation at Labex TCA-ISGR[END_REF][START_REF] Simard | Another look at right-detached NPs[END_REF].

22

Marked identifying clauses in Zaar

Marked identifying clauses are a subclass of identifying clauses as defined by [START_REF] Halliday | Notes on Transitivity and Theme in English: Part 2[END_REF] in his series of articles on Transitivity and Theme in English. They translate in English as what is usually called "it-clefts" and are exemplified in (20). 10 (20) máː nə zəgì átâ wul vèːsòː máː nə zəgì átâ wul vìː -ês -oː even COP1 Ziggy 3SG.REM say mouth -DEF -ASS Actually, it is Ziggy who said it.

Halliday explains that any clause such as John saw the play can be organised into a "cleft sentence" with equative form through the nominalisation of one set of its elements, e.g. what John saw was the play. The former, without the nominalisation, is non-identifying and the second is identifying. The identifying clause adds the further information that one of the participants is definable by participation in the process. In an identifying clause, it is always the nominalization which is "to be identified". A further division is made by Halliday between

Marked and Unmarked Identifying clauses:

There is thus an association of variablevalue with themerheme similar to that of identifiedidentifier with givennew: in the unmarked case, the identified is given, the identifier new, and the variable is theme, the value rheme. […] in a sense a theme is a variable to which a value is to be assigned.

But as always the speaker may exploit the contrastive possibility of not 10 Marked identifying clauses are often described under the name it-cleft, after [START_REF] Higgins | The pseudo-cleft construction in English[END_REF], referring to their morphosyntactic exponents in English. Cf. for example [START_REF] Huddleston | The Cambridge Grammar of the English Language[END_REF]:1414) where it-clefts are defined as "a bi-clausal copulative construction consisting of an impersonal pronoun (the cleft pronoun), a copular verb, the informationally prominent phrase (the cleft phrase) and an embedded relative clause (the cleft clause)". However, Zaar does not have an impersonal pronoun corresponding to the 'it' of English "it-clefts", and the copula can be omitted. This is the reason why I opted for Halliday's semantic approach, as less language-specific.

mapping the variable on to the theme; hence to the unmarked, operative [Type(1) what John saw was the play] corresponds a marked, receptive form

[Type(2) it was the play that John saw]. (Halliday 1967:228 22), the copula nə 'be' does not have an expletive pronoun. This is the rule for the two copulas used in Zaar for identification with the meaning '(it) is X': nə X (COP1, in ( 23)) and X kən (COP2, the most frequent, in ( 24 (23). This shows that the Identifier is still in a microsyntactic relation with the predicate of the Identified, hence the annotation with a "+' added to the chevron.

Zaar can even omit the copula altogether. In (25), no copula is used for the IC2 structure.

This example is taken from a conversation where two young girls complain that they stayed idle at home the previous week-end. They promise themselves that this Sunday, they will not merely go out, but '(it is) everywhere' (that) they will go.

(25) "âː" < dzàŋ làːdì máː <+ kakáp >+ má gèːwàyéy // âː dzàŋ láːdì máː kakáp má geːwayé -íː ah day Sunday even everywhere 1PL.FUT walk_around RES ' "Ah" < on Sunday indeed <+ (it is) everywhere >+ (that) we will stroll. //' (Girls_A_010) When the copula is omitted, the absence of morphological marking on the Identifier (e.g. kakáp 'everywhere' in ( 23)) can result in ambiguity between an IC2 and a compound IlU with a pre-nucleus topic. See, e.g. ( 26) and ( 27) where there is no morphological change between kyâːn 'you' in (26), where it is a topic, and in ( 27), where it is an Identifier:

(26) Féːlêks < kyâːn máː < káː rigá kə yisə ́n tíː "áy" // In ( 27), the personal pronoun kyâːn 'you' is rhematised, and bears a prosodic prominence, whereas in ( 26) the pronoun kyâːn is topicalised (through a continuative prosodic contour) and the predicate káː rigá kə yisə ́n tíː 'you know him' bears the prosodic prominence. In both cases, the pronoun kyâːn precedes the predicate, and neither the pronoun nor the predicate carry a morphological exponent of their rhematic or thematic status. The change in the rhematic status of the illocutionary components is expressed only through intonation, through the change in the main prosodic prominence from the predicate to the Identifier in the IC2 structure.

The prosody of topic and identifying clauses in Zaar

Let us see a brief illustration of the intonation patterns characterizing those utterances and start with the "neutral" declarative sentence in (28), and its corresponding pitch track in By contrast, topics are pre-nucleus units characterised by various prosodic cues separating them from the comment. The two main cues that are always present are: suspension of declination, followed by a pause. These two exponents can be reinforced by a lengthening of the last segment of the topic, pitch reset and/or change of register. An example is provided in (29) and its pitch track in Figure 4, where the second topic, ɮàmɗì gòsɗìːːː 'the place where she goes', is separated by a change to a much lower register. The first topic mǎːm móːmi kúma 'as for Momi's mother' is a topic specified by the discourse particle kuma 'as for'. In the corpus, out of a total of about 1,400 utterances, 586 have been tagged as compound utterances with pre-or post-nuclei, while 571 have been tagged as simple (thetic, all-new) and 108 have been tagged as marked Identifying Clauses (IC2).

Syntactic representation

The differences in the properties explored in the previous section can be neatly represented using dependency graphs, as developed e.g. in the Universal Dependencies Grammar project (de [START_REF] Marneffe | Universal Stanford Dependencies: a Cross-Linguistic Typology[END_REF]) and the annotating tool Arborator [START_REF] Gerdes | Collaborative Dependency Annotation[END_REF]. The tagging of peripheries, discourse markers, etc. has been adapted to account for the properties described in Zaar (see Note 3).

Topic

Topics are represented as peripheric to the root, e.g. in (31) the IlCs laː 'work' and məːríwôpm 'our children'. The two topics have been labelled as dislocated in the graph (see Figure 6).

Marked Identifying Clause (IC2)

In UD analysis, the identifier of e.g. ( 32) is the root of the graph, and the identified variable (tə ̀tàyáː fûːmí ʧǐː, '(that) they used to tell us like this') is a dependent of the identifier (see Figure 7). ( 32) "tôː" < gíː >+ tə ̀tàyáː fûːmí ʧǐː // ' "Well" < it is this >+ (that) they used to tell us like this. //' (lit. THIS, they told us like this.) Moral_Har_088 When the copulas nə or kən are used, they are represented as dependents of the lexical predicate, i.e. the Identifier, as in ( 33) and ( 34) below (see Figures 8 and9). non-contiguous annotations of the parent tier. For a given annotated file in the "classical" Elan format, extra annotations can be created as new lines in two sets of tables: Groups and Links. Individual groups and links in the table can then be highlighted in the annotation tiers when selected, and the corresponding passage in the sound file can be played. The file can be searched, with multiple criteria including tier annotations, table, and distances in terms of alignment, annotation and time span. These tables can be sorted by types, names or annotations, which has a great heuristic value and opens new possibilities for structural annotations (whether informational or syntactic) in Elan. Figure 10 shows a screenshot of Example 13 annotated for IS with Elan and the Links and Groups module. In the first table (called Groups, top left of the screen), to create a group, the annotator selects a set of annotations in any of the existing tiers, gives this group a name and a type that can be selected in a controlled vocabulary. These sets consist of a single or several annotations that can be selected from one or several tiers, and can be discontinuous.

For this work, I used the Groups table to identify sets of words that make up Illocutionary Components (IlC: Nuclei, Pre-nuclei, Post-nuclei and In-nuclei), and tagged them with their function (Type) and reference number (Name) (see Figure 11).

Figure 11. Groups table sorted by Type with the phatic pre-nucleus of Example 13 selected

In the second table (called Links, top right of Figure 10), the annotator creates links between two sets of annotations built on the same principle as groups. One set is called the Source, and the other set is called the Targets. The links created are given a Name and a Type in the same way as for groups. The sources or the targets can also be taken from the Groups table. In this case, the sets selected from the Groups table can be viewed either by showing the annotations in the tiers, or the types and names given to the groups in the Groups table .   For this work, I have used the Links table to tag the Illocutionary Units. For better readability and convenience sake, the table shows the full text on the text tier as the source of the links and the IlCs (groups) tagged in the Groups table as targets. I have used the "type" column of the Groups table for a temporary, rule of thumb functional tagging of IlUs, indicating whether they contain e.g. questions, conditionals, rhetorical devices such as parallel IlUs, etc. (see Figure 12). The corresponding annotation can be viewed in the tiers below when selected, and the corresponding sound segment can be played via the media player in Elan (see Figure 13). The labels tagging the macrosyntactic constituents are both structural and functional, as seen in Table 1. lead me to a revision of my analyses and some of the labels used for tagging. This labile method must strike a balance between rapidity of annotation (a process which can be very time consuming) and how fine-grained our analysis needs to be. To be fully labile, the tagging system must anticipate the need for regular revisions, e.g. automatic conversion and collapsing of categories.

The next section is devoted to a typology of peripheries, i.e. pre-and post-nucleus components of IlUs, as retrieved in the corpus using the "groups" table sorted by type (see Figure 11).

Non-aligned peripheries

Non-aligned peripheries are elements entering in a microsyntactic relation with the root of the IlU, and as such, pertain to the textual construction of the utterance.

Pre-Nucleus (<+)

The pre-nucleus governed constituents are left-dislocated adjuncts (PR-Adv, PR-Cls, PR-Cnd)

and the pre-nucleus constituent of Unmarked Identifying Clauses (PR-IC1).

 PR-Adv, or left-dislocated adverbial adjunct (40) "tòː" < dzàŋ làːdì <+ má ɬǐː kində > "báː" //

Post-Nucleus (>+)

The post-nucleus governed constituents are the nucleus appendixes (APX, e.g. afterthoughts)

and the post-nucleus constituent of Marked Identifying Clauses (PST-IC2).

 PST-IC2

IC2s constitute a single intonation unit. In these constructions, the illocutionary nucleus is not on the verb of the clause, which follows in the post-nucleus situation, but on the Identifier. In the following examples, the nucleus is bolded, and the ">+" sign that follows the nucleus Likewise, kápwâːsə ̀ŋ, 'all of them', a part of the long example (4d) repeated in (17), is an appendix added to the nucleus as an afterthought after a final break.

indicates

9 Conclusion

In this paper analysing peripheries in relation with syntax and information structure in Zaar, a Chadic language spoken in Nigeria, I have argued that a minimal annotation representing in a simple and concise way the interface between information structure and syntax is essential to retrieve meaningful data. The article uses the concept of macrosyntax, based on illocutionary units, for this new level of annotation using existing morphosyntactic tiers in Elan. With the corresponding annotation script, a pilot 90 min (15,000 words) corpus has been annotated and a preliminary study of peripheries in this language has been done on this annotated corpus. I have argued that, although topics and frame-setters share the same intonation pattern, their syntactic properties call for a specific syntactic representation for which I have used a system adapted from Universal Dependencies Grammar. Some concluding comments can be done concerning the system introduced in this paper to annotate the information structure of Zaar, and how this structure is patterned in the language. I have chosen this punctuation, and developed a corresponding set of tags bearing in mind that it should be as theory-neutral as possible in order to implement a genuine bottom-up methodology, with a heuristic aim in mind, and hoping that the results can be used for typological comparisons. Another quality of this system of annotation is related to the fact that the notion of piling, accounting for coordination, can easily and intuitively be extended to dysfluencies, discontinuities and ellipses, and is perfectly adapted to the restitution of the oral flow. Despite the apparent accidents, interruptions and ellipses, the restitution of the piles proves that meaning, syntax and information progress and develop like the fugues and counterpoints of a musical score, which a description limited to the boundaries of a canonical grammatical sentence has been unable to account for. Finally, in the way Zaar shapes sound into meaning with the help of intonation, syntax and semantics, it appears that the left periphery is dominant and ICs are a device that is all the more meaningful as it is sparsely used. The three components of Zaar

Figure 1 :

 1 Figure 1: UD representation of Example 2Formally, a dependency is a directional relation between two words, which is represented by

Figure 2 :

 2 Figure 2: UD representation of Example 3

Figure

  Figure 3. 11

( 29 )

 29 mǎːm móːmi kúmá < ɮàmɗì gòsɗìːːː < ʧáː fini gòs <+ koyarwa makaranta // maːm kə ́ móːmi kúmá ɮam -ɗi gòs -ɗi mum POS Momi too return -DIR 3SG.POS -DIR ʧáː fi -ni gòs koyarwa makaranta 3SG.ICPL do -INCH 3SG.POS teaching school 'As for Momi's mother < the place where she goes (lit. her going) < what she does <+ is teaching children in school.' (SAY_BC_CONV_02_SP1_023-6)

Figure 4 :

 4 Figure 4: Pitch track of Example 29As for IC2s, they constitute a single intonation unit with no break, and are characterised by a

(

  31) laː < məːríwôpm < ʧì gwàːsə ̀ŋ <+ tə ́ laː hń // 12'As for work < our children < they themselves <+ (they) don't have any work.' (Wom_B_221)

Figure 6 :

 6 Figure 6: UD representation of Example 31

Figure 7 :

 7 Figure 7: UD representation of Example 32

Figure 10 :

 10 Figure 10 : Screenshot of Example 13

Figure 12 .

 12 Figure 12. Links sorted by types showing Ex (13) in IlU classification

Figure 13 .

 13 Figure 13. Annotation and sound file segment corresponding to Example 13

  (Nucleus of IC2) NCL-IC1 (Nucleus of IC1) Post-Nucleus PST-ALL: Allocutive, Vocative APX: Nucleus Appendix (Afterthought)

8

  Typology of pre-and post-nucleiWhen micro-and macro-syntactic dependencies are aligned, the boundaries of the nucleus correspond to the microsyntactic dependency unit of the verb/predicate carrying the illocutionary act, and include all the elements governed by this head. All the dialogical units are aligned (viz outside the government of the nucleus head). The aligned textual units are:Discursive links (PR-DCT) and Topics (TOP and ANT). As for non-aligned units, the prenucleus governed constituents comprise left-dislocated adjuncts (PR-Adv, PR-Cls, PR-Cnd) and the pre-nucleus constituent of IC1 (PR-IC1). The post-nucleus governed constituents are the nucleus appendix (APX, e.g. afterthoughts) and the post-nucleus constituent of IC2s (PST-IC2).8.1 Aligned peripheriesAligned peripheries are divided into two classes which are respectively dedicated to different types of information functions: a) the textual construction of the utterance (textual peripheries, e.g. Topic, Appendix, Locutive Introducer); b) its communicative support (dialogical peripheries, e.g. Phatic, Allocutive, Expressive, etc.; seeCresti 1999:15). The only textual periphery that is not part of the dependency frame of the head of the nucleus is the Topic (TOP) and it appears massively in pre-nucleus position: only 2 examples of post-nucleus topics (also called right-edged topics, or antitopics: ANT) are found in the corpus, against 611 cases of TOP. Topics are illustrated in (33) for left-edged Topics (TOP) and in (34) for rightedged Topics (ANT). Right-edged topics are characterised by a low tone, flat contour, and follow a non-final prosodic break.

  Abbreviations in morphosyntactic glossing: AOR, Aorist; ASS, Assertive; COMP, Complementiser; COND, Conditional; COP1, Copula #1; COP2, Copula #2; CPL, Completive; DEF, Definite; DIR, Directional; DIST, Distal; DM, Discourse Marker; EXCL, Exclamation; FILL, Pause Filler; FUT, Future; ICPL, Incompletive; INCH, Inchoative; INDF, Indefinite; ITER, Iterative; LOC, Locative; NEG, Negation; NMLZ, Nominaliser; OBJ, Object; PL, Plural; POS, Possessive; POSL, Possessive Link; PROX, Proximal; QLT, Qualitative; QUEST, Question; REM, Remote Past; RES, Resultative; SG, Singular; VRT, Virtual.

General Abbreviations: IC, Identifying Clause; IC1, Unmarked Identifying Clause; IC2, Marked Identifying Clause; IlC, Illocutionary Component; IlU, Illocutionary Unit; IS, Information Structure; IU, Intonation Unit; TAM, Tense-Aspect-Mood complex; UD: Universal Dependencies. Function tags in UD representation: advmod, adverbial modifier; aux:nsubj, TAM and subject compound; ccomp, clausal complement; conj:coord, coordinated conjunct; conj:dicto, dysfluency, reformulation, elaboration; cop, copula; csubj, clausal subject; dep, unspecified dependency (e.g. predicative complement of yi 'be'); det, determiner; discourse, discourse element; dislocated, dislocated element; dobj, direct object; iobj, indirect object; mark, marker; obl, oblique; punct, punctuation.

kóː ŋaː gə ̀t.

	b	[S2] gə ̀ɗàː ?
		gə ̀t	aː
		woman QUEST
		'A woman?'
	c	[S1] kóː ŋaː	gə ̀t
		or young woman
		'Or a girl.'
	d	[S2] ŋaː gə ̀t tá gìː ʃí ɓə ́ɮə ̀ŋ. Kápwâːsə ̀ŋ […]
		ŋaː	gə ̀t	tá	giː	ʃí	ɓə ́ɮə ̀ŋ káp wáːsə ̀ŋ
		young woman 3PL.FUT bury 3PL.OBJ outside all 3PL.POS
		'Girls, they would bury them outside. All of them. […]'
	e	[S1] tə ́ gə ̀t ɓét kóː ?
		tə ́	gə ̀t	ɓét	kóː
		with	woman	all	or
		And women generally? //
	f	[S2] m ̀ː tə ́ gə ̀t ɓét tá gìː ʃí ɗân.
		m ̀ː tə ́	gə ̀t	ɓét tá	giː	ʃí	ɗáni
		er with woman all 3PL.FUT bury 3PL.OBJ there
		'Er and women generally, they would bury them there.'
		(Bury_Sdy_20)
	4) a [S1 ] tô gə ̀t kən yáː mə̂s kúmá tá gìː tə ̀ gòs ɗòː?
		tôː gə ̀t	kən yáː	məs kúmá
		DM woman COP2 3SG.COND die too
		tá	giː tə	gòs	ɗôː
		3PL.FUT bury 3SG.OBJ 3SG.POS where
		'Well if it is a woman that dies, they will bury her where?'

The elements coordinated across the turns of conversation are linked to the structure of the first question, and inherit their syntactic function from the first element of the pile, as

	This analysis and its accompanying annotation system elegantly underline the coherence of
	this large passage without postulating the existence of elements that have been deleted
	through ellipsis. Each element in (4c and e) is linked to the initial utterance (4a), and inherits
	its referential coordinates from this unit.
	3.4 Parallel constructions
	(5)	yàːʃí mə ́nːːː malâːri máːːː, lə̌pm zaːr máː
	represented in (4'):
	(4') gə ̀t	kən yáː mə̂s kúmá […]
		kóː ŋaː gə ̀t
		tə ́ gə ̀t ɓét
		' if it is	women	that die [ …]'
			or girls
			and women in general

tá tûːr gyáː ɗûː, tá tûːr gyáː náɣat

	yàːʃí	mə ́n	malâːr	-i	máː	lə̌pm	zaːr		máː
	3PL	people	Malar	-INDF	too	moon.of	Zaar	too
	tá	túːr	gyáː	ɗûː	tá	túːr	gyáː	náɣat
	3PL.FUT	PL	cook	PL	beer	3PL.FUT	PL	food
	'The people of Malar, (at) the Zaar festival, they would brew beer, they would
	cook food.' (Cal_Har_045)

  In Zaar, this difference in the management of information is paralleled by a syntactic difference which confirms that topics and frame-setters belong to different functional levels:The same is true for (15) where the frame-setter ɗaŋgənín tsə ́n, 'right now' is dependent on the verb ɲom, 'wrestle', and this has no temporal adjunct as a dependant modifier inside the nucleus.

	f (14) ɗa gìp kìmsə ́y <+ kə ɬə ́ tutə //= kə fuːtə // [S2] "m ̀ː" tə ́ gə ̀t ɓét< tá gìː ʃí ɗân //
	ɗa	gìp	' "Er" and women generally < they would bury them kímsə -íː kə ɬə tu =tə
	at	there //'(Bury_Sdy_20) inside Kimsə -DIST 2SG.AOR go meet 3SG.OBJ
	kə 4.5.3 Left-dislocated circumstantial adjuncts fuː =tə 2SG.AOR tell 3SG.OBJ
	Left-dislocated circumstantial adjuncts have the same prosodic profile as topics, but they have 'In Kimsə <+ you go meet him //= (and) tell him.' (Boys_B_188)
	a different function. It is agreed that adverbials and other circumstantial adjuncts are frame-
	setters that limit the applicability of the main predication to a certain restricted domain (see
	Chafe 1976). Using the concept of common ground, Krifka & Musan (2012) establish a
	tôː ɗangəní tsəní	tá	ɲom tə ́ káɗi
	well now	like_this 3PL.FUT take with dog
				her
			where ?//'
	b	[S2] gə ̀ɗàː ?//
			'A woman ?//'
	c	[S1] {| ^kóː ŋaː gə ̀t |} //
	'{| ^Or a girl |} //' topics are pragmatic, belong to Information Structure, whereas frame-setters belong to the
	d dependency frame of the verbal root. In (14) and (15) for example, no adverb (such as ɗáni, [S2] ŋaː gə ̀t tá gìː ʃí ɓə ́ɮə ̀ŋ > kápwâːsə ̀ŋ […] //
	'Girls they would bury them outside > all of them.[…] //' 'there') need modify the verbs tu, 'meet'. This is done by the left-dislocated adjunct ɗa gìp
	kìmsə ́y, 'in Kimsə'.

3 (see Example 4), coordination occurs across turn-taking. It is annotated for macrosyntax in (4'): (4') a [S1 ] "tô" { gə t |} kən yáː mə̂s kúmá tá gìː tə ̀ gòs ɗòː ?// ' "Well" and if it is { a woman |} that dies, they will bury e [S1] {| ^tə ́ gə ̀t ɓét kóː } //? {| ^And women generally } ?// difference between contrastive topics and frame-setters which remains valid when contrasting frame-setters with aboutness topics: With contrastive topics, the current common ground management contains the expectation that information about a more comprehensive, or distinct, entity is given; contrastive topics indicate that the topic of the sentence diverges from this expectation. With frame setters, the current common ground management contains the expectation that information of a different, e.g., more comprehensive, type is given, and the frame setter indicates that the information actually provided is restricted to the particular dimension specified. (Krifka & Musan 2012:32) (15) "tôː" ɗaŋgənín tsə ́n <+ tá ɲôm tə ́ káɗi // ' "well" right now <+ he will wrestle with Dog. //' (Hyena_S1_282)

  (18) gyáː gə ̀~ raːsə ́n tsə ́n < tá gìː ʃí ɗân //

	gyáː gə ̀ːrí raːs	-ín	tsəní	tá	giː	ʃí	ɗáni
	PL	old	locust_bean PROX like_this 3PL.FUT bury 3PL.OBJ there
	'These old locust-bean trees like this < they would bury them there. //'
	(Bury_Har_109)
	(19) ŋaː gə ̀t < tá gìː ʃí ɓə ́ɮə ̀ŋ //+ kápwâːsə ̀ŋ //
	ŋaː	gə ̀t	tá	giː	ʃí	ɓə ́ɮə ̀ŋ	káp wáːsə ̀ŋ
	young woman 3PL.FUT bury 3PL.OBJ outside all 3PL.POS
	'Girls < they would bury them outside //+ all of them. //' (Bury_Har_103)

Table 1 :

 1 Group Types tagging macrosyntactic constituentsUsing this tag set, I have been able to test a tentative typology of pre-and post-nucleus units on 11 annotated files (90 minutes; 15,000 words). I was able to extract the list of illocutionary constituents, and check the consistency of the annotation. The aim of this type of extraction is to look for regularities in the marking of the units, in syntax, morphology, intonation and reference tracking, i.e. do a basic bottom-up research. It is clear that the relevancy of the results is dependent on the tagging, which is based on my intuition and understanding of the language, checked and controlled by informants. Of course, this bottom-up stance is not devoid of any theoretical bias, but the exhaustivity of the annotation will (and already has)

  Dialogical constituents are used to establish, maintain or qualify the illocutionary act. They occur before, or after the nucleus. They are surrounded by inverted commas in the transcription. It is possible to distinguish the following types:

	(34) gòpm < kóːdzàŋgyòː <+ mìɣá laː káwêy > myàːníːːː gút zaːr // (35) "tòː" < yâːn < ʧǎː ʧím tə ̀ ɣá vìː válti tu ʃéro > "ŋǎːn" // "yâwwàː" < "tôː" < 3SG.CPL love 1SG.OBJ 1SG.REM say 3SG.OBJ COMP no
		gòpm átâ yi ʧík // kóːdzàngyóː mìká '"Because" < "you know" < in the beginning <+ as Kamchak said he loved me laː káwêy 1PL.POS everyday 1PL.CONT work merely tôː yâːn ʧǎː ʧim tə ká vìː válti tu <+ I told him no way //' (Girls_B_147)
	myàːní guɗi DM 3SG 3PL.ICPL 8.1.2.3 Expressive	zaːr	call 3SG.OBJ	at speech	muslim	COMP
	1PL ʃéro Exclamations in Pre-Nucleus position (e.g. PR-EXP; kâːy, 'hey' in (39)). woman.PL human ŋǎːn yâwwàː tôː átâ yi ʧík
		'We < everyday <+ we do nothing but work > we Zaar women. //' flogging QUEST ok DM 3SG.REM be thus
	(Wom_A_169) ' "Well" < this < they call it in Hausa 'shoro' > "no"? // "OK" < "well" < that's (39) [Sp1] sə ̀kéːɗì ʧáː ndará //
		how it used to be. //' (Bury_Har_149) [Sp2] "kâːy" < ʧáː poləmgáy sòːséy //
	8.1.2 Dialogical constituents 8.1.2.2 Allocutive sə ̀kêːt -i	ʧáː	ndará
	Vocatives are examples of allocutives that can appear either before (PR-ALL, in [25]) or after skirt -INDF 3SG.CPL be_proper
	kâːy ʧáː the nucleus (PST-ALL, in (36) where Afo is a proper noun): pol =mə káy sòːséy eh 3SG.CPL please 1SG.OBJ LOC quite
	(36) "ká" < "Àfóː" < káː ye yáddiyóːɗam myáː súː sú kámʃâk > "kwǎː" // [Sp1] 'The skirt is nice. //'
		[Sp2] ' "Hey" < I really like it. //' (Girls_B_069)
		ká	Àfóː káː		yel yáddiyóːɗan
	disapproval Afo 2SG.CPL see how 8.1.2.4 Connective
		myáː	súː sú kámʃâk	kwǎː
		1SG.ICPL like PL Kamshak DM
		'"What" < "Afo" < you saw how I like Kamshak > "anyway". //'
		(Girls_B_073)
	In (37), the speaker is protesting, using a yes/no rhetorical question, ending in a vocative, i.e.
	'Afo', the name of the co-speaker.
	(37) mə ̀ káp Ngasaː > "Àfóː" ?!//
	 Expressive (PR-EXP & PST-EXP), giving an emotional strength to the illocutionary mə ̀ kap Ngas -aː Àfóː
	act.	1SG.AOR take Angas -VRT Afo
	 Connective (PR-DCT & PST-DCT), linking different parts of the discourse (utterances 'We should marry Angas people > "Afo" ?!//' (Girls_B_104)
	8.1.1 Topic within a turn, or across turns) maintaining some explicative, causal, temporal or In (38) káː yisə ́ŋ, 'you know', shows another way of maintaining the communicative channel,
	concessive values. Most of them occur in pre-nucleus position. (33) tsə ́tŋgə̂n də ǹ máː < myáː yel [nə lǎː bàptàk ɓasmí //] // with an associated IlU:
	tsə ́tn -kə ́nì də ̀n (38) ^dóŋ < "káː yisə ́ŋ" < farko máː<+ ɗaŋ kámʃâk tà wu tu ʧáː sûːm <+ mətá máː myáː yel sit -NMLZ house even 1SG.ICPL see 8.1.2.1 Phatic wultə tu baːbù //
		nə dón COP1 work POSL useless LOC 1PL.OBJ laː kə ́ káː yisə ́ŋ farko máː bàptàk ɓas =mí because 2SG.CPL know beginning even as ɗan kámʃâk Kamshak REM tà
		'Sitting home < I see [it is useless for us. //] //' (Girls_B_035) ʧáː súː =mə mətá wul =tə tu	baːbù

 Phatic (PR-PHA & PST-PHA), dedicated to control the communicative channel, ensuring its maintenance. They are either fillers (e.g. er…, mm…), discourse punctuators (OK, well, Zaar tôː), marks of agreement with the speaker (uh, Zaar mː, èː), etc.

 Allocutive (PR-ALL & PST-ALL), specifying to whom the message is directed, keeping their attention (Vocative, you know, you see) or introducing evidential modality (I think, etc.).

In (

35

), three cases of phatic units are exemplified: tôː, 'well' and yâwwàː, 'OK' as PR-PHA, and the TAG ŋǎːn, 'no?' as PST-PHA.

In (38), don, 'because', is an initial discursive link (PR-CNT) working as a connective. It is annotated with a circumflex accent.

  the people who do it <+ are the people of Marsang. //' (Cal_Har_010)

	' "Well" < on Tuesday <+ we'll go to Kində > "no". //' (Girls_A_001)
	 PR-Cls, or left-dislocated clausal adjuncts
	(41) "tòː" < kyàː gìː tíː <+ "tôː" ká ɮə ̀ːríː ʧíp //
	tôː kyǎː	giː	tə		-íː	tôː ká	ɮə ́ːr -íː	ʧíp
	DM 2PL.ICPL bury 3SG.OBJ RES DM 2PL.FUT stay RES quietly
	' "Well" < (after) you had buried him <+ "well" < you would sit still. //'
	(Bury_Har_046)
	Correlative conditionals (i.e. conditionals with a temporal meaning: 'if (=when, =each time
	that) … then…') are analysed just like ordinary adjuncts:
	(42) yáː yelmə̌ŋ <+ ʧáː fitə wusúŋə̌ŋ > "éy" //
	yáː		yel	=mə		hń	ʧáː	fi =tə
	3SG.COND see	1SG.OBJ NEG2 3SG.CPL DO 3SG.OBJ
	wusúŋ	hń	éy
	be_nice	NEG2 indeed
	yâːn hali	ɗa	kàm	má		ɗiːp -i
	if	chance COP3 indeed 1PL.FUT buy -INDF
	àmáː mə ́n	yóːɗan ʧǎː		fi nə	mə ́n	mársəŋ
	but	people which 3PL.ICPL do COP1 people Marsang
	'^But <
	tôː dzàŋ láːdì	má	ɬə -íː kində báː
	well day Tuesday 1PL.FUT go RES Kində NEG1

'If/when he does not see me <+ he is not happy > "hey". //' (Girls_B_077)  PR-Cnd, or left-dislocated conditionals (43) yâːn hali ɗa kàm <+ má ɗìːɓí // 'If there is a chance <+ we will buy it. //' (Girls_B_056)  PR-IC1, or pre-nucleus section of Unmarked Identifying Clauses (IC1) (44) ^àmáː < mə ́n yóːɗan ʧǎː fî <+ nə mə ń mársəŋ //

^séː tə ̀ ŋál kə ́láːsòː //

  that there is a microsyntactic relation with what follows.(45) ^dòːmín < sə ́ŋwaːrí < séː dàːʃì yáː môr lǔːy >+ əndá ʧàːyi vər tə sə ́ŋwaːrês // gíː kə ́ mán well DIST 2PL.AOR come '"Well" < that >+ you have come (for)? //' (= "well", is it that what you have come for?) (Girls_A_090) Afterthoughts, which are elaborations or correction of the illocutionary act of the nucleus, are expressed in a different IlU. They are preceded by a final intonation break and a pitch reset, and they receive a falling contour. In (47), the afterthought is bolded:(47) móɣʃi makaranta < ma ɗyǎːŋoː //+

	dòːmín	sə ́ŋwaːrí	séː		daː	-ês	yáː	mor
	because	chief_priest only_if	person DEF	3SG.COND do_a_little
	luː	-íː kəndá	ʧàːyí	vər	tə	sə ́ŋwaːrí	-ês
	get_old RES then	3PL.ICPL.ITER give	3SG.OBJ chief_priest DEF
	mókʃi	makaranta ma	ɗyáː	hń -oː
	courting school	1SG.FUT be_able NEG2 ASS
	séː	tə ̀	ŋal		kə ́lâːs	-oː
	unless	3SG.AOR	look_for class	ASS
	'Dating in school < I couldn't do it //+ ^unless he changed class. //'
	(Girls_A_076)

'^Because < a chief < (it's) only when a man is a bit old >+ ^then they make him a chief. //' (Rel_Har_008) (46) "tòː" < gíː >+ kə ́ mân ?// tôː

See Note 3 for the list of UD function tags used in the paper.

Conjunctions are treated asymetrically with the first conjunct as the head, and all the other conjuncts as dependants of this element.

See also Section 5.3 for a more detailed presentation of intonation patterns in Zaar.

See Note 9 on microsyntax.

The "&" indicates an aborted IlC.

ʧì is analysed as the fusion of the 3PL subject pronoun tə ̀ plus the defective verb yi 'be'.

(33) nə ɬərtín >+ ka ɓəl > fáː // '(It) is this root >+ (that) you will dig > indeed. //' (Moral_Har_069) (34) "tôː" < yáːni kən >+ wò fi wuki gín ɗányâːlín // ' "Well" < it is THIS >+ (that) will make this very medicine. //' (Moral_Har_076) Illocutionary Units come forth with a clear specialisation: the pre-nucleus establishes the frame/ground/site around the speaker's point of view; the nucleus carries the action/opinion, etc. in relation to the site; the post-nucleus seeks the hearer's approval, reactions or comments.