Philippe Druault
email: philippe.druault@upmc.fr

Grégory Germain
email: gregory.germain@ifremer.fr

Analysis of hydrodynamics of a moving trawl codend and its fluttering motions in flume tank

The coupled dynamics of the codend fluttering motion and its hydrodynamics behavior are investigated based on PIV measurements carried out around a 1/10 scaled model codend. The mean wake flow characteristics are firstly analyzed demonstrating the great effect of the codend motion. The differences in flow contour between fluttering motion and no-fluttering codend are highlighted by analyzing the classical mean characteristics of a body wake flow. In presence of fluttering motion, the classical results are recovered only if one considers the vertical extent of the codends oscillations as a reference length scale. Secondly, the frequency content of codend oscillations is analyzed and it is linked to the physical mechanisms. It is observed that the motion of the codend is mainly of a low-frequency activity and of another component related to the vortex shedding street. It is then demonstrated a complex fluid-structure interaction where the near wake hydrodynamics of the moving codend structure are an intricate interplay between the codend fluttering motion and the shear layer instabilities and large scale vortex shedding. The knowledge of such flow instabilities is of great importance to improve the understanding of the force acting on a trawl and for the implementation of some selected device to drive fish near the codend and then to reduce juvenile by-catch.

Introduction

Trawls funnel-shaped nets are dragged horizontally in the ocean. To optimize their efficiency in terms of catch capability and their impact on the fuel consumption, they must have a high static stability which is difficult because of the great influence of hydrodynamic effects. It is now well known that the turbulent flow developing around fishing nets structures impacts the whole drag force of the fishing gear and the selectivity. A particular attention should be paid to the trawl codend which is the part of the trawl where fish are retained. Indeed, the codend hydrodynamics have as a non-negligible impact on its selection [START_REF] O'neill | An investigation of the relationship between sea state induced vessel motion and cod-end selection[END_REF]. For instance, the codend fluttering motions lead to some changes in the codend mesh openings [START_REF] Gjosund | Flow through nets and trawls of low porosity[END_REF][START_REF] Bouhoubeiny | Phase-averaged mean properties of turbulent flow developing around a fluttering sheet of net[END_REF][START_REF] Druault | PIV measurements combined with the motion tracking technique to analyze flow a around moving porous structure[END_REF] and may have direct consequences on cleaner catches, knowing that the catch is correlated to the codend behavior [START_REF] Madsen | Behavior of different trawl codend concepts[END_REF][START_REF] Priour | An experimental/numerical study of the catch weight influence on trawl behavior[END_REF][START_REF] Digre | Effect of trawling with traditional and 't90' trawl codends on fish size and on different quality parameters of cod gadus morhua and haddock melanogrammus aeglefinus[END_REF].

The analysis of the high Reynolds turbulent flow around a moving codend remains a challenge for both experimental and numerical approaches. Indeed, the immersion of such a fishing net structure in a turbulent flow induces structure oscillations that are directly linked to elastic, kinematic and dynamic instabilities. The origins of such structure oscillations for an immersed body in a turbulent flow are multiple [START_REF] Naudascher | Flow-Induced Vibrations : An Engineering Guide[END_REF]. The most common ones in the present flow configuration are i) induced vibrations caused by local hydrodynamic effects (fluctuating velocities, vortex shedding wake, etc) ; ii) induced vibrations caused by the warp tension variations, iii) induced vibrations due to movements of the deformable structure itself. Furthermore, the geometry of the rigid structure has a significant effect on the wake [START_REF] Blevins | Flow-Induced Vibration[END_REF]. Therefore, in the current moving codend fishing net structure, some other potential sources of structure excitation are also present. They are related to the possible fluid-passage though the porous structure and also to the fully unsteady Turbulent Boundary Layer (TBL) flow developing inside and outside the fishing net surface [START_REF] Druault | POD investigation of the unsteady turbulent boundary layer developing over porous moving flexible fishing net structure[END_REF][START_REF] Bouhoubeiny | Phase-averaged mean properties of turbulent flow developing around a fluttering sheet of net[END_REF]. Finally, every excitation mechanism may occur simultaneously and interacts with each other, increasing the complexity of the fluid-structure interaction characterization. As a consequence, some dimensionless parameters classically used for the Vortex Induced Vibration (VIV) analyses can not be used in the present flow configuration. For example, parameters such as dimensionless time and normalized oscillation amplitude (both related to body oscillations) or mass ratio can not be properly estimated here due to the unsteady character of the fluttering motion of the codend as well as to the instantaneous variability of the fishing net mesh opening.

Even if the flow around freely moving structures is of relevance for many practical applications, the complex flow interaction with a freely moving deformable structure remains poorly understood. In the past, some canonical flow configurations such as the wake of a rigid fixed cylinder or sphere have been fully investigated. The wake flow developing behind a rigid body which is forced to oscillate has been previously analyzed as well, demonstrating the classical lock-in phenomenon [START_REF] Bearman | Vortex shedding from oscillating bluff bodies[END_REF][START_REF] Williamson | Vortex induced vibrations[END_REF]. The dynamics of the flexible cable for which vortex induced vibrations in two degrees of freedom are present, have also been studied [START_REF] Jauvtis | The effect of two degrees of freedom on vortex-induced vibration at low mass and damping[END_REF][START_REF] Modarres-Sadeghi | Chaotic response is a generic feature of vortex-induced vibrations of flexible risers[END_REF]. These last studies and references therein may be related to present flow configuration even if bottom trawl can not be really identified as a flexible cable due to its particularities : flexible, porous and deformable. Besides, we must not forget that the body of the trawl is funnel-like, wide at its entrance and narrowing towards the codend. Even if the codend is of greater diameter than the cylindrical middle part of the trawl (see figure 1 -right hand side), trawl gear may be also related to an elongated body. However, the great disparities compared to previous studies dealing with elongated body concerns the flow which develops not only around the fishing structure but also inside it.

So far, there have been very few studies about the investigation of a freely moving codend structure and its fluid-structure oscillation interaction. Pichot et al. [START_REF] Pichot | On the experimental study of the flow around a fishing net[END_REF] previously used Laser Doppler Velocimetry (LDV) measurements to study the mean flow field over a rigid fixed codend with a closed and an open net entrance. Kim [START_REF] Kim | Analysis of the turbulent flow and tilt in the cod-end of a bottom trawl during fishing operations[END_REF] performed one-point measurement inside a codend demonstrating the effect of turbulence on fish behavior. Based on Particle Image Velocimetry (PIV) measurements, Bouhoubeiny et al. [START_REF] Bouhoubeiny | Time-resolved PIV investigations of the flow field around rigid cod-end net structure[END_REF] investigated the flow around a rigid codend and also around an oscillating codend model. These preliminary analyses showed the possible turbulent flow interactions with the fluttering codend structure without any full investigation of PIV database. In order to increase juvenile fish escape, Kim et al. [START_REF] Kim | An actively stimulating net panel and rope array inside a model cod-end to increase juvenile red seabream escapement[END_REF][START_REF] Kim | Fluttering characteristics of the ropes and nets as an active stimulating device inside the cod end of a trawl net[END_REF] recently proposed to implement an Active Stimulating Device (ASD) allowing to drive fish near the codend. Such an ASD technique induces fishing gear fluttering motions with varying amplitudes during fishing operations. Therefore, prior to implementing such a stimulating device, it seems important to determine the codend hydrodynamics and its associated oscillations frequency content. Moreover, a great disparity is obtained in the measurements when dealing with different flow conditions not only during real fishing operations [START_REF] Kim | Analysis of the turbulent flow and tilt in the cod-end of a bottom trawl during fishing operations[END_REF] but also in a laboratory [START_REF] Priour | Premecs-ii : Development of predictive model of codend selectivity[END_REF]. In this study, we propose a preliminary specific flow configuration which has been scaled to approach the real flow field around full scale codend. The analysis of the hydrodynamics and associated codend oscillations is then performed providing significant information that could be extrapolated to a full scale codend in certain conditions. More precisely, we focus on hydrodynamics behavior of rear codend even if the flow characteristics inside the codend may certainly also induce coded oscillations. However, it seems that the codend motions are mainly related to flow characteristics around the body. The codend movement is mainly linked to the lock-in phenomenon due to the vortex street developing behind the codend.

The present research is motivated by the lack of detailed quantitative information on the coupled dynamics of the codend fluttering motion and its hydrodynamic behavior. The aim of this study is to characterize the flow in the wake of a moving codend and to link the flow characteristics to the motion of the codend. We then propose to experimentally investigate the moving codend hydrodynamics of a 1/10 scaled bottom trawl. More specifically based on PIV measurements of the unsteady wake developing behind the moving codend structure, the frequency content of codend oscillations is characterized and linked to the physical mechanism related to the whole oscillating process.

Experimental set up

Experimental measurements are performed in the IFREMER (French Research Institute for Exploitation of the Sea) wave and current circulation flume tank whose dimensions are the following : 18m(length)×4m(width)×2m(depth) [START_REF] Pichot | On the experimental study of the flow around a fishing net[END_REF][START_REF] Bouhoubeiny | Time-resolved PIV investigations of the flow field around rigid cod-end net structure[END_REF]. The bottom of the flume is a conveyor belt which can be synchronized with the water speed in order to simulate devices in contact with the bottom, like bottom trawls. A side observation window of 8 × 2m 2 allows users to observe the behaviour of the trawl models during trials and to carry out measurements. In this work, the hydrodynamics of a freely moving modeled bottom trawl are investigated with PIV measurements. The measurements are made with a constant streamwise velocity of U re f = 0.4m.s -1 and a turbulence intensity rate of 5 %. This turbulence level corresponds to the one computed in another far field measurement plane where the flow is uniform and based on the same inflow conditions. Using Reynolds decomposition, the turbulence level has been determined and it is of an order of 4.8%. The input streamwise velocity corresponds to a towing speed of 2.5knots at real scale.

Description of the 1 :10 scaled codend model

A 1 :10 scaled model of a bottom trawl is considered in this work. Such a model is 4m long with a swept width of 1.8m (horizontal opening) and a swept height of 0.4m (vertical opening). The rigging is composed of four bridles of 2m long (with a diameter of 1mm) connected to 1/10 trawl doors. The top bridles are fixed to the warp as shown in [START_REF] Priour | An experimental/numerical study of the catch weight influence on trawl behavior[END_REF]. The fishing net structure is a 1 :10 scaled model of a commercial two faces bottom trawl. The model is made with diamond-shaped meshes of 8mm long (the length of the meshes between two knots), with a twine diameter of 0.6mm [START_REF] Druault | POD investigation of the unsteady turbulent boundary layer developing over porous moving flexible fishing net structure[END_REF]. 60 meshes have been used for the codend part, with a trawl design presented in [START_REF] Priour | An experimental/numerical study of the catch weight influence on trawl behavior[END_REF].

Figure 1 displays the upstream part of the bottom trawl and its associated codend. The codend catch is simulated using porous balls. During experiments, no displacement of the catch are observed. The diameter of the codend at rest is d = 0.22m (see figure 2 for the definition of d). The Reynolds number of the present flow configuration based on the flow velocity U re f is Re = U re f d/ν = 88000, with ν the kinematic viscosity of water.

Note that an investigation of the Turbulent Boundary Layer (TBL) developing over the horizontal part of this bottom trawl has been already performed [START_REF] Druault | POD investigation of the unsteady turbulent boundary layer developing over porous moving flexible fishing net structure[END_REF] demonstrating the influence of trawl motion onto the TBL flow characteristics. This preliminary study confirmed the great complex flow structure interaction mechanism in such a flow configuration.

PIV measurements

In this study, instantaneous velocity vector fields are measured in the symmetric plane of the codend with classical PIV apparatus (see figure 2). Glass spheres with a mean diameter of 15µm are used to seed the flow. The PIV method relies then on the determination of the motion of seeded particles by analyzing the displacements of particles between two successive images [START_REF] Adrian | Particle image Velocimetry[END_REF]. The interest of PIV is that measurements provide not only the spatial instantaneous description of flow structures but also their dynamics (time-frequency description). The PIV system consists of a double pulsed laser type two-chamber Gemini PIV Nid-Yag 2 × 120mJ at 4Hz, and a Dantec Hi-sense CCD camera, with a focal lens length of 60mm. The laser is placed behind the codend and the camera is outside the tank. The camera has a 4Hz frequency for a double-frame images with a 1600 × 1200pixels 2 resolution. Finally, instantaneous velocity fields are obtained using a cross-correlation PIV algorithm. The size of the interrogation window is 32 × 32pixels 2 and adjacent windows are 25% overlapped. Instantaneous velocity fields are then measured on the physical mesh of n x × n y = 99 × 74 = 7326 points.

To investigate the near wake behind the codend net structure, the PIV system is implemented to determine the streamwise u and vertical v velocity components along the horizontal x and vertical y directions respectively. An illustration of a raw PIV image is given in figure 2. Note that the PIV system can not enable us to evaluate the particle image displacement in the shadow area produced by the codend structure. This shadow area is located between the dashed white line and the white line given in figure 2. The white line indicates the contour of the structure around which PIV measurements are available. 814 instantaneous PIV velocity vector fields are then obtained on the regular mesh of 7326 points with a spatial resolution of 3.8mm in both directions. The time step between two PIV vector fields is 0.25s.

Results

An analysis of the mean flow properties is first performed. Then, the codend fluttering motion is investigated by extracting the codend contour-line in each PIV image. It is to be noted that no fluttering of the wing net, of the float line or even the catch were observed. Finally, codend hydrodynamics are studied with an emphasis on the coupling between fluttering structure motions and codend hydrodynamics.

Analysis of the mean flow properties

Figure 3 presents two instantaneous planar velocity fields at two characteristic times corresponding to the extreme vertical positions of the codend structure during the measurements : i) the lowest one -left hand side and ii) the highest one -right hand side. The turbulent wake flow is clearly evidenced and is also greatly function of the structure displacement. As stated previously, one can not really appreciate the flow developing all around the codend structure, especially in the upstream part of the codend because of the shadow effect in the image. Indeed, the codend structure is axi-symmetric and close to a spherical-shape structure (see figures 1 and 2). In these figures, the shear layer vortices are observed. These vortices are clearly discernible (black circles) along the line y/d = 0.4 (figure 2-left) and even for x/d ≈ 0.6. They appear just after the codend structure indicating that the transition point moves upstream due to the codend oscillations.

Prior to discussing any mean flow properties, an analysis of the statistical convergence of the mean velocities and the turbulent stresses is conducted. It is known that the number of PIV samples is a compromise between processing capabilities and acceptable convergence of statistics [START_REF] Ullum | Statistical analysis and accuracy of PIV data[END_REF][START_REF] Uzol | The effect of sample size, turbulence intensity and the velocity field in the experimental accuracy of ensemble averaged PIV measurement[END_REF]. For present convergence analysis, we extract velocity signals at selected locations in the measurement plane corresponding to high and low shearing areas. Five points (labelled X i with i varying from 1 to 5) are considered and they are indicated with black squares in a graph of the figure 5. Figure 4 displays the variation of averaged flow quantities as a function of the number of samples used to compute the statistics. Even if the time duration is certainly too small for properly investigating the very low frequency content of the velocity signals, these results demonstrate that a converged state is reasonably achieved. Present PIV statistics and spectral analysis presented in §3.3, would need longer time histories. It is then a first step towards a more detailed analysis.

Figure 5 represents the topology of the mean flow field computed in the context of the Reynolds decomposition. In each graph, the highest and lowest positions of the structure are indicated with a dotted-line and a continuous line respectively. Note that the unsteady motion of the structure avoids any use of the Reynolds decomposition in the area swept by the codend oscillations. That justifies zero values imposed in this domain. The mean flow pattern in the wake recirculation is classically dominated by two-symmetric vortices and it is quite similar to the one obtained behind a fixed rigid codend [START_REF] Bouhoubeiny | Time-resolved PIV investigations of the flow field around rigid cod-end net structure[END_REF]. However, when comparing the mean wake flow topology of the present fluttering codend to a fixed codend [START_REF] Bouhoubeiny | Time-resolved PIV investigations of the flow field around rigid cod-end net structure[END_REF], the locations of both symmetric vortex centers are slightly different. For the fixed rigid codend, the centers are approximately located at y = ±0.5d while in the present flow configuration, the associated centers are located at y = ±0.35d. This is directly linked to the oscillating structure motion which tends to limit the transverse extent of symmetric vortices. On the other hand, the streamwise extent of the wake recirculation is higher (approximately ≈ 1.5d) than the one obtained behind a rigid fixed codend [START_REF] Bouhoubeiny | Time-resolved PIV investigations of the flow field around rigid cod-end net structure[END_REF] and the one classically observed when dealing with the wake of a cylinder [START_REF] Braza | Turbulence properties in the cylinder wake at high reynolds numbers[END_REF] or sphere obstacle. The recirculation length also depends on the structure's oscillation response. The length denoted D (see figure 5) corresponding to the vertical spatial extent of the structure's y-oscillations is then estimated and D is equal to 0.258m. This results in a recirculation length of 1.28D approaching classical previous observations. The fluttering motion of the codend structure in a fluid flow increases then the strength of the shed vortices that modifies the resulting drag force accordingly.

Figure 5 displays the following Reynolds tensor components : u 2 , v 2 , u v , where an overbar indicates the time average. Results indicate that the Reynolds stresses are symmetric about the structure center axis. The center-axis is defined as the average y-center of the structure motion. The normal components are larger than the shear component. Such isosurface representations exhibit classical behavior [START_REF] Bouhoubeiny | Time-resolved PIV investigations of the flow field around rigid cod-end net structure[END_REF][START_REF] Braza | Turbulence properties in the cylinder wake at high reynolds numbers[END_REF] even if the PIV measurement plane is limited to x/d = 1.3 (that is x/D = 1.1), avoiding any full investigation of the wake flow. The u 2 component has a two-lobe structure taking its origin in the structure oscillation area. This suggests an increased unsteady activity in this area related to the structure's y-oscillations. The v 2 component seems to have a one-lobe structure located outside the PIV measurement plane. The shear component u v exhibits two areas with opposite signs, located on either side of the wake centre line. Globally the topology of the mean flow field is close to classical results if one considers the vertical extent of the structure's oscillations as a reference length scale. Moreover, as previously observed for a vibrating cylinder [START_REF] Williamson | Vortex induced vibrations[END_REF], the wake flow pattern greatly depends on the structure's oscillations which are analyzed in the next section.

Analysis of the codend fluttering motion

The detection of the codend interface is implemented in each PIV image (figure 2) and consists of the following steps : nine points located on the surface of the bottom trawl are manually marked in each PIV picture. The fishing structure moves quite slowly, and the local fishing net deformation is quite small compared to the amplitude of the fishing structure oscillations. Thus, the time evolution of each marked point is similar in the PIV plane. That enables the time evolution of the whole fishing net structure to be determined during experiments.

Figure 6 displays the time evolution of the vertical and longitudinal oscillations of the codend structure. In this graph, the fluctuating parts of both signals are plotted. The structure's y-oscillation amplitude is more than twice the height of the one related to the x-oscillation. The peak to peak vibration amplitudes of x-and y-oscillations are 0.057d and 0.1735d respectively that corresponds to ±3% and ±8.7% of d. The standard deviation of the y-displacement signal is also a little higher than the one of the x-displacement signal. In this graph, one also plots the xand y oscillatory associated velocity. The maximum oscillatory velocity is u xm = 0.019m.s -1 and u ym = 0.064m.s -1 for x-and y-oscillations respectively. These values correspond to 5% and 16% of the reference flow velocity U re f . Therefore, such a motion has a non-negligible effect on the codend hydrodynamics. The effect of codend's oscillations is to increase the resultant velocity of the fishing structure and, consequently, modify the codend hydrodynamics then the fluid forces and the mesh opening as the node positions will certainly change at any time. Regarding the discussed Fourier analysis in the following, given the complexity of the interaction, much longer signals should certainly be needed in order to evaluate the predominant frequency peaks with sufficient precision. In this sense, to improve the statistical convergence, the Welch method is retained to compute spectra. Each spectrum presented in the following figures is then computed using FFT based on Welch's method and using a Hann window.

X1 X2 X3 X4 X5 u 2 (X i )/U 2 re f v 2 (X i )/U 2
In figure 6, the spectral representation of structure's oscillations is given in a log-log scale. The highest frequency peak is obtained at a very low frequency component ( f 1 = 0.01Hz) for both x-and y-displacements. The second frequency peak is observed at f 2 = 0.21Hz, especially for the transverse y-displacement. Note that the interactions between the low frequency component ( f 1 ) and the f 2 frequency induce an additional frequency f 3 = f 2 ± f 1 , especially observable for f 3 = f 2 -f 1 = 0.20Hz. Globally, the codend's oscillations are fully unsteady and the spectrum of the structure'x-oscillations exhibits mainly a frequency peak around f 1 = 0.01Hz while the spectrum of the structure's y-oscillations exhibits a more broadband spectrum with two pronounced peaks around f 1 and f 2 frequencies.

Note that low frequency of the codend is independent on frequency of the incoming flow. Indeed, previous velocity measurements were performed in the same flume tank however based on different flow configurations (for instance, flow around a rigid codend [START_REF] Bouhoubeiny | Time-resolved PIV investigations of the flow field around rigid cod-end net structure[END_REF] or marine current turbine flow [START_REF] Medina | Correlation between synchronised power and flow measurements, a way to characterize turbulence effects on a marine current turbine[END_REF]) and the velocity spectra did not exhibit such a low frequency content.

Based on these frequency values, the dimensionless Strouhal number S t = f L/U with f the frequency oscillation, L the reference length and U the reference velocity is calculated. One obtains S t1 = f 1 D/U re f = 0.0065 and S t2 = f 2 D/U re f = 0.135. Then the second frequency peak ( f 2 ) is related to the vortex shedding frequency of the natural wake flow excitation. Indeed, as it has already been demonstrated, the vortex shedding frequency of a structure in motion is smaller than the vortex shedding frequency of a structure at rest. For a similar flow configuration but with a rigid fixed codend, Bouhoubeiny et al. [START_REF] Bouhoubeiny | Time-resolved PIV investigations of the flow field around rigid cod-end net structure[END_REF] showed that the Strouhal number related to the vortex shedding frequency was 0.19. Conversely, the low frequency motion seems to be directly linked to the cyclic variations in the warp tension related also to the elastic nature of the elongated bottom trawl. This is reinforced with the fact that the f 1 frequency is the main frequency detected in the structure x-oscillation. This low-frequency appears as the only component in the spectra of transverse and streamwise oscillations, indicating that these oscillations are locked into the same kind of dynamics which is certainly related to the warp tension enabling the bottom trawl to be maintained horizontally. Furthermore, this low frequency component is quite similar to the one also detected on the horizontal surface of this bottom trawl [START_REF] Druault | POD investigation of the unsteady turbulent boundary layer developing over porous moving flexible fishing net structure[END_REF]. This result is also coherent with previous measurements of cable forces [START_REF] Facq | Compte rendu d'essai de mesures d'efforts et de géométries sur une nappe d'alèse[END_REF] enabling a fluttering sheet of net to be maintained in a flow [START_REF] Bouhoubeiny | Phase-averaged mean properties of turbulent flow developing around a fluttering sheet of net[END_REF]. Indeed, these measurements have shown that the cable force signal is of low frequency content [START_REF] Facq | Compte rendu d'essai de mesures d'efforts et de géométries sur une nappe d'alèse[END_REF]. Finally, the codend structure in a fluid flow will vibrate due to the regular shedding of vortices into the wake and also to the warp tension maintaining the codend horizontally. The predicted amplitude of codend oscillations depends upon the difference between the frequencies associated with both phenomena and the oscillations due to the warp tension are the most dominant ones. This result is quite comparable to previous work dealing with VIV of a long flexible structure in cross-flow [START_REF] Modarres-Sadeghi | Chaotic response is a generic feature of vortex-induced vibrations of flexible risers[END_REF]. They observed that the in-line direction oscillations dominated the cross-flow ones and these in-line oscillations arised from the tension stiffness. Note that it is quite interesting to observe that the computation of the Strouhal number based on the f 1 and on the streamwise velocity of the x-oscillation : S t1 = f 1 × D/u xm provides S t1 = 0.135. This is close to the value of S t2 previously computed and related to the vortex street. This result emphasizes the great coupling that exists between the vortex sheet and the low frequency fluttering motion of the structure.

Fourier analysis of PIV velocity field

The spectral content of the instantaneous velocity fields extracted along two vertical lines is now studied. More precisely, the streamwise and vertical velocity components are stored at two y lines located just after the codend (x/d = 0.58) and in the wake flow (x/d = 1.27). These vertical lines are indicated in figure 2 with dotted lines. Figure 7 represents velocity spectra represented as a function of y-positions (x-axis) and frequencies (y-axis). These spectra are normalized with the maximum value of these four spectra. Note that this maximum is nearly the same for the spectra of the streamwise velocity component along both y-lines but for the y-line (x/d = 0.58) this maximum is attained for y/d = ±0.5 at low frequencies ( f ≈ f 1 ) while for the other yline (x/d = 1.27) this maximum is attained for y/d ∈ ±[0.4 : 0.55] at frequency f = f 2 . When regarding raw spectra, the amplitude of transverse velocity spectra is more than ten times smaller than that of streamwise velocity spectra. This is mainly due to the difference between streamwise and transverse velocity component values.

Firstly, the u spectra exhibit both f 1 and f 2 frequencies in the shear layer of the wake flow. Such shear layer extends along the vertical direction and covers the length swept by the structure's y-oscillations. In the separating shear layer (x/d = 0.58), one also observes the subharmonics of the main low frequency component ( f 1 ) of the codend oscillations. The streamwise velocity component is very sensitive to the low frequency oscillations. It is then indicated that the main flow instabilities in the shear layer originate from the codend motion.

Secondly, the transverse velocity component is mainly related to the vortex shedding frequency ( f 2 ) in each vertical line with an emphasis in the wake (x/d = 1.27). These results confirm the lock-in phenomenon which is that the vortex shedding frequency coincides with the structure's oscillations at frequency f 2 . However, it is interesting to note that in the developing wake x/d = 1.27, the low-frequency component is still present at a smaller amplitude than the one associated with the f 2 frequency. The topology of the low-frequency activity is different from the vortex shedding street but it is still present in the wake indicating that this is a slow process, and a long-lived one. This confirms that both frequency peaks of the codend's oscillations are quite fully recovered in the wake of the codend.

Previous VIV analysis of the near wake instabilities of a rigid fixed cylinder at Re = 10000 [START_REF] Dong | A combined direct numerical simulation-particle image velocimetry study of the turbulent near wake[END_REF] have shown that the shear layer vortices were observed to occur at higher frequencies than the Strouhal vortices. Here, the frequency instability of shear layer vortices are directly dictated by the low-frequency codend motion. Codend hydrodynamics are then very sensitive to the structure's oscillations, and vice versa. Such results will certainly have some implications on the catchability. Indeed, as it has been demonstrated in Bouhoubeiny et al. [START_REF] Bouhoubeiny | Phase-averaged mean properties of turbulent flow developing around a fluttering sheet of net[END_REF], a similar fully unsteady TBL flow is developed both inside and outside the horizontal part of the fishing net structure. Then, it is expected that inside the codend, the codend fluttering motion induces some hydrodynamics having similar frequency content. As a direct application, the knowledge of the codend motion would certainly enhance the efficiency of Active Stimulating Device [START_REF] Kim | An actively stimulating net panel and rope array inside a model cod-end to increase juvenile red seabream escapement[END_REF][START_REF] Kim | Fluttering characteristics of the ropes and nets as an active stimulating device inside the cod end of a trawl net[END_REF] previously proposed to active fish stimulus escapement.

Vortex shedding street analysis

To pursue the analysis of the wake hydrodynamics, we propose to extract the vortex shedding street from PIV database. For such analysis, a Fourier filtering procedure is implemented. It consists in first performing a FFT of the available instantaneous vector PIV fields. Then in Fourier space, one only selects the frequency domain located around f 2 (see figure 6 where two dashed vertical lines delimit the domain). Then an inverse FFT is done to recover the velocity fields filtered at f 2 corresponding to the vortex shedding street. An illustration of such a filtering procedure is presented in figure 8. It represents eight instantaneous filtered velocity streamlines from t 1 = 154.5s (graph (a)) to t 2 = 158s (graph (h)). The time interval between two consecutive images is equal to two times the time step of PIV measurements. Vertical lines in figure 6-center indicate the temporal domain under consideration that corresponds to approximately one cycle of 1/ f 2 period of the movement of the codend structure. The vortex shedding street is then clearly visible on such graphs. Due to the limitation of the PIV measurement plane area, one only focuses in the following on the vortex generated and developed in the upper part of the codend. To appreciate the vortex street development, a specific algorithm previously developed [START_REF] Druault | Use of Proper Orthogonal Decomposition for time interpolation from PIV data[END_REF] is applied to detect instantaneously the vortex core on each filtered vector field. Briefly, the vortex center has to fulfill three main criteria : (1) its neighboring instantaneous angular velocities have to be oriented in the same direction, (2) its neighboring angular velocity must have a higher absolute value than the center velocity and (3) the absolute velocity value for the vortex center has to be very low. This algorithm is then applied to each available filtered velocity field enabling the determination of the center coordinates of the vortex developing in the upper part of the wake. As an illustration, these centers are indicated with a black point in each graph of figure 8. Figure 9 (left) displays the temporal evolution of the y-coordinate (normalized with d) of the detected vortex centers. Red points indicate the envelop of this representation. It is quite interesting to observe that the spectrum of the envelop of these y-coordinates exhibits a low frequency component equal to f 1 frequency. This confirms that the vortex street is also linked to the low frequency codend's oscillations. Figure 10 represents a zoom view of the time evolution of the vortex core y-motion during the same 1/ f 2 period previously detailed. It is superimposed on the transverse motion of the codend (already represented in figure 6-center). We observe that the transverse motions of the vortex centers and of the codend are convected away in opposite directions. When the codend has an upward motion, the vortex initiated in the upper shear layer goes downward due to the local low pressure area located just behind the codend. This result emphasizes the great complex non-linear coupling phenomena in the present fluidstructure interactions. Specific mathematical post-processing tools have to be applied to pursue this investigation allowing a characterization of the full understanding of the complex non-linear mechanisms in such a flow configuration. That would also enable to be fully examined the flow instabilities. However, one of the first difficulties of post-processing present database concerns the unsteady character of the moving interface. A solution may consist in using the phase averaged operator based on Proper Orthogonal Decomposition previously developed [START_REF] Druault | POD investigation of the unsteady turbulent boundary layer developing over porous moving flexible fishing net structure[END_REF][START_REF] Bouhoubeiny | Phase-averaged mean properties of turbulent flow developing around a fluttering sheet of net[END_REF]. Such analysis is ongoing.

Conclusion

PIV measurements was carried out to investigate the hydrodynamics around a fluttering 1/10 scaled model codend in the symmetric plane of the structure. We first examined the mean flow properties of the wake developing behind the fluttering structure. It was observed that the mean flow characteristics greatly depend on the structure oscillations and strongly approach the mean flow characteristics of the wake developing behind a rigid body if one considers the vertical extent of the codend's oscillations as a reference length scale. The fluttering motion of the codend was then elucidated. We supposed that the main codend's oscillations are in the transverse and longitudinal directions. The motion of the codend is to be seen as a low-frequency component superimposed to another component associated with the vortex shedding street. The first one is directly related to the tension variation of the warp which enables the fishing structure to be maintained horizontally. The second one corresponds to the lock-in phenomenon related to the vortex street developing behind the fluttering structure. The topology of the low-frequency activity is different from the vortex shedding street. This fluttering motion changes the fluid force acting on the codend and also the local porosity of the codend. As measurements are performed only for a specific flow configuration with a 1/10 scaled codend model, it is then expected that the amplitude of the codend oscillations may be 10 times higher during fishing operations, so a vertical motion of the order of a tenth of the codend diameter.

The periodic vortex shedding affects the codend motion. On the other hand, the oscillating structure will affect the fluid flow around the codend, the fluid force and the vortex pattern. Consequently, a complex fluid-structure interaction is present. The present study is a preliminary work demonstrating that the near wake hydrodynamics of moving codend structure are an intricate interplay between the codend fluttering motion and the shear layer instabilities and large scale vortex shedding. The non-linear flow instabilities require further investigations to elucidate the significant coupling between the fluttering motion and the hydrodynamic behavior. For such a purpose, specific post-treatment tools such as Proper Orthogonal Decomposition would be a solution to perform a phase averaging operation and to provide flow decomposition based on an energetic criterion. It is to be kept in mind that hydrodynamics of the codends might change with higher or lower catches. Present study is a preliminary experiment to reproduce the codend hydrodynamics and the associated codend fluttering motions. It allows the observations of the main flow characteristics developing around oscillating codend. This work also proposes a methodology for the analysis of flow characteristics and associated codend oscillations. It is then expected that present results and especially the codend movements provide significant information that could be extrapolated to a full scale coded under certain conditions.

The knowledge of such flow instabilities related to the codend fluttering motion is of great importance not only for structural models used to predict the selectivity process of the codend [START_REF] O'neill | Presemo : predictive model of codend selectivity -a tool for fishery managers[END_REF] but also to improve the understanding of the force acting on a trawl allowing the characterization of the shape, drag and behavior of the fishing structure during a trawling process, tension and loads in its threads and ropes. This also has a consequence on the fish moving process and then on the selectivity. Finally, some other parameters such as environmental conditions (sea state, ship motions, . . .) have also a non-negligible influence on trawl performance [START_REF] Queirolo | Effects of environmental conditions and fishing operations on the performance of a bottom trawl[END_REF].

Références

Figure 1 : 1 :

 11 Figure 1: 1 :10 scaled model of a bottom trawl. Entrance (left hand side) and moving codend (right hand side).

Figure 2 :

 2 Figure 2: Left : Illustration of a Raw PIV image. Hatched white areas indicate the left part of the codend structure which can not be identified thanks to PIV image due to shadow effects. Right : Sketch of the PIV investigation area around the oscillating codend structure surface. PIV measurements plane included the highest and lowest positions of the codend during measurements. The two dashed vertical lines indicate the locations along with spectral analyses will be conducted (see §3.3 and figure 7).

Figure 3 :

 3 Figure 3: Illustration of two instantaneous PIV vectors fields. Left : lowest position of the codend structure. Right : highest position of the codend structure.

Figure 4 :

 4 Figure 4: Convergence analysis of averaged flow quantities computed from N consecutive snapshots. First line : Mean flow. Streamwise velocity component (left) ; transverse velocity component (right). Center line : Reynolds stresses.u 2 (X i )/U 2 re f (left) ; v 2 (X i )/U 2 re f (right) ; bottom-left : u (X i )v (X i )/U 2 re f .

Figure 5 :

 5 Figure 5: Top-right : Mean streamline of the velocity field. Reynolds tensor components : u 2 (Top-left) v 2 (Bottom-right) u v (Bottom-left).

Figure 6 :

 6 Figure 6: Top : Fluctuations of the x-structure oscillations normalized with the codend diameter (d) and superimposed on the associated oscillatory x-velocity. Center : Fluctuations of the normalized y-structure oscillations superimposed on the associated oscillatory y-velocity. Bottom : Spectral representations (in a log-log scale) of both x-(left) and y-structure oscillations (right).

Figure 7 :

 7 Figure 7: Spectral representation (linear scale) of the streamwise (left) and transverse (right) velocity components along the lines x/d = 0.58 (top) and x/d = 1.27 (bottom). Each spectrum is normalized with the same value.

Figure 8 :

 8 Figure 8: Illustration of a vortex core detection applied to filtered PIV velocity database. Representation of eight instantaneous filtered velocity streamlines (from t 1 = 154.5s (graph (a)) to t 2 = 158s (graph (h)).

Figure 9 :

 9 Figure 9: Time evolution of the transverse y-coordinate (y c ) of the detected vortex cores.

Figure 10 :

 10 Figure 10: Superimposition of the y-displacement (y c /d) of the vortex core (line) and the transverse displacement y/d (dashed line) of the structure.

Ph. Druault and G. Germain / European J. Fluids-Mechanics-B 00 (2016) 1-18


Amina Meriem

Chiraz Zingla

Yahya Latiri

Catherine Slimani

Philippe Berrut

Mulhem

Zingla

-Chiraz Latiri

Yahya Slimani

Catherine Berrut

Philippe Mulhem

Tweet Contextualization Based on Wikipedia and Dbpedia

Keywords: Tweet contextualization, Association rules, INEX, Explicit Semantic Analysis, Query Expansion. MOTS-CLÉS : Contextualisation des tweets, Expansion de requêtes, INEX Analyse Sémantique Explicite, Règles d'association

Introduction

Microblogging has emerged as one of the primary social media platforms for users to submit, in real-time, short messages, to report an idea or an opinion. Twitter is an online social networking service that enables users to tweet about any topic within the 140-character limit called tweets. However, this limit causes users to employ different strategies such as abbreviations and slangs in order to compress more information in minimum of characters. Tweets are, therefore, often misspelled or truncated and especially hard to understand.

To study this problem, INEX (Initiative for the Evaluation of XML retrieval) launched the tweet contextualization track for the first time in 2011. Thus, the tweet contextualization track proposed to answer questions of the form "What is this tweet about?" using a cleaned dump of Wikipedia, in order to allow the reader a better understanding of the tweet. The principle of this task is first to find documents that are related to the tweet (using a information retrieval system IRS), and then to generate an accurate summary of such documents (using an automatic summarization system ASS). INEX organizers provide the task participants with a baseline system that combines IRS and ASS. This baseline system takes as input a text query (tweet). This task allows participants to focus on the best tweet formulation for the IRS, since it has a direct impact on the summary quality.

Our goal here is to study tweet expansions as presented in Figure 1, and to evaluate the results using the INEX baseline system, on the INEX 2014 test collection. Several works have already proposed the use of the tweet expansion methods for the target task such as in [START_REF] Morchid | LIA@inex2012 : Combinaison de thèmes latents pour la contextualisation de tweets[END_REF], where the authors proposed to use Latent Dirichlet Analysis to expand the original tweets, and in [START_REF] Zingla | INEX2014: Tweet Contextualization Using Association Rules between Terms[END_REF], where the authors proposed to use association rules inter-terms, but these approaches do not include a term ranking step. The absence of this ranking step resulted in noisy queries containing unrelated terms to the original tweet. In this paper, we propose to expand the original tweets, using two external knowledge sources, namely, Wikipedia and Dbpedia. We opted to use Wikipedia because it is currently the largest knowledge repository on the Web. The use of this huge source is fruitful by allowing a massive knowledge representation of a tweet. We also use Dbpedia because it provides vast amounts of structured knowledge extracted from Wikipedia info boxes, hence, allowing to augment tweet representation with massive amounts of related information. Taking into account the weaknesses of the existing works cited before, we propose to enhance the tweet expansion process so that it will be composed of two steps : Tweet candidate terms generation, and candidate terms ranking and selection. While the first step extracts related information, i.e. terms, from Wikipedia and Dbpedia, the second step computes the semantic relatedness score between the original tweet and the candidate terms, using a new measure (ESAC) that relies on Explicit Semantic Analysis (ESA) and association rules.

The remaining of this paper is organized as follows: In section 2, our work is put in the context of related works, while section 3 gives a detailed description of our tweet expansion methods. Section 4 presents our experimentations and results. Finally, section 5 is dedicated to the conclusion of this work and gives future works.

Related Work

In this section we review some related works, referring to query expansion and tweet contextualization.

Query expansion

Several works in the literature are proposed for the query expansion task, such as in [START_REF] Song | Integration of association rules and ontologies for semantic query expansion[END_REF] where the authors proposed a novel semantic query expansion technique that combines association rules with ontologies and Natural Language Processing techniques. This technique uses the explicit semantics as well as other linguistic properties of unstructured text corpus, it incorporates contextual properties of important terms discovered by association rules, and ontology entries are added to the query by disambiguating word senses. In [START_REF] Latiri | Query expansion using fuzzy association rules between terms[END_REF], authors addressed query expansion by considering the term-document relation as fuzzy binary relations. Their approach to extract fuzzy association rules is based on the closure of an extended fuzzy Galois connection, using different semantics of term membership degrees. In [START_REF] Shekarpour | Keyword Query Expansion on Linked Data Using Linguistic and Semantic Features[END_REF], authors proposed an approach based on performing an initial retrieval of resources according to the original keyword query, the proposed process is divided into three main steps. In the first step, all words closely related to the original keyword are extracted based on two types of features linguistic and semantic. In the second step, various introduced linguistic and semantic features are weighted using learning approaches. In the third step, they assign a relevance score to the set of the related words. Using this score they prune the related word set to achieve a balance between precision and recall. In these two previous works, relations weighting aspect are important. Authors in [START_REF] Tan | Multimedia Information Modeling and Retrieval (MRIM) /Laboratoire d'Informatique de Grenoble (LIG) at CHiC2013[END_REF] proposed a semantic approach that expands short queries by semantically related terms extracted from Wikipedia, they incorporate the expansion terms into the original query and adapt language models to evaluate the expanded queries. The proposal of the paper is therefore, in a certain way, a continuation of these works, but in the case of tweet contextualization.

Query expansion for microblog retrieval

Query expansion techniques are also used for microblog retrieval, authors in [START_REF] Bandyopadhyay | Query expansion for microblog retrieval[END_REF], for example, used external corpora as a source for query expansion terms. Specifically, they used the Google Search API (GSA) to retrieve pages from the Web, and expanded the queries employing their titles. In [START_REF] Lau | Retrieval Using Topical Features and Query Expansion[END_REF], authors proposed a twitter retrieval framework that focuses on topical features, combined with query expansion using Pseudo-Relevance Feedback (PRF) to improve microblogs retrieval results.

Tweet contextualization

Despite the fact that the idea to contextualize tweets is quite recent, there are several works in this context. Recently, authors of [START_REF] Ermakova | IRIT at INEX 2012: Tweet Contextualization[END_REF] proposed a method based on the local Wikipedia dump, they used the Term Frequency-Inverse Document Frequency TF-IDF cosine similarity measure enriched by smoothing from local context, named entity recognition and Part-Of-Speech weighting presented at INEX 2011. They modified this method by adding bigram similarity, anaphora resolution, hashtag processing and sentence reordering. The sentence ordering task was modeled as a sequential ordering problem, where vertices corresponded to sentences and sentence time stamps represented sequential constraints. They proposed a greedy algorithm to solve the sequential ordering problem based on chronological constraints. While in (Deveaud and Boudin, 2013a), authors used a method that allows to automatically contextualize tweets by using information coming from Wikipedia. They treated the problem of tweets contextualization as an automatic summarization task, where the text to resume is composed of Wikipedia articles that discuss the various pieces of information appearing in a tweet. One of the limitations of this approach is that the number of Wikipedia articles used to extract the candidate sentences is set manually. They explore the influence of various tweet-related articles retrieval methods as well as several features for sentence extraction. Whereas, in [START_REF] Deveaud | Effective Tweet Contextualization with Hashtags Performance Prediction and Multi-Document Summarization[END_REF], authors added a hashtag performance prediction component to the Wikipedia retrieval step. They used all available tweet features including web links which were not allowed by INEX's organizers. In [START_REF] Linhares | An Automatic Greedy Summarization System at INEX 2013 Tweet Contextualization Track[END_REF], authors used an automatic summarizer named REG based on a greedy optimization algorithm to weigh the sentences. The summary is obtained by concatenating the relevant sentences weighed in the optimization step. In [START_REF] Morchid | LIA@inex2012 : Combinaison de thèmes latents pour la contextualisation de tweets[END_REF], authors used Latent Dirichlet Analysis (LDA) to obtain a representation of the tweet in a thematic space. This representation allows the finding of a set of latent topics covered by the tweet, this approach gives good results for the tweet contextualization task. Authors in [START_REF] Zingla | INEX2014: Tweet Contextualization Using Association Rules between Terms[END_REF] use the association rules between terms to extend the tweet, they project the terms of tweet on the rules' premises and add the conclusions to the original tweets.

Closely similar to our task, the authors in [START_REF] Meij | Adding semantics to microblog posts[END_REF], aim at adding semantics to microblog posts. They proposed a method that uses machine learning, and is based on a high-recall concept ranking and a high-precision concept selection step.

Finally, in (Torres-Moreno, 2014) authors developed three statistical summarizer systems the first one called Cortex summarizer, that uses several sentence selection metrics and an optimal decision module to score sentences from a document source, the second one called Artex summarizer, that uses a simple inner product among the topic-vector and the pseudo-word vector and the third one called REG summarizer which is a performant graph-based summarizer.

Tweet Expansion

The tweet expansion aims at augmenting the thematic space of a given tweet by a massive amount of related terms. This is done to improve the IRS performance by giving more chance for a relevant document, which does not contain the original tweet terms, to be retrieved. In our work, we expand the original tweets in two steps (cf. Figure 2), namely:

-Step 1: Candidate terms generation.

-Step 2: Candidate terms ranking and selection. The first step generates the candidate terms, for a given tweet, from Wikipedia and Dbpedia, the second one ranks these candidate terms according to their similarity to the tweet, and selects the best ones to be added. Three alternatives are proposed for the candidate terms generation step. Two are based on Wikipedia and one is based on Dbpedia.

Step 1: Candidate Terms Generation

Wikipedia is a huge knowledge source growing every day due to the contribution of people all around the world, it represents a very large, high quality, and valuable source in natural language, this is why we opted it to extend the tweets. We proposed to use two different methods to extract information related to a given tweet from this source. The first one is a statistical method based on association rules mining, the second one based on text mining. Authors, in [START_REF] Tan | Multimedia Information Modeling and Retrieval (MRIM) /Laboratoire d'Informatique de Grenoble (LIG) at CHiC2013[END_REF], have proved, in another frame, that the use of WIkipedia is interesting for the query expansion. Dbpedia is a project aiming to represent Wikipedia content in RDF triples. It plays a central role in the Semantic Web, due to the large and growing number of resources linked to it. We claim that the use of these knowledge sources will augment the tweet representation by a massive amount of related information. As DBpedia is structured and filtered, we expect less noisy expansions than with Wikipedia. However, using DBpedia may generate smaller expansions than Wikipedia. Using both sources is expected to select precise terms (from DBpedia) as well as broad terms (from Wikipedia). We propose to generate candidate terms for a given tweet using different methods based on these knowledge sources, Wikipedia and Dbpedia, namely: Association rules mining, text mining, and knowledge base extraction.

Association Rules Mining

An association rule binds two sets of terms, which respectively constitute its premise (T 1 ) and conclusion (T 2 ) parts. Thus, a rule approximates the probability of having T 2 in a document, given that those of the T 1 are already there. Compared to simple co-occurrences measures, association rules are then oriented, and we believe that this point is a must in our case, as we try to find new query terms from the initial tweet. The rule R is said to be based on the termset T equal to T 1 ∪ T 2 . The support of T is equal to the number of documents in C containing all the term of T . The support is formally defined as follows:

Supp(T ) = |{d|d ∈ C ∧ ∀t ∈ T : (d, t) ∈ I}| [1]
Where:

- The confidence of a rule R: T 1 ⇒ T 2 is computed as:

C
Conf(R) = Supp(T ) Supp(T 1 ) . [2]
An illustrative example of association rules is highlighted in Table 1. An association rule R is said to be valid if its confidence value, i.e., Conf(R), is greater than or equal to a user-defined threshold denoted minconf. This confidence threshold is used to exclude non valid rules. The process of generating candidate terms for a tweet is performed in the following steps:

-Selecting a sub-set of articles, from Wikipedia, similar to the tweet, using an algorithm based on the TF-IDF measure [START_REF] Xia | An improvement to TF-IDF: Term Distribution based Term Weight Algorithm[END_REF].

-Annotating the selected articles using TreeTagger. The choice of TreeTagger was based on the ability of this tool to recognize the nature (morpho-syntactic category) of a word in its context. TreeTagger uses the recursive construction of decision trees with a probability calculation to estimate the Part-Of-Speech of a word.

-Extracting specific terms, we extract terms that are nouns.

-Generating the association rules using an efficient algorithm called CHARM. We adapted the algorithm CHARM [START_REF] Zaki | An efficient algorithm for closed itemset mining[END_REF], because it allows to generate non-redundant association rules [START_REF] Yahia | Approches d'extraction de règles d'association basées sur la correspondance de Galois[END_REF]. As parameters, CHARM takes minsupp as the relative minimal support and minconf as the minimum confidence of the rules. While considering the Zipf distribution of the selected sub-set of Wikipedia articles, the minimal threshold of the support value is experimentally set in order to spread trivial terms which occur in the most of the documents, and are then related to too many terms.

-Obtaining the candidate terms for a given tweet, the candidate terms are the terms that appear in the conclusions of the irredundant association rules whose premise is included in the original tweet.

Text Mining

The second method to generate candidate terms for a given tweet from Wikipedia consists in exploring the Wikipedia articles related to the tweet, especially, the articles' first sentences that we call: definitions.

To achieve this, we use some heuristics, namely:

-Given a tweet, first, we search, in Wikipedia, all articles that correspond to the tweet's words. (Since the tweets are shorts, we consider all the words of tweet) This is done using WikipediaMiner1 , which is a toolkit developed for tapping the rich semantics encoded within Wikipedia. This toolkit helps to integrate Wikipedia's knowledge into applications , by: -Providing simplified, object-oriented access to Wikipedia's structure and content.

-Measuring how terms and concepts in Wikipedia are connected to each other.

-Detecting and disambiguating Wikipedia topics when they are mentioned in documents.

-We extract, from these articles, their corresponding definitions.

-We annotate these definitions using TreeTagger, then, we extract specific terms (nouns) from these annotated definitions. They are the candidate terms for the original tweet.

Knowledge Base Extraction

The Dbpedia Ontology currently contains about 4,233,000 instances. The table 2 below lists the number of instances for several classes withClass Instances Dbpedia concepts are described by short and long abstracts in 13 different languages. These abstracts have been extracted from the English, German, French, Spanish, Italian, Portuguese, Polish, Swedish, Dutch, Japanese, Chinese, Russian, Finnish and Norwegian versions of Wikipedia We use information coming from Dbpedia to extend the original tweet. This method consists in extracting for each term in the original tweet, a set of related information from the Dbpedia ontology. These related information present the candidate terms for the original tweet. This is done using a SPARQL query: by rdf : type. SPARQL query is a semantic query language for databases, able to retrieve and manipulate data stored in Resource Description Framework (RDF) format.

Step 2 : Candidate Terms Ranking and Selection

This step consists in ranking the candidate terms according to their semantic relatedness to the given tweet, and selecting the best ones to be added. To achieve this, we propose a new semantic relatedness measure (ESAC) that combine the Wikipedia-based Explicit Semantic Analysis (ESA) measure and the association rules' confidence value. Explicit Semantic Analysis (ESA) is a promising approach that calculates semantic relatedness proposed by [START_REF] Gabrilovich | Computing Semantic Relatedness Using Wikipedia-based Explicit Semantic Analysis[END_REF]. It is a vectorial representation of text that uses Wikipedia as a knowledge base. Specifically, in ESA, a word is represented as a column vector in the TF-IDF matrix of the corpus and a text is represented as the centroid of the vectors representing its words. Semantic relatedness of two given texts can be obtained by calculating the correlation between two high dimensional vectors generated by ESA.

-The ESAC measure between a tweet tw and a term w is defined as:

ESACtw, w = α ESAtw, w + (1 -α) Conf R(tw,w) if ∃ R(tw, w); ESAtw, w, otherwise. [3]
Where -ESAtw, w is the score of relatedness between the tweet tw and the candidate term w calculated as follow:

ESAtw, w = tw.w tw 2 w 2 . -Conf R(tw,w) = M AX {Conf Rj (wt,w) } with M AX{A} = m , m ∈ A is a maximal element of A if for all s ∈ A, m ≤ s implies m = s.
-Conf R(wt,w) is the confidence of the rule R that express the association between the candidate term w and a word in tweet wt.

α is a weighting parameter ∈ [0, 1].

We used the ESA implementation described in [START_REF] Gabrilovich | Computing Semantic Relatedness Using Wikipedia-based Explicit Semantic Analysis[END_REF], we realized our runs with the best parameter value obtained by experiments. Once we the semantic relatedness between the tweet and the candidate terms, we selecting the most related ones that have semantic relatedness score greater than a determined threshold and adding them to the original tweet.

Experimentations

Once, as described above, we have generated the extended tweets, we need to extract the tweet context. This context takes the form of an easy-to-read summary, composed of passages from a provided Wikipedia corpus.

To achieve this, as described in the Introduction, we use the system provided by the INEX 2014 Tweet Contextualization organizers2 composed of : an Information Retrieval System (IRS) to find the most relevant Wikipedia articles, and an Automatic Summarizer System (ASS) to extract, from the relevant Wikipedia articles, the passages most representative of the tweet. This system was available to participants through a web interface or a Perl API. The system receives as input a query and returns a context. This latter consists of Part-Of-Speech (POS) sentences annotated with TreeTagger . This annotation process allows to assign a score for each sentence using TermWatch. The set of sentences, not exceeding 500 words (this limit is established by the organizers), defines the context of the tweet.

Data Collection

The tested INEX 2014 collections contains:

1) A corpus of 3 902 346 articles rebuilt in 2013 from a dump of the English Wikipedia of November 2012. All notes and bibliographic references that are difficult to handle are removed and only non-empty Wikipedia pages (pages having at least one section) are kept. Resulting documents are made of a title (title), an abstract (a) and sections (s). Each section has a sub-title (h). The abstract and sections are made of paragraphs (p) and each paragraph can have entities (t) that refer to Wikipedia pages. Each document is provided in XML format.

2) A collection of English tweets, composed of 240 tweets selected from the CLEF RepLab 2013. To focus on content analysis alone, urls are removed from the tweets.

Runs

We conducted different runs (cf. Table 3), namely: The parameters fixed for the experiments are: α = 0.5, minsupp = 15 ,minconf = 0.7.

Evaluation Metric

We have evaluated our runs according to the Informativeness metric, this latter is proposed by the INEX organizers, it aims at measuring how well the summary helps a user understand the tweets content. There are actually 47 tweets, from the 240 tweets, used for the INEX 2014 evaluation. For each tweet, each passage will be evaluated independently from the others, even in the same summary. The results are based on a thorough manual run on 1/5 of the 2014 topics using the baseline system. From this run two types of references were extracted, namely:

-a list of relevant sentences per topic.

-extraction of Noun Phrases from these sentences together with the corresponding Wikipedia entry.

The dissimilarity between a reference text and the proposed summary is given by:

Dis(T, S) = t∈T (P -1) × 1 - min(log(P ), log(Q)) max(log(P ), log(Q)) [4]
where :

-T , a set of query terms present in reference summary.

-S, a set of query terms present in a submitted summary.

f T (t), the frequency of term t in reference summary.

f S (t), the frequency of term t in a submitted summary.

-

P = f T (t) f T + 1. -Q = f S (t)
f S + 1. There are different distributions for the reference summaries, namely:

-Unigrams made of single lemmas (after removing stop-words).

-Bigrams made of pairs of consecutive lemmas (in the same sentence).

-Bigrams with 2-gaps also made of pairs of consecutive lemmas but allowing the insertion between them of a maximum of two lemmas (Also referred to as skip distribution).

Results

proportion of the improvement / descent in the Bigrams with 2-gaps compared to run 361

Within INEX 2014

Table 4 presents the official runs submitted by INEX 2014 participants from six countries (Canada, France, Germany, India, Russia, Tunisia).

We submitted the run:run-Wikipedia-RA (361) (c) to take part of the INEX 2014 competition, and we achieved the best informativeness results (the results are sorted by performance on Bigrams with 2-gaps and the lowest scores represent the best runs).

After INEX 2014

After winning the INEX 2014 competition for the tweet contextualization task, we continued to improve our results. run-ESAC-Wikipedia-textmining (b), run-ESAC-Wikipedia-RA (d), run-ESAC-Dbpedia (f) outperform run-Wikipedia-textmining (a), run-Wikipedia-RA (c), run-Dbpedia (e) this is due to the term ranking step that reduced the noise in the extended tweets by eliminating the non related terms, and fine-grained the semantic representation of the tweets, indeed, the use of association rules that led to the enforcement of the relatedness score between the candidate terms and the tweet, ensured that the extended tweets contain adequate correlating terms with the initial ones and helped avoid inclusion of non-similar terms in them as much as possible, so the extended tweets were, to some extent, clean. We performed a bilateral paired Student t-test to evaluate the statistical significance of the differences of the averages between our best official run from INEX 2014 run-Wikipedia-RA (361) (c) from Table 4 and the best run from our proposals, run-ESAC-Wikipedia-Dbpedia (g) from table 5.

The differences, respectively for Unigrams, Bigrams or Bigrams with 2 gaps, are not significant according to the significance threshold of 0.05. However, we noticed that, for three topics (with tweet ids 257798105473380352, 262290292173045762 and 276815901897146368), the run-ESAC-Wikipedia-Dbpedia (g) largely underperforms the run-Wikipedia-RA (361) (c).

Table 6 presents the tweet with id 257798105473380352. Table 7 shows the expansions used for the runs run-Wikipedia-RA (361) (c) and run-ESAC-Wikipedia-Dbpedia (g) for the topic 257798105473380352. From this table, we find that the number of terms added for run-Wikipedia-RA (361) (c) is much smaller than for run-ESAC-Wikipedia-Dbpedia (g). In this large number of terms (exceeds 30) for the run-ESAC-Wikipedia-Dbpedia (g), noisy terms appear like "revenue", "english" and "country". The same observation is made on the two others topics mentioned above. We believe that this problem may be corrected in the future by limiting the number of added terms for the query expansion.

We propose to compare the runs run-Wikipedia-RA (361) (c) and run-ESAC-Wikipedia-Dbpedia (g), when our proposal run-ESAC-Wikipedia-Dbpedia (g) does not fail. We obtain the results presented in table 8. The respective differences between the run-Wikipedia-RA (361) (c) and our proposal run-ESAC-Wikipedia-Dbpedia (g) on these 44 topics are all statistically significant (noted † in table 8) according to bilateral paired Student t-tests with significance threshold of 0.05. This proves that, if we are able to detect in advance when our proposal fails, we can greatly improve the state of the art results.

Table 6. The tweet with id 257798105473380352.

automotive Fiat S.p.A CNH-Fiat merger CNH rejects merger proposal from Fiat Industrial: US farm and industrial vehicle group CNH has rejected a merger proposal from its pa... 2012-10-15-13:00

Conclusion and Perspectives

In this paper, we presented our works in the tweet contextualization field. We proposed different methods, based on Wikipedia and Dbpedia, to expand the tweets. Our proposed methods are divided into two steps, the first step generates the candidate terms and the second one ranks them and expends the original tweets by the most related ones. We conducted our experimentations on the INEX 2014 collection. The results we obtained through the different performed runs showed a significant improvement in the informativeness of the contexts, and have outperformed the winning run, which we submitted to the INEX 2014 tweet contextualization task. In our future work, we intend to extend our approach to take into account the tweets specificities (#,@,...). Furthermore, we mean to generalize our approach will ChiC). This latter contains short queries that have no sufficient information to express their semantic.

Figure 1 .

 1 Figure 1. Explanatory schema of the proposed work

Figure 2 .

 2 Figure 2. The Proposed Methods for Tweet Expansion

  is the whole set of documents which form the collection d is a single document of the collection (d ∈ C) -τ the whole set of distinct terms of the collection C -T a set of terms of the collection (T ⊆ τ ) -t a single term of the collection (t ∈ τ ) -I ⊆ C × T is a binary (incidence) relation. Each couple (d, t) ∈ I indicates that the document d ∈ C has the term t ∈ τ .

  (a) run-Wikipedia-textmining: Tweet expansion based on text mining without terms ranking and selecting step. (b) run-ESAC-Wikipedia-textmining: Tweet expansion based on text mining with terms ranking and selecting step. (c) run-Wikipedia-RA: Tweet expansion based on association rules mining without terms ranking and selecting step. (d) run-ESAC-Wikipedia-RA: Tweet expansion based on association rules mining with terms ranking and selecting step. (e) run-Dbpedia: Tweet expansion based on knowledge base (Dbpedia) without terms ranking and selecting step. (f) run-ESAC-Dbpedia: Tweet expansion based on knowledge base (Dbpedia) with terms ranking and selecting step. (g) run-ESAC-Wikipedia-Dbpedia: Combining run-ESAC-Wikipedia-textmining, run-ESAC-Wikipedia-RA and run-ESAC-Dbpedia.

Table 1 .

 1 Association Rules examples taken from Wikipedia articles

	Premise	Conclusion Support Confidence
	manufacture car	356	0.8921
	manufacture motor	301	0.9102
	campus	university	279	0.7431

Table 2 .

 2 Instances per class.

	Resource(overall) 4,233,000
	Place	735,000
	Person	1,450,000
	Work	411,000
	Species	251,000
	Organisation	241,000

Table 3 .

 3 The conducted runs.

		ESAC Ranking and selection No ranking, no selection
	Association rules mining	d	c
	Text mining	g b	a
	Knowledge base extraction	f	e

Table 5

 5 depicts our obtained results. As seen in the table, run-ESAC-Wikipedia-Dbpedia (g) has achieved the best informativeness results and has outperformed the

Table 4 .

 4 INEX Tweet Contextualization 2014 official informativeness results based on sentences.

	Run	Unigrams Bigrams Bigrams with 2-gaps INEX system
	361 (c) 0.7632	0.8689	0.8702	YES
	360	0.782	0.8925	0.8934	YES
	368	0.8112	0.9066	0.9082	NO
	369	0.814	0.9098	0.9114	NO
	359	0.8022	0.912	0.9127	YES
	370	0.8152	0.9137	0.9154	NO
	356	0.8415	0.9696	0.9702	NO
	357	0.8539	0.97	0.9712	NO
	364	0.8461	0.9697	0.9721	-
	358	0.8731	0.9832	0.9841	NO
	363	0.8682	0.9825	0.9847	-
	362	0.8686	0.9828	0.984	-

Table 5 .

 5 The obtained informativeness results based on sentences.

	Run Unigrams Bigrams Bigrams with 2-gaps
	g	0.7494	0.8520	0.8535
	b	0.7613	0.8629	0.863
	f	0.7610	0.8629	0.8638
	a	0.7665	0.8661	0.8668
	d	0.7612	0.8671	0.8695
	c	0.7632	0.8689	0.8702
	e	0.7940	0.8822	0.8831
	other runs, this is due to the combination of the information coming from the two
	knowledge sources, Wikipedia and Dbpedia.

Table 7 .

 7 The tweet (id= 257798105473380352) expansions by run-Wikipedia-RA (361) (c) and run-ESAC-Wikipedia-Dbpedia (g)

	Tweet id	Expansion for run c	Expansion for run g
	257798105473380352 car vehicle government	business car vehicle
		acquisition model	manufacturing engine group
		market automaker auto	turin acquisition model
		production america state production market
			auto fiat industry
			design manufacture world
			sector revenue factory
			torino italy automotive
			automobile manufacturer
			sector revenue usa
			company organisation
			country english automaker
			government world

Table 8 .

 8 Informativeness results based on sentences, on the 44 selected runs.

	Run Unigrams Bigrams Bigrams with 2-gaps
	g	0.7364 †	0.8439 † 0.8454 †
	c	0.7800	0.8912	0.8923
	contextualize normal (regular) queries by applying it on other data collections such as
	Cultural Heritage in CLEF collection (

http://wikipedia-miner.cms.waikato.ac.nz/

http://qa.termwatch.es/data

Acknowledgments. This work is partially supported by the French-Tunisian project PHC-Utique RIMS-FD 14G 1404.