Tiantian Xu

Gilgueng Hwang

Nicolas Andreff

Stéphane Régnier

Scaled-up Helical Nanobelt Modeling and Simulation at Low Reynolds Numbers

Micro and nanorobots can change many aspects of medicine by enabling targeted diagnosis and therapy, and minimal invasive surgery. A helical nanobelt with a magnetic head was proposed as a microrobot driven by rotating magnetic field in prior works. Magnetically coated tails were already shown in some works. However the control of such surface magnetic tails is not clearly realized yet. This paper aims to obtain control parameters for the modeling and simulation of the influence of surface magnets onto the swimming performances. For this, we created scaled-up helical nanobelts and the experimental testbed to get the control parameters and to prepare future closed-loop control.

I. INTRODUCTION

Micro and nanorobots have the potential to change many aspects of medicine. Microrobots can be used for the localized delivery of chemical and biological substances (e.g. targeted drug delivery, hyperthermia), to remove material by mechanical means, to act as simple controllable static structures or to transmit biological data from a specific location hard to reach [START_REF] Nelson | Microrobots for minimally invasive medicine[END_REF].

Swimming at the microscale is considered as swimming at low Reynolds numbers. Helical propeller inspired by nature's bacterial swimming behaviors is promising for medical microrobots, because helical propulsion is suited to a low Reynolds number regime [START_REF] Purcell | Life at low reynolds number[END_REF], [START_REF] Abbott | How should microrobots swim ?[END_REF]. In Japan, researchers proposed the first spiral type swimming mechanism with a cubic magnetic head driven by a rotating magnetic field [START_REF] Honda | Micro swimming mechanisms propelled by external magnetic fields[END_REF]. This micro swimming mechanism was as large as a few millimetres. This group tested the swimming performances of spiral type micro-machines at low Reynolds number. They predicted that this spiral type micro-machine with a length of 11.5 mm could still be scaled down to micrometer-sized machine [START_REF] Ishiyama | Spiraltype micro-machine for medical applications[END_REF].

Nanorobots are significantly smaller than microrobots, and typically envisioned as devices to target individual cells [START_REF] Patel | Nanorobot : a versatile tool in nanomedicine[END_REF] simple medical task. They are still developing and characterizing the fundamental building blocks of future nanorobotic devices [START_REF] Nelson | Microrobots for minimally invasive medicine[END_REF].

The first helical nanorobot called the "Artificial Bacterial Flagella" (ABF), with a length of approximately 50 µm and a thickness of approximately 30 nm was developed by a group at ETH Zurich [START_REF] Zhang | Artificial bacterial flagella : Fabrication and magnetic control[END_REF], [START_REF] Zhang | Characterizing the swimming properties of artificial bacterial flagella[END_REF], [START_REF] Zhang | Artificial bacterial flagella for micromanipulation[END_REF]. The ABF consists of a helical tail made by GaAs/InGaAs and a thin-square-plate soft-magnetic "head" on one end. A low-strength rotating magnetic field acts on ABF's magnetic "head" to drive it. Further size reduction and applying the same principle to drive artificial magnetic propellers with a length of approximately 1 µm was achieved in [START_REF] Ghosh | Controlled propulsion of artificial magnetic nanostructured propellers[END_REF].

A group at ISIR and LPN demonstrated that electroosmotic propulsion of "Helical NanoBelt" (HNB) showed much higher swimming performances in terms of maximum swimming velocity and manipulation force. Moreover, a 10 nm thick ferromagnetic metal layer was successfully added to the surface of helical tails [START_REF] Hwang | Remotely powered propulsion of helical nanobelts[END_REF], [START_REF] Hwang | Electro-osmotic propulsion of helical nanobelt swimmers[END_REF]. This surface-coated HNBs has a similar geometry to ABF, but their entire surface can be functional to propulsion in comparison to the ABF's soft magnetic head as the only functional part and the tail as a passive part.

Although all surface magnetic tails are advantageous, their control has not been clearly realized mainly due to the limited observation tools to characterize the swimming characteristics and to identify the control parameters in micro and nanoscale. This prevents their microrobotic applications which usually need to implement the closed loop control. In this paper, we propose to assemble Scaled-up Helical Nanobelts (SHNs) and to develop their control testbed to observe the propulsion characteristics (e.g. cut-off frequency, maximal translational speed) and to identify the control parameters (e.g. propulsion matrix) of helical microrobots. This proposed methods and system enable low-cost, simple geometry modifications, fast and easy control parameter identifications. This system reveals different propulsion characteristics and control parameters of two different SHNs with one or four pitch magnetic surfaces.

Section II presents an overview of the system including a control setup and SHNs used in the experiments. Section III introduces the control principle of the SHNs. Section IV presents the characteristics of the SHNs, including cut-off frequency, identification of the propulsion matrix, forces and torques analyses and translation tests.

II. SYSTEM OVERVIEW

This section presents an overview of our system. The latter (see Fig 1a) consists of a control system and a tube containing the SHN immersed in a viscous liquid named hereafter the target system. The control part controls the SHNs in the target part.

A. Control system

Researchers utilized orthogonal arrangements of electromagnetic coil pairs to generate rotating uniform magnetic fields at the center of the system's workspace. However, these coil pairs are difficult to scale up [START_REF] Abbott | How should microrobots swim ?[END_REF]. A rotating permanent magnet allows us to control a scaled-up model wirelessly [START_REF] Fountain | Wireless control of magnetic helical microrobots using a rotating-permanentmagnet manipulator[END_REF]. A linear motor stage contributes to give a translation motion.

The magnet used in the experiments is cylindrical, 60 mm in length and 15 mm in diameter, mounted on a Maxon DC Motor. The magnetic field magnitudes around the magnet were measured using a Hirst GM08 gaussmeter, and are depicted by Fig 2, in which the magnet is put along the x axis, and the center of the magnet is at the origin.

The rotating magnet manipulator is mounted on a Newmark System linear motion stage, whose travel range is 250 mm and maximal speed is 220 mm/s.

B. Target system

The target system is a SHN encapsulated in a transparent tube filled with viscous liquid. Liquids used in the experiment are different glycerol solutions.

The target system is placed from 1.5 mm to 3.5 mm away from the magnet axis. The magnetic field magnitude ranges from 10 mT to 70 mT.

Two types of SHNs were fabricated for the experiments. The first type of SHNs named SHN1 (see Swimming microscale objects are largely limited in their performances due to the low Reynolds dynamics [START_REF] Abbott | How should microrobots swim ?[END_REF]. The so-called Reynolds number, a dimensionless quantity that embodies the interaction between the inertia and viscosity of a fluid, is defined as :

Re = ρvL µ ( 1
)
where v is the velocity, ρ is the density of the fluid, L is a characteristic linear dimension (length of helical robot) and µ is the dynamic viscosity of the fluid.

In the following experiments, the liquids in which the SHNs swim are different glycerol solutions in order to analyse the influences of Reynolds numbers on the propulsion of SHNs. Table II summarizes the viscosities of different glycerol solutions at 10 • C and the Reynolds numbers for HNB in water and for SHNs in different glycerol solutions.

C. Measurement system

A Basler camera running at 60 Hz is used to measure the dropping velocities. The dropping velocities are calculated from the video recorded by the pixels travelled in unit time. The angular speeds are measured by the time that a SHN uses to finish a whole turn rotation.

III. MODELLING AND CONTROL

We demonstrate in this section the rotation principle by SHN1. Its magnetic pitch should be put in the area where the magnetic field is locally strong, called functional area. There are two functional areas in front of the magnet : on its left-hand side and on its right hand side, which are presented by Fig. 4a.

The magnet manipulator rotates in clockwise direction. The magnetic pitch is put in front of the magnet manipulator in the left-hand side, shown in Fig. 4e. Fig. 4a shows the magnetic field in the yOz plane generated by the magnet at positions 0 degree. All of the magnetic field vectors are in the yOz plane. While the magnet rotates 30 degree, the magnetic field vectors lean down, as can be seen in Fig. 4b and4c, and they tend to align an easy axis of the magnetic helical tail along the field. Although there are two possible easy axes (see Fig. 4d), the one that is closer to the local applied field acts as the easy axis, which is the first way of magnetization in Fig. 4d. This magnetization axis follows the local applied field to lean down. Therefore, the helical tail rotates in the counter clockwise direction of z direction, seen by its head.

If the magnetic pitch is put in the right-hand of the magnet manipulator, as presented by Fig. 4f, the second magnetization axis in Fig. 4d acts as an easy axis, because it is closer to the local applied field described by Fig. 4c. In this case, the helical tail rotates in the clockwise direction, seen by its head. Finally, if the magnetic pitch is put in the middle area of magnet manipulator, the helical tail cannot rotate. These phenomena are proved by experiments.

For SHN4, it's enough that one of its pitches is in the functional area. The principle and the direction of rotation are the same as that of SHN1. But if two pitches of SHN4 are in the different functional areas of magnet, the SHN4 will be exerted two torques in opposite direction. Therefore, the SHN4 cannot rotate.

A. Forces and torques

Swimming along the axis of a helical propeller is known. The nonfluidic applied force (f ) and nonfluidic applied torque (τ ) on a helical swimmer are linearly related to its velocity (v) and angular speed (ω) with four principal quantities described by a symmetric propulsion matrix [START_REF] Purcell | Life at low reynolds number[END_REF], [START_REF] Abbott | How should microrobots swim ?[END_REF] :

f τ = a b b c v ω (2)
The parameters a, b, and c encapsulate the geometric and environmental properties of the helical robot. The nonfluidic applied forces for SHNs are gravitational, buoyancy, friction and magnetic forces while the only nonfluidic torque is the magnetic torque.

1) Apparent weight: The apparent weight is the net force between gravitational force and buoyancy force, which is defined as :

-→ f w = (ρ (SHN ) -ρ f ) - → g V (SHN ) (3)
where ρ (SHN ) is the density of the SHN, ρ f is the density of the fluid and V (SHN ) is the volume of the SHN, which is shown in Table I.

2) Friction force: Friction is the force resisting the relative motion of solid surfaces, fluid layers and material elements sliding against each other. Friction force is proportional to the apparent weight :

f F = µf w ( 4
)
where µ is the coefficient of friction.

3) Magnetic force and torque: The magnetic force and torque that are exerted on an object with uniform magnetization -→ M in a magnetic field with flux density -→ B are defined as [START_REF] Yesin | Modeling and control of untethered biomicrorobots in a fluidic environment using electromagnetic fields[END_REF] :

-→ f m = V m ( -→ M • ) - → B (5)
-→ τ m = V m -→ M × - → B ( 6
)
where V m is volume of the magnetic part of SHN. An applied magnetic field -→ B at the location of the magnetic SHN transduces into torque τ m and spatial magnetic gradient -→ B transduces into applied force -→ f m .

IV. CHARACTERIZATION OF SCALED-UP HELICAL NANOBELT

A. Propulsion characteristics of SHNs

Step-out frequencies of helical robot in their rotating propulsions have been revealed in [START_REF] Ishiyama | Spiraltype micro-machine for medical applications[END_REF], [START_REF] Zhang | Characterizing the swimming properties of artificial bacterial flagella[END_REF]. Researchers found that the forward velocity grows linearly with the applied field until a step-out frequency. Beyond this step-out frequency, the available magnetic torque is no longer sufficient to keep the microrobot rotating in sync with the applied field, resulting in a smooth but drastic decrease in velocity.

For SHNs, we observed rather a cut-off frequency. The SHNs rotate in sync with the applied magnetic field until the cut-off frequency is reached. Beyond this cut-off frequency, SHNs suddenly do not rotate anymore, but oscillate.

The cut-off frequency strongly depends on the viscosity of the liquid in which SHNs swim The apparent weight of SHN4 is bigger than that of SHN1. The friction is proportional to the apparent weight. Therefore, more torque is required to rotate SHN4 than SHN1 with the same rotation frequency, as can be seen on Fig. 5. The rotating magnet provides the same magnetic torque to SHN1 and SHN4. Thus, cutoff frequency of SHN4 is smaller than that of SHN1 in the same viscous liquid for the same rotation frequency.

Two methods are viable for surpassing the cut-off frequency : to increase the magnetic torque by increasing the magnetic field magnitude and to reduce the helical belt weight. The method of increasing the magnetic field magnitude is the most suitable for microscale.

B. Propulsion matrix identification

The a, b, c parameters in (2) need be identified. The sign convention is defined as that the downward direction is positive for f and v. The right-handed chirality rotation direction is defined as positive for ω and τ .

The first experiment consists in dropping SHN1 freely in the 75% glycerol solution by placing the tube vertically. According to the parameters of SHN1 in Table I, its gravity force is measured as 0.02 N and the buoyancy force due to 75% glycerol solution is calculated as 0.01 N from the measured volume (1 mL). The nonfluidic applied force is the apparent weight calcuted as 0.01 N. The measured dropping velocity is 102 mm/s and the measured angular speed is 5.7 rad/s.

If the SHN had a certain negative angular speed, its dropping velocity could be zero, because the propulsive force and buoyancy force would balance the gravity force. Unfortunately, below the cut-off frequency, this balance of forces is not reached.

The second experiment consists in dropping down SHN1 within an active rotating magnetic field. The external magnetic field gives a negative torque to SHN1. SHN1 drops with left-handed chirality rotation. The dropping velocities decrease. By adjusting the rotation frequency of the applied magnetic field to modify the angular speed of SHN1, the falling down velocity of the latter can be modified (see Table III). The velocities and angular speeds are noted v i and ω i for rotating magnetic field of i hz.

We assumed that the magnetic forces for different rotating magnetic fields are identical. Then, we calculate the propulsion matrix from the measures of dropping with 0 Hz, 1 Hz and 2 Hz external rotating magnetic field. From (2), the nonfluidic applied force f is expressed as :

f = av 1 + bω 1 = av 2 + bω 2 (7)
with v 1 = 83 mm/s, v 2 = 74 mm/s, ω 1 = -6.3 rad/s and ω 2 = -12.6 rad/s. Equation [START_REF] Zhang | Artificial bacterial flagella : Fabrication and magnetic control[END_REF] gives us a relationship between a and b, that 0.009a = -6.3b. The nonfluidic force for the first experiment is the apparent weight 0.01 N, can be expressed as :

f 0 = av 0 + bω 0 = -71.4b + 5.7b (8)
with v 0 = 102 mm/s and ω 0 = 5.7 rad/s. The coefficient b is calculated as -1.5 × 10 -4 and the coefficient a is calculated as 0.11.

The magnetic torque for the first experiment is zero, and can be expressed as :

τ 0 = bv 0 + cω 0 ( 9
)
with v 0 = 102 mm/s and ω 0 = 5.7 rad/s. The coefficient c is calculated as 2.7 × 10 -6 . Therefore, the propulsion matrix for SHN1 in 75% glycerol solution is estimated as :

a b b c = 0.11 -1.5 × 10 -4 -1.5 × 10 -4 2.7 × 10 -6 (10)
We remark that all of the propulsion matrix coefficients are expressed in the international system of units. This propulsion matrix of SHN1 in 75% glycerol solution estimated from the measures of dropping with 0 Hz, 1 Hz and 2 Hz external rotating magnetic field was verified by the measures of dropping with 3 Hz and 4 Hz external rotating magnetic field. The nonfluidic forces and torques can now be calculated and showed in Table III rotating magnetic field. According to the calculations, the nonfluidic forces are the gravity forces. Thus, the external rotating magnetic field is considered to give a pure magnetic torque, because there is no relative displacement between magnetic pitch and rotating magnet manipulator. We need thus two measurements to determine the a, b, c parameters.

The same experiments, dropping freely and dropping with an external rotating magnetic field translating with the first pitch of SHN1 in 50% and 90% glycerol solutions, give us the propulsion matrices of SHN1 in 50% and 90% glycerol.

The gravity force of SHN4 is measured as 0.05 N and the buoyancy force due to 75% glycerol solution is calculated as 0.015 N from the measured volume. The nonfluidic applied force is its apparent weight calcuted as 0.035 N. The method to determine the propulsion matrices of SHN4 is the same. The propulsion matrix of SHN4 in 90% glycerol solution cannot be estimated, because the cut-off frequency of SHN4 in 90% glycerol solution is 0 Hz. SHN4 drops very fast in 50% glycerol solution. This fact can enlarge the inaccuracy of parameters estimation. Table IV gathers the a, b and c parameters of propulsion matrices of SHN1 and SHN4 in different viscous liquids.

We observe that the absolute values of a, b, and c parameters increase with viscosity. The a parameters of propulsion matrices of SHN4 are bigger than that of SHN1, and the c parameters are similar in propulsion matrices of SHN1 and of SHN4. These propulsion matrices will allow us to achieve a closed-loop control in the future.

C. Force and torque analyses

Forces and Torques are now simulated by the propulsion matrices estimated in section IV-B. Fig. 6a depicts the required forces and torques for SHN1 in 50% glycerol solution in function of the rotation frequency for different desired translation velocities.

Less force is required for a higher rotation frequency with the same translation velocity, but more force should be applied on SHN1 for a higher translation velocity with the same rotation frequency. In ideal conditions, SHN1 rotating at 1.5 Hz can reach a translation More torque is required for a higher rotation frequency. Theoretically, the required torque for the same rotation frequency does not depend on the translation velocity, as can be seen in Fig. 6a. Naturally, a helix translates screw-likely in a viscous liquid. Therefore a negative torque is required to keep SHN1 translating without rotation.

Fig. 6b describes the required torques and forces for a translating SHN1 at 50 mm/s in different viscous liquid. The figure shows that more torque and force are required for rotating SHN1 in a more viscous liquid, because more torque and force are needed to overcome the rotational fluidic drag torque and fluidic drag force.

The three torque lines cross at about 0.8 Hz and a little higher than 0 Nm. It means that if SHN1 is pulled with a pure force without torque and its translation velocity is 50 mm/s, it rotates at about 0.8 Hz. The three force lines cross 0 N with different rotation frequencies. In different viscous liquids, the resistances are not the same. Different additional rotation frequencies are required to counterbalance the different resistances. Fig. 6c describes the required forces and torques for SHN1 and SHN4 in 75% glycerol solution in function of the rotation frequency for a 50 mm/s translation velocity. More force is required for rotating SHN4 than SHN1. The required torque for SHN4 and for SHN1 is closed, and the required torque for SHN4 is a little higher than that for SHN1. The influence of viscosity on torque is more important than the influence of weight.

D. Translation velocity of SHNs

SHNs can swim forward following the rotating magnet manipulator in translation, depending on the balance of propulsive, magnetic, and friction force. The propulsive force is positive for right-handed chirality rotation, and negative for left-handed chirality rotation. Fig. 7 describes the maximal translation velocity of SHN1 in 75% glycerol solution for different rotation frequencies of the magnetic field. The maximal translation velocity of SHN grows approximatively linearly with the rotation frequency of magnetic field until the cut-off frequency for right-handed chirality rotation, because the propulsive force of SHN1 grows.

SHN1 can also swim in left-handed chirality way, because the magnetic force overcomes the propulsive force and friction. Once the propulsive force increases and the sum of propulsive force and friction becomes bigger than the magnetic force, the SHN1 stops swimming in left-handed chirality way. Thus, the maximal translation velocity of left-handed chirality decreases with the rotation frequency.

V. CONCLUSION

In this paper, we proposed a scaled up experimental system for preliminary analysis of helical nanobelt swimming abilities. This scaled up system includes a SHN, permanent rotating magnet and linear translation to study the control parameters. We identified the propulsion matrices of SHN1 and SHN4 in different viscous liquids. The absolute values of a, b, c parameters from the propulsion matrices increase with viscosity. The a parameters of propulsion matrices of SHN4 are bigger than that of SHN1, and the c parameters are similar in propulsion matrices of SHN1 and of SHN4. Less force is required for a higher rotation frequency to keep a SHN translating at a defined velocity. More force is required for a higher translation velocity. More force is required as well for a SHN in a more viscous liquid with a defined rotation frequency and translation velocity. Both more force and more torque are required for SHN4 than for SHN1 in the same condition with the same rotation frequency and translation velocity. Required torques are independent from the translation velocity. Required torques depend linearly on the rotation frequency and depend strongly on the viscosity of liquid. More torque is required for a higher rotation frequency in a more viscous liquid. The influence of viscosity of the liquid for torques is more important than the influence of the weight. These propulsion matrices allow us to achieve a closed-loop control in the future.

Fig. 1 :

 1 Fig. 1: (a) A global view of system. (b) Target system : scaled-up helical nanobelt encapsulated in a transparent tube filled with liquid.

Fig. 2 :

 2 Fig. 2: Magnetic field magnitudes measured around the magnet. The black cylinder presents the magnet.

Fig. 3 :

 3 Fig. 3: (a) The surface of SHN1's first pitch is covered by magnets. (b) SHN4's entire surface is covered by magnets.

Fig. 4 :

 4 Fig. 4: (a) Magnetic field in yOz plane generated by the magnet at the position 0 • . (b) Magnetic field in yOz plane generated by the magnet at the position 30 • .(c) Magnetic field in xOz plane generated by the magnet at the position 30 • . (d) Two ways to consider the local magnetization of the helical tail : when SHN1 is located in the left-hand side (top) or the right-hand side (bottom) of the magnet. (e) The SHN1 in the left-hand side of magnet rotates in counter clockwise direction seen by its head. (f) The SHN1 in the right-hand side of magnet rotates in clockwise direction seen by its head.

  , because SHNs have to overcome the rotational fluidic drag torque which depends on the viscosity of the liquid. Different cut-off frequencies of SHN1 in different viscous liquids are depicted by Fig 5a, and that of SHN4 is depicted by Fig 5b.

Fig. 5 :

 5 Fig. 5: Experimentally measured cut-off frequencies for (a) SHN1 and (b) SHN4.

Fig. 7 :

 7 Fig. 7: Maximal translation speed for SHN1 in 75% glycerol solution both for right-handed and left-handed chirality.

  . Researchers are not yet ready to fabricate a nanorobot capable of performing even a

	Manuscript received September, 2011. This work was supported
	by Émergence-UPMC-2011

T. XU is with Institut des Systèmes Intelligents et Robotique, UPMC, Paris, France. tiantian.xu@isir.upmc.fr G. Hwang is with Laboratoire de Photonique et de Nanostructures, Marcoussis, France. gilgueng.hwang@lpn.cnrs.fr N. Andreff is with FEMTO-ST Institute, ENSMM, Besancon, France. nicolas.andreff@femto-st.fr S. Régnier is with Institut des Systèmes Intelligents et Robotique, UPMC, Paris, France. stephane.regnier@isir.upmc.fr

TABLE I :

 I Specifications of SHNs used in experiments.

	Fig 3a) has its first pitch (i.e. a 1-full-turn portion of
	the helix) covered by small magnets (Q-05-1.5-01-N
	Supermagnete). The other one named SHN4 (see Fig
	3b) has its entire surface (i.e. the 4 pitches) covered
	by small magnets. The base structures of the SHNs
	nanobelt are made by a prototyping machine. The
	dimensions of SHNs are referred to the dimension of
	the real HNB [11]. The thickness of SHNs are limited by
	prototyping machine'

s capacity. The volumes of SHNs are measured by Archimede method. The dimensions and other specifications of the two types of SHNs used in the experiments are depicted by Table

I

.

TABLE II :

 II Viscosities and Reynolds numbers for different glycerol solutions.

  for dropping with different external

	Type	Viscosity	a	b	c
		50%	0.06 -1.3 × 10 -4	1.8 × 10 -6
	SHN1	75%	0.11 -1.5 × 10 -4	2.7 × 10 -6
		90%	0.73 -8.4 × 10 -4	1.1 × 10 -5
	SHN4	50% 75%	0.09 -1.2 × 10 -4 0.19 -2.9 × 10 -4	1.9 × 10 -6 3.3 × 10 -6

TABLE IV :

 IV Summary of a, b and c parameters of propulsion matrices for the two types of SHNs in different viscous liquids. The fact that SHN1 and SHN4 drop fast in 50% glycerol solution can enlarge the inaccuracy of parameters estimation.

TABLE III :

 III Measurements of dropping velocities and angular speeds of SHN1 and SHN4 in different glycerol solutions in function of the rotation frequency of the applied magnetic field, and the nonfluidic forces and torques calculated from estimated propulsion matrices. Required forces and torques for SHN1 in 50% glycerol solution in function of SHN1's rotation frequency for different desired translation velocities. (b) Required forces and torques for translating SHN1 at 50 mm/s in different viscous liquids. (c) Required forces and torques for SHN1 and SHN4 in 75% glycerol solution in function of SHN1's rotation frequency for a desired translation velocity at 50 mm/s. at 30 mm/s without applied forces, because the pitch of SHN1 is 20 mm. Yet in the experimental conditions, SHN1 had to rotate at around 2 Hz to keep a 30 mm/s translation. This additional rotation frequency is to counterbalance the friction with the tube wall. SHN1 should rotate at 3 Hz -4 Hz instead of 2.5 Hz for a 50 mm/s translation without applied forces, and 6 Hz -7 Hz instead of 5 Hz for 100 mm/s. For 30 mm/s and 50 mm/s translation, the ranges of rotation frequency are confirmed by experiments. SHN1 can not reach the desired 100 mm/s translation velocity in experiments below the cut-off frequency at 7 Hz.

		0.02				2 x 10 -4 2 x 10 -4 2 x 10 -4		0.04				10 x 10 -4 10 x 10 -4 10 x 10 -4		0.02	3 x 10 -4 3 x 10 -4
			100mm/s							90%					SHN4
	nonfluidic force (N)	0 0.01	50mm/s 30mm/s		-1 0 1 -1 0 1 -1 0 1	nonfluidic torque (N.m)	nonfluidic force (N)	0 0.02		75% 50%		0 5 0 5 0 5	nonfluidic torque (N.m)	nonfluidic force (N)	-0.01 0 0.01	SHN1	0 1 2 0 1 2	nonfluidic torque (N.m)
		0 -0.01 0 0 0	2 2 2 2	4 rotation frequency (hz) 6 4 6 4 6 4 6	8 8 8 8	10 10 -2 10 -2 10 -2			0 -0.02 0 0 0	2 2 2 2	4 rotation frequency (hz) 6 4 6 4 6 4 6	8 8 8 8	10 10 -5 10 -5 10 -5			0 -0.02 0 0	2 2 2	4 rotation frequency (hz) 6 4 6 4 6	8 8 8	10 10 -1 10 -1
				(a)							(b)					(c)
	Fig. 6: (a)

ACKNOWLEDGMENT

We acknowledge funding from Émergence-UPMC-2011 research program


L M Aarou

J Eanne V I L

Lan Eau

Parenthetical Classification for Information Extraction

Keywords: Parentheticals, Punctuation, Information Extraction. KEYWORDS IN FRENCH (FR): Parenthétiques, Ponctuation, Extraction d'information

The article focuses on a rather unexplored topic in NLP: parenthetical classification. Parentheticals are defined as any text sequence between parentheses. They have been approached from isolated perspectives, like translation pairs extraction, but a full account of their syntactic and semantic properties is lacking. This article proposes a new comprehensive scheme drawn from corpus-based linguistic studies on French news. This research is part of a project investigating the structural aspects of punctuation signs and their usefulness for Information Extraction. Parenthetical classification is approached as a relation extraction problem split into three correlated subtasks: syntactic and semantic classification and head recognition. Corpus-based studies singled out 11 syntactic and 18 semantic relation subtypes. The article addresses automatic classification, using a combination of CRF and SVM. This baseline system reports 0.674 (head recognition), 0.908 (syntax), 0.734 (semantics), and 0.518 (end-to-end) of F1.

Condensed version in French (FR)

Cet article a pour objectif de contribuer à une meilleure connaissance des propriétés syntaxicosémantiques des parenthétiques, définies comme des empans de texte entre parenthèses. La tâche y est abordée du point de vue de l'Extraction de Relations : (i) extraction des têtes externe et interne des parenthétiques, (ii) classifications syntaxique et sémantique des couples de têtes extraites.

La tête interne d'une parenthétique est son élément informationnel majeur. Sa tête externe est l'élément du contexte auquel l'information entre parenthèses doit préférentiellement être rattachée. Une particularité de ces têtes est de couvrir à peu près toutes les classes grammaticales : texte, phrase, Entités Nommées, noms, verbes, adjectifs, etc. Trois catégories spécifiques ont dû être définies pour les têtes externes : a, p renvoient respectivement aux cas où la tête externe est le texte entier et la phrase dans sa globalité alors que n renvoie au cas où il est impossible de spécifier un rattachement particulier. Dans les exemples de la Table 1, les têtes sont en caractères gras. La Table 2 (en bas à gauche) donne des précisions statistiques sur la nature des têtes dans le corpus étudié.

L'étude d'un corpus de la presse française (Le Monde) a permis d'identifier 11 classes syntaxiques, organisées suivant différents critères (voir détails section 4.2) : parenthétique de nature propositionnelle (inter-clause) ou non (intra-clause), apposition/adjonction (exemples (1, 4, 5, 8)/ (2, 3, 6, 7), présence ou absence de mots introductifs (soulignés dans les exemples), la parenthétique est (ou non) en coordination avec sa tête externe (exemples (3b), (7b)). La Table 1 donne un exemple de chacune des 10 principales classes ainsi définies. le produit intérieur brut (PIB). [the gross domestic product (GDP).] (2) il est (très) réussi. [it is (very) nice.] (3a) son taux directeur (à 2,5%). [its reference rate (at 2.5%).] (3b) elle a connu la liberté (et les pressions). [ 1). Les études de corpus ont permis de mettre à jour 18 classes sémantiques génériques, comme l'ancrage spatial ou temporel (section 4.3).

Des conventions d'annotation ont été élaborées pour permettre l'annotation complète d'un corpus de 1000 parenthétiques. On pourra se reporter aux Tables 2 pour une description de ce corpus en termes de classes et à la Table 4 pour l'accord inter-annotateurs. Bien que, dans la classe des parenthétiques sémantiquement classées, la relation sémantique soit totalement implicite, le bon accord inter-annotateur montre, s'il en était besoin, que le lecteur décode sans difficultés la nature de l'information qui lui est donnée entre parenthèses. As [START_REF] Say | Current approaches to punctuation in computational linguistics[END_REF] point out, punctuation has not attracted much theoretical attention in Linguistics nor in NLP (see however [START_REF] Briscoe | The syntax and semantics of punctuation and its use in interpretation[END_REF][START_REF] Jones | What's The Point? A (Computational) Theory of Punctuation[END_REF][START_REF] Nunberg | The Linguistics of Punctuation[END_REF]). Nevertheless, it is pervasive in written texts and commonly used in NLP systems: phrase boundaries, sentence boundaries, and so on.

This work is a part of a discourse-oriented project investigating how punctuation interacts with different linguistic levels such as syntax and semantics. It attempts to provide answers as to why and how punctuation helps comprehension, through the analysis of text segments between parentheses, named parentheticals. This punctuation structure should not be confused with the definition of parentheticals as optional embedded segments.

The article introduces a new scheme designed for robustness and large coverage from an Information Extraction perspective. The task is divided into three subtasks, Head recognition, Syntactic and Semantic classification. The choice of the classes is based on a linguistic corpus study on French news: 11 syntactic relations and 18 semantic relations have been defined, according to several levels of granularity or dimensions.

The article describes the application and results of an annotation experiment on a sampled corpus of a thousand parenthetical observations. It provides baselines for each subtask, using various feature sets. The results, along with an analysis of feature set impact, call for further experiments as well as for a generalization of the task to different corpora.

Section 3 discusses related work on parentheticals and section 4 introduces the classification scheme. Section 5 details the annotated corpus. The systems are described in section 6 and their evaluation is presented in section 6.3.

Parentheticals in Information Extraction (IE)

It is commonly stated that parentheticals provide optional information: they can be removed without affecting understanding. For instance, they are deleted in sentence compression applications (e.g. equation ( 31) in [START_REF] Clarke | Global inference for sentence compression: An integer linear programming approach[END_REF]). However, they have recently aroused interest in IE.

IE [START_REF] Sundheim | Overview of the third message understanding evaluation and conference[END_REF][START_REF] Sarawagi | Information extraction[END_REF] is the NLP field concerned with (i) the identification of Named Entities (NE) from text, (ii) their co-referring units (anaphora, acronyms), and (iii) their interactions (e.g. Affiliation, Location). Two text types have particularly been studied in IE: newswire and biomedical articles. In both types, parentheticals are pervasive. For instance, Bretonnel Cohen et al. ( 2010), report finding about 17,000 parentheses in a corpus of 97 scientific articles (about 600,000 words). Comparatively, we found more than 4 parentheses per article in newswire texts (136,000 on 17,000,000 words).

Parentheticals have mainly been studied under the topics of Abbreviation, Translation and Transliteration pairs extraction. Abbreviation recognition (extracting co-referring full and short forms) is a well-defined task which has both been conducted on biomedical literature [START_REF] Pustejovsky | Automatic extraction of acronym-meaning pairs from medline databases[END_REF][START_REF] Schwartz | A simple algorithm for identifying abbreviation definitions in biomedical text[END_REF] and on newswire [START_REF] Okazaki | A discriminative approach to japanese abbreviation extraction[END_REF]: systems generally record more than 0.9 in F1. [START_REF] Okazaki | A discriminative approach to japanese abbreviation extraction[END_REF] analyzed 7,887 frequent parenthetical instances and classified them into Acronym, Translated Acronym, Alias and Other.

In their study, the Other category covers 81.9% of all studied instances. The authors propose to split it into alphabetic transcription, location, or affiliation.

Parentheticals have also been studied in the field of Machine Translation. [START_REF] Cao | A system to mine large-scale bilingual dictionaries from monolingual web pages[END_REF] observe that many terms (very frequently NE) are followed by their English translation inside parentheses on Chinese monolingual webpages. They use parentheticals to extract a bilingual dictionary automatically, and find that the majority of pairs are not covered by a standard lexicon. In a similar experiment, [START_REF] Kaji | Splitting noun compounds via monolingual and bilingual paraphrasing: A study on japanese katakana words[END_REF] propose to classify transliteration pairs in order to help segmenting complex katakana compounds.

A recent much larger scheme was proposed for the biomedical domain [START_REF] Bretonnel Cohen | Parenthetically speaking: Classifying the contents of parentheses for text mining[END_REF]. The authors propose to classify parenthetic content into 20 categories. They note that some categories are ambiguous if only the content inside parentheses is taken into account.

The scheme introduced in the next section builds on previous works and aims to be generic and rich at the same time.

Annotation scheme

The annotation scheme is the result of an in-depth corpus-based linguistic study. It proposes to identify, when possible, the most prominent unit inside parentheses (internal head) and the word in the host sentence (external head) to which it is most preferably linked. This link is syntactically characterized. Besides, most of the time, deleting parentheses affects sentence grammaticality (cf. ex. ( 4)), so the relation between parenthetical and its environment needs to be inferred by the reader. In this case (and only in this case), the scheme provides semantic categories.

Head Detection

Internal heads are most straightforwardly detected, because they tend to correspond to the syntactic head of the first information group. When more than one head can be selected, only the first is kept.

External heads are very frequently multi-word units (cf. example (1), Table 1), but is not necessarily the head of its own syntactic phrase. In the following example (9), the relation holds between a color and its interpretation, but it is "niveau" which is the syntactic head of the prepositional phrase.

(9)...maintenir le niveau d'alerte antiterroriste au niveau orange (très élevé) [keeping the antiterrorism threat level at level orange (very high)]

In some rarer cases, the external head may follow (and not precede) the parenthetical, as in ex.

(2), Table 1. More examples can be found in bold type in Table 1.

Three labels are provided when no words can be singled out as head: p for the whole proposition, n for no head, for example in the case of truncation (cf. end of 4.2) and a is used when the parenthetical provides information on the whole document.

Syntactic classification

Ten syntactic categories were organized along four criteria. An example for each of them is given Table 1.

• The first criterion is the distinction between intra(-clause) and inter(-clause). A parenthetical is inter if its content can be viewed as a finite clause (cf. examples ( 5), ( 6), (7a), ( 7b) and ( 8) of Table 1.). In contrast, an intra corresponds to non-finite clauses (cf. ( 1), ( 2), (3a), ( 3b) and ( 4)).

• The second criterion discriminates between adj(-oined) and app(-ositionnal) (nonadjoined) parentheticals. In the case of adj parentheticals, the sentence remains correct when the brackets are removed (cf. ( 2), (3a), (3b), ( 6), ( 7a) and (7b), Table 1.). In app parentheticals, the deletion of the parentheses breaks the progression of the sentence (cf.

(1), ( 4), ( 5) and ( 8)).

• The third criterion divides parentheticals into intro(-duced) (IN) and not-intro(duced) (NI) parentheticals. A parenthetical is intro when an expression introduces its head, and links it with the outer context (cf. (3a), (3b), ( 4), (7a), ( 7b) and ( 8) of Table 1, where introducing elements are underlined).

Eight classes are obtained by applying the previous three criteria. A fourth criterion splits intro adj parentheticals (inter or intra) and discriminates between coord(-inated) and not-coord(inated) parentheticals. In coord parentheticals, the internal head has the same syntactic category as the external head (word or clause) (cf. ( 3b) and ( 7b)).

The last and eleventh class concerns the case of punctuation marks in brackets ( (...), (!), etc.), called truncation. All cases have been found in corpus, though with high distribution differences (Table 2, left).

Semantic classification

Eighteen semantic categories, organized into four dimensions, were defined for intra app NI (intra-clause appositional not-introduced) parentheticals, which lack an explicit link. Classifying other syntactic classes was left for further investigation.

Corpus Annotation

The scheme was tested on a sample of French news (114 parentheticals) by two highly-trained annotators. The results of inter-annotator agreement for the three tasks are illustrated in Table 4. Kappa indexes show that parenthetical syntactic (0.89) and semantic (0.79) categories could easily be recognized by annotators. The Kappa was not computed for Head recognition since head spans vary greatly. It is thus hard to approximate the random baseline on which the Kappa is based [START_REF] Grouin | Proposal for an extension of traditional named entities: From guidelines to evaluation, an overview[END_REF]. As can be seen in Table 2 (left), the intra app NI class is the most frequent syntactic class. This validates the use of a semantic scheme designed especially for this class (other syntactic classes being semantically classified as NULL). Heads are mostly words, though the "p" class covers 6% of examples.

The counts of semantic classes (Table 2, right) shows that the semantic class Other, used for the examples of intra app NI parentheticals which don't match with the defined semantic categories covers less than 5% of examples.

At last, the annotated corpus was sampled (stratified sampling) according to the concatenated labels to build the training and testing corpora (half each).

Head Label pre-detection

Pre-detection is a straightforward task: most corpus instances are annotated with one label (ID), which results in a high baseline of 0.888, just by assigning this label to all examples. The SVM beats this baseline with 0.963 of overall F1. Detailed feature analysis shows that the Ab and Size features do not individually help for this task since the resulting models behave like the baseline. The best feature set is F (forms): the SVM perfectly classifies a and n classes (1 in F-measure). This is due to the fact that the a class corresponds to a parenthetical which only contains punctuation signs such as "...". The n class instances generally occur at the end of an article and are immediately preceded by dashes. The real challenge is therefore to discriminate the p class (0.667) from the ID class (0.981).

Head recognition

Only external heads were evaluated for this task. The baseline selects the word immediately preceding the parenthetical as the head, because most heads occupy this position. An example can be considered correctly labeled if (i) all the labeled words need to be correct (exact evaluation), or if (ii) at least one word needs to be correctly labeled (soft evaluation). The baseline F1 is very low (0.3) in the first case, and reaches 0.649 in the second case. The best results (0.674) recorded for the CRF were obtained with a window size of 4 words [-1,2]. The best feature set is C (0.499), i.e. the categories provided by the linguistic analyzer, including Named Entities. These results are still much lower than the system using the combination of all feature sets (0.674 in F1 for exact matching; 0.774 for soft matching). The latter takes benefit of the pre-detection features (best feature set for p and n classes) but also largely improves exact head recognition (+0.146 compared to C).

The high difference between Soft and Exact head recognition across feature sets indicates that multi-word units management play a large part in system performances.

Classification

The Syntax and semantic tasks were first carried out independently. The Syntax task consists of 7 labels (4 rare inter categories are missing) and the semantic task, of 19 classes (Indexing is missing). The baseline model assigns the most frequent class to all examples (0.818 in F1 for syntax, 0.182 for semantics). Table 3 shows the superiority of the T set for syntax.

T is composed of precise syntactic labels; for instance, it discriminates between various verb forms such as past and present participles (contrary to the C set which only divides between auxiliaries, modals, actions and gerunds).

Concerning Semantics, it is the C feature set which is the most effective. This said, the system reaches higher scores when all the features are taken together. It is also clear from the table that POS tags (T) have a greater impact than forms (F) on this task.

A

End-to-end Evaluation

The aim of the end-to-end evaluation is to observe how Head recognition affects both syntactic and semantic classification. An example was considered correct when all task labels were correctly assigned. F1 significantly drops drops to 0.518 on exact matching, and to 0.586 on soft matching. These results are consistent with previous work in RE. [START_REF] Zhou | Exploring various knowledge in relation extraction[END_REF] report 0.55 of F1 when recognition and classification are evaluated together on subtypes (0.68 on supertypes), and attribute 73% of errors to recognition (53% in our case).

It is interesting that the Situational dimension, which contains traditional RE broad categories (SA and Affiliation), obtains the best scores. These scores are even higher than reported in RE literature [START_REF] Sun | Semi-supervised relation extraction with largescale word clustering[END_REF], though the dataset is barely comparable. Abbreviation experiences comparably lower results than reported in the literature: [START_REF] Okazaki | A discriminative approach to japanese abbreviation extraction[END_REF] report 95.7% accuracy (0.887 of F1).

Conclusion and discussion

Parenthetical classification is a rather unexplored topic and this article aims at providing insights into this punctuation pattern. An annotation scheme was designed to cover most frequent cases for three tasks: syntactic and semantic classification and head recognition.

Corpus analyses revealed that most parentheticals lack an explicit link to the external context (the App syntactic class), but are nonetheless similarly understood by annotators. Only the Intra App NI class was semantically labeled (81% of instances) and tested. Analyzing inter app parentheticals was left for further investigation because it is believed that they must be studied on the discourse level (see for example [START_REF] Marcu | The rhetorical parsing of unrestricted texts: a surface-based approach[END_REF]): proposition links may be characterized as causal for instance.

Other annotation experiments have been started on different text types (encyclopedic, legal, scientific or fictional documents), to assess the robustness of the scheme across text types, and evaluate automatic systems in the light of domain adaptation. Preliminary results are encouraging in the sense that the same scheme can be used with little adaptation.

The evaluation proposed a baseline using CRF and SVM for each task separately, with various feature sets based on POS tags, Named Entities, Forms, etc. The best model reported 0.908 for syntax, 0.734 for semantics, and 0.674 for head recognition. It is interesting that different feature sets have had different impacts on classification tasks. All tasks except semantics have shown better performance on isolated feature sets. Besides, [START_REF] Zhou | Exploring various knowledge in relation extraction[END_REF] have shown that chunking improves performances ACE Relation Extraction. Following evaluations should investigate the benefits of feature sets like chunking and semantic lexicons (as hyperonym lexicon for Type and Instanciation categories).

Since classification tasks such as syntax or semantics reported better results, it would also be interesting to investigate what gain results from their use as feature sets, much like what was done for pre-detection. Overall, it seems that improving recognition performances would rely on careful feature construction.

As suggested in section 6.3.4, the results obtained for Affiliation and SA are higher than usually reported on standard RE. This could simply be due to the fact that parenthetical structures impose strong constraints which facilitate classification. If these results are confirmed in subsequent evaluations, it would mean that parentheticals could be used as a small window to extract valuable seeds for general RE.

1.

  The first, Co-reference (CoRef), corresponds to cases where both heads refer to the same entity, but use different names. (a) Abbreviation: the parenthetical contains an abbreviation of the external head (its full form; cf. example (1)). (b) Explanation: the definition of an acronym (the reverse of the previous relation). (c) Translation: it contains a translation of the external head in an other language. (d) Reformulated Entity (RefEnt): other co-referential relations not covered by the previous classes; for example, the name an actor has in a movie. (e) Reformulated Value (RefVal): it translates the value expressed by the external head in another unit of measurement. 2. The second broad class, Categorization (Cat), refers to asymmetric relations between entities and categories. (a) Type: it provides the category of the entity of the external head (as hyponyms). (b) Instantiation: the reverse of the previous relation. It provides an instance of the category expressed by the external head. (c) Value Precision (ValPrec): it precises the value of its external head, which is already a quantity category (drop, growth, etc.). 3. The third class relates to Situational relations (Sit). Most correspond to standard semantic relations defined for relation extraction (ACE, 2008). (a) Product Source (PS): it refers to the producer, editor, etc. of a product referred by the external head (e.g. book). (b) Affiliation: it contains the organization to which its external head (person or organization) is affiliated. (c) Spatial Anchoring (SA): it sets the spatial location of an entity. (d) Temporal Anchoring (TA) is split into Date and Period (of any kind of entity). (e) Argument Value (ArgVal): it gives a value related to its external head (as age). 4. The fourth class concerns Referencing (Ref), where parentheticals attribute references or indexes to the external head. (a) Inter-textual Reference (IR): it makes a reference to the journal, media as source of the external head (citation). (b) Para-textual Reference (PR): it refers to para-textual elements of the document (figure, footnote, etc.) (c) Coordinates: It provides the code value indexing entities in a given coding scheme (phone number, postal address, etc.). (d) Indexing: it refers to the marks (numbers) indexing document elements (such as examples) and to which parentheticals may elsewhere refer to. Contrary to Okazaki et al. (2008), translated acronyms are here considered as abbreviations. In principle, most classes defined by Bretonnel Cohen et al. (2011) could be fitted in this scheme, like p-values (ArgVal) or Figure references (IR).

Table 1 :

 1 Exemples pour la classification syntaxique. (Examples for syntactic Classification.)

	La classification sémantique ne traite que d'une classe syntaxique particulièrement fréquente
	(82%) : les parenthétiques appositives non introduites et intra-propositionnelles (exemple
	(1) Table

Table 2 :

 2 Fréquences des classes dans le corpus. (Sample corpus class counts.)

	Syntactic Class	Frequency	Semantic Class	Frequency
	Intra App NI		801	NULL		177
	Intra Adj IN Not-Coord	60	CoRef-Abbreviation	150
	Inter App NI		27	Sit-SA		87
	Truncation		25	Cat-Instantiation	78
	Intra Adj NI		21	Sit-ArgVal		72
	Inter Adj IN NotCoord	22	Sit-Affiliation		72
	Intra Adj IN Coord	21	Ref-IR		55
	Inter Adj NI		1	CoRef-EntRef		49
	Total		978	Other		43
				Sit-PS		43
				Cat-ValPrec		28
				CoRef-ValRef		27
				Cat-Type		25
				CoRef-Translation	22
	Head Class	Frequency	Sit-TA-Date		21
	ID		869	Ref-PR		9
	p		62	CoRef-Explanation	9
	n		25	Sit-TA-Period		7
	a		22	Ref-Coordinates	4
	Total		978	Total		978
	Le système proposé comme baseline (Section 6) combine les CRF (pour la détection des candi-
	dats) et les SVM (pour la classification), pour chaque tâche indépendamment et toutes tâches
	confondues. L'évaluation de ce système (Table 3) a permis tout d'abord d'observer que les en-
	sembles de variables (formes, étiquettes morpho-syntaxiques, Entités Nommées, etc.) avaient
	un impact qui variait en fonction de la tâche : les étiquettes morpho-syntaxiques (T) sont par
	exemple les plus utiles à la classification syntaxique. De plus, la détection des candidats est une
	tâche cruciale, étant donné que le nombre de couples candidats aux frontières correctement
	délimitées est responsable d'une chute de la F-mesure globale du système (0,674, indépen-
	damment, 0,518 toutes tâches confondues). Ces résultats sont confirmés par ceux obtenus par
	(Zhou et al., 2005) en Extraction de Relation à grand nombre de classes.
	Feature	Pre-detection	Exact-Rec.	Soft-Rec	Syntax	Semantics
	F	0.965	0.426	0.680	0.861	0.512
	C	0.914	0.499	0.705	0.859	0.637
	T	0.955	0.470	0.714	0.908	0.582
	Ab	0.888	0.318	0.642	0.818	0.312
	Pre-detection	-	0.349	0.719	-	-
	Size	0.886	-	-	0.796	0.286
	All	0.963	0.674	0.774	0.902	0.716
	Baseline	0.888	0.3	0.649	0.818	0.182
	Table 3: Résultats obtenus par le système en fonction des ensembles de variables utilisés.
	(Independent task results on each feature set.)
	Les expériences à venir feront intervenir les informations syntaxiques pour évaluer leur apport.
	La robustesse du schéma d'annotation nécessite d'être également mise à l'épreuve; les expéri-
	ences préliminaires menées en ce sens sur des corpus encyclopédiques, littéraires, juridiques
	et scientifiques n'ont jusqu'à présent donné lieu qu'à des modifications minimes.

Table 4 :

 4 Inter-annotator agreement synthesis.

	Task	# agr.	# disagr.	Total	Kappa
	Syntax	109	5	114	0.89
	Semantics	88	13	101	0.79
	Head	103	11	114	/

Définies dans cet article comme du texte entre parenthèses, les parenthétiques ont été jusqu'à présent peu étudiées en TALN. Si elles ont fait l'objet d'études particulières telles que l'extraction de paires de traduction, il manque une approche globale des relations syntaxiques et sémantiques qui les rattachent à leur contexte. Cet article propose un nouveau schéma de classification élaboré à partir d'études de corpus de presse. Cette recherche s'inscrit dans un projet explorant les aspects structurants des signes de ponctuation et leur utilité en Extraction d'Information. La classification des parenthétiques est abordée sous l'angle de l'extraction de relations et divisée en trois sous-tâches : classification syntaxique et sémantique et reconnaissances des têtes. Les études de corpus ont fait émerger 11 classes syntaxiques et 18 classes sémantiques. L'article propose d'évaluer un système combinant CRF et SVM. La baseline obtenue est de 0,674 (reconnaissance des têtes), 0,908 (syntaxe), 0,734 (sémantique) et 0,518 (toutes tâches confondues) de F-mesure.

Different algorithms were tested to confirm this.

6 System design and Evaluation

Overview

Relation Extraction (RE) systems typically (i) extract Named Entity (NE) pairs to filter positive targeted instances (recognition step), before (ii) they attribute a label to them (classification step). The recognition step is problematic since it requires that all possible NE instances be extracted: [START_REF] Sun | Semi-supervised relation extraction with largescale word clustering[END_REF] indicate that the number of negative instances is about 8 times higher than the number of positive ones. The current best classification systems on complex schemes rely on feature-based approaches [START_REF] Zhou | Exploring various knowledge in relation extraction[END_REF]. Such methods typically use information on candidate NE pairs (such as NE tag, POS tag, form, etc.), along with information on the words in between [START_REF] Zhou | Exploring various knowledge in relation extraction[END_REF] for prediction.

In our case, candidate pairs (heads) do not correspond uniquely to NE, but also to whole sentences, quotations, verbs, adjectives, etc.: the number of candidate pairs is huge. This is why, instead of elaborating a preprocessing system, the recognition step was approached as a sequence labeling task (6.2).

What is more, annotators had the choice between using labels and select word spans to identify parenthetical heads. Therefore, the system first discriminates labeled instances (a, p, and n) from others (ID class). In a second step, it detects head boundaries from previously predetected ID instances.

This first step (pre-detection), along with syntactic and semantic classification, is approached as a classification task performed on each parenthetical instance.

System

Two systems were used : CRF++ [START_REF] Kudo | Crf++: Yet another crf toolkit[END_REF] for head recognition and SVM [START_REF] Hall | The weka data mining software: An update[END_REF] for parenthetical classification as they are recognized as very efficient algorithms 1 . The features used for CRF Recognition include:

• forms (F) without any processing.

• categories (C) provided by a linguistic analyzer, which includes NE recognition and semantic labels [START_REF] Rosset | Integrating spoken dialog and question answering: the ritel project[END_REF]. This tagset was transformed into BIO format [START_REF] Kim | Introduction to the conll-2003 shared task: Language-independent named entity recognition[END_REF].

• POS tags (T) provided by the Tree-tagger [START_REF] Schmid | Probabilistic part-of-speech tagging using decision trees[END_REF].

• Abbreviation pairs (Ab) from the system provided by [START_REF] Schwartz | A simple algorithm for identifying abbreviation definitions in biomedical text[END_REF].

• pre-detection labels (a, p, n, ID) propagated on all the words, Unigrams, bigrams, and label bigrams [START_REF] Kudo | Crf++: Yet another crf toolkit[END_REF] occurring in the most optimal window size (cf. 6.3.2) were used for all feature sets.

The same features were used for classification, except removing predetection labels and adding parenthetical size (Size). For the other sets (F, C, and T), each feature value was combined with positional parameters to distinguish between the first and second words before and after the opening brace.

Evaluation

Evaluation was performed on the test corpus (490 instances) using the standard metrics of precision, recall and F1 (F-measure). All results are displayed in Table 3.


Maarouf El

Villaneau Ismaïl
email: jeanne.villaneau@univ.ubs.fr

Jeanne

A French Fairy Tale Corpus syntactically and semantically annotated

Keywords: Syntactic Annotation, Semantic annotation, Fairy Tales

Fairy tales, folktales and more generally children stories have lately attracted the Natural Language Processing (NLP) community. As such, very few corpora exist and linguistic resources are lacking. The work presented in this paper aims at filling this gap by presenting a syntactically and semantically annotated corpus. It focuses on the linguistic analysis of a Fairy Tales Corpus, and provides the description of the syntactic and semantic resources developed for Information Extraction. Resources include syntactic dependency relation annotation for 120 verbs; referential annotation, which is concerned with annotating each anaphoric occurrence and Proper Name with the most specific noun in the text; ontology matching for a substantial part of the nouns in the corpus; semantic role labelling for 41 verbs using the FrameNet database. The article also sums up previous analyses of this corpus and indicates possible uses of this corpus for the NLP community.

Introduction

Fairy tales, folktales and more generally children stories have lately attracted the Natural Language Processing (NLP) community. For example, the LREC 2010 conference 1 welcomed at least three papers on such corpora; Fairy Tales markup Language have been defined to tag text sequences according to Propp's theory (see (Scheidel and Declerck, 2010) and references therein) and NLP research projects have recently been launched (e.g. FACT 2 ). Considered applications include Text classification, referring to work in Literature such as (Propp, 1968) or (Aarne and Thompson, 1973) and robot story tellers (see (Gelin et al., 2010;Theune et al., 2003) for instance), with a focus on expressive reading (Volkova et al., 2010). Such applications may benefit from Information Extraction (IE), a NLP task aimed at extracting entities, semantic and coreference relations from text. Children stories is a new domain of application for IE (mainly focused on newspapers and medical corpora; (Nadeau and Sekine, 2007)) and may reveal different or specific problems for NLP systems. One interesting issue concerns the specificity of their content: children stories are not always set in a "real" environment (such as one would expect in newspapers corpora), but feature magical beings as well as extraordinary events (motifs). This paper focuses on the linguistic analysis of a Fairy Tales Corpus, and provides description for the syntactic and semantic resources developed for IE in terms of classification and relation extraction. To our knowledge, NLP ontologies such as Cyc 3 or Wordnet 4 do not provide a detailed classification (or "microtheories") for fictional entities or events. The work pre-1 LREC 2010 (www.lrec-conf.org/lrec2010/); see also AMI-CUS (ilk.uvt.nl/amicus/) and the 2010 Symposium on Computational Models of Narratives (narrative.csail.mit.edu/fs10/) 2 wwwhome.cs.utwente.nl/˜hiemstra/2011/folktales-asclassifiable-texts.html 3 www.cyc.com 4 wordnet.princeton.edu sented in this paper aims at filling this gap by presenting a syntactically and semantically annotated corpus. Section 2 introduces the project and describes the corpus. Sections 3 and 4 introduce the syntactic and semantic annotations.

The Fairy Tales Corpus

Research project

The Fairy Tales Corpus (FTC) was originally collected in a project of the French National Research Agency, EmotiRob (Saint-Aimé et al., 2007). The goal of this project was to design an interactive companion robot for fragile children. The part of the project which is concerned here involves detecting emotion (happiness, sadness, etc.) through linguistic analysis (ASR transcripts): this is the task of EmoLogus (Le Tallec et al., 2010), a symbolic system which computes emotions on top of a semantic representation. EmoLogus requires semantic knowledge (concepts and relations) to generate a semantic representation for each speech act. An emotion lexicon was created to cover words which could be used in children interactions, based on previous lexica and experiments in schools (see (Le Tallec et al., 2010) for further details). A corpus was then needed to extract sufficient context (linguistic) information for these words. The FTC was chosen as the best alternative since it is directed towards children and because it contained a large proportion of the targeted words (76% of nouns and 90% of verbs; (El Maarouf et al., 2009a)).

Objectives

The purpose of the annotation was to prepare and extract semantic information needed by EmoLogus. This involved the creation of a verb database where each meaning is connected to contextual patterns (see (Hanks, 2008) for a similar perspective). In order to preserve the specificity of the research context and of the corpus, the annotation was carried out in a corpus-based fashion (Sinclair, 1991): verbs were analysed one by one through their concordances and patterns were ranked according to their frequency. This methodology allows to identify corpus-based semantic patterns for verbs, i.e. patterns as they appear in corpus. However, the task of syntactic relation extraction was led separately from pattern analysis in order to analyse what was involved in the creation of patterns: patterns were only merged in a second step. The most frequent full verbs (120 verbs of frequency>30) were selected for manual annotation (a sample is shown in . The corpus also shows variety in terms of content: some stories involve the 'ordinary' (non-magical) everyday life of children while others focus on animal protagonists, fairies, witches or even aliens. In conclusion, the FTC is heterogeneous in terms of authorship and content, but its single audience (children) provides for a dimension of homogeneity.

Syntactic Annotation

The corpus was first automatically tokenized, lemmatized and Part-Of-Speech-tagged with the Tree-Tagger (Schmid, 1994). Tag errors were corrected as the annotation progressed.

Annotation scheme

All the words (headwords) syntactically linked to each verb were extracted and labelled using a set of categories. Each relation consists in a triple <R,V,A >where R stands for the name of the relation and V and A stand for the verb and its argument respectively. Full syntactic annotation was performed: both arguments and adjuncts were annotated and non-finite verb forms were included. (2) Biribi s'avance un peu plus. trans. Biribi approaches a little more.

French makes great use of pronouns (Blanche- Benveniste, 1990), and the same pronoun may function differently according to context: in (3), the word "en" (which may be loosely translated as a contraction of "of it") plays the role of a location source whereas it functions as the syntactic object in (4).

(3) Les bisons ne peuvent presque plus en sortir. trans. buffaloes could hardly get out of it.

(4) On en prenait des quantités raisonnables. trans. We took reasonable quantities of it.

Other conventions include:

• Phrasal verbs with specific meaning are given a separate index: pronominal verbs like "se pousser"

(5), causatives like "faire remarquer" (6) and combinations like "se faire remarquer" (7).

(5) "Pousse-toi, triple idiot !" dit Jennyfer. trans. "move over, you prize idiot !" said Jennyfer.

(6) Mais il m'a fait remarquer que l'on pourrait toujours le faire plus tard. trans. But he pointed out to me that it could be done later.

(7) Il faut toujours que tu te fasses remarquer. trans. You always behave conspicuously.

• As in PropBank, quotatives (not headwords) such as "pousse toi, triple idiot !" illustrated in (5) were annotated.

• In ambiguous contexts, the most informative head rather than the syntactic head (first governing noun) was chosen as the argument. This often occurs with collective nouns (8).

(8) Il prit un morceau de bois. trans. He took a piece of wood.

• In special cases where verbs lack an overt subject (e.g. in imperative mood), the argument of the relation is the verb itself ( 9).

(9) Prends le couloir et la première porte à droite. trans. Take the corridor and the first door to your right.

• Every headword of coordinated groups should be selected on an equal basis (duplication of syntactic relations). In (10), the Objects are both "ballon" and "peluche".

(10) Il prit le ballon et par la même occasion la peluche. trans. He took the ball and the toy at the same time.

• Since non-finite forms are included, the same argument may be linked to more than one verbs (as in control and raising verbs), coordinated forms included. In (11), the pronoun "J"' (I) is annotated as the subject of all the verbs ("aimer", "aller", "se coucher", "déranger").

(11)-J'aime autant aller me coucher que de déranger les gens. I prefer going to sleep than disturbing people.

The relation tagset covers 219 relations when taking prepositions into account (94 hapaxs) and 32 labels excluding prepositions (5 hapaxs). A sample is provided in Table 3.

Syntactic Patterns

Syntactic patterns can be restored by adding up all syntactic relations for each verb occurrence. To show the kind of patterns obtained from the annotation, we chose the verb "répondre" (to answer). This verb occurs 165 times in the FTC for only one index (not a phrasal verb) and 22 syntactic patterns were collected. As can be seen, two syntactic relations play a predominant role in the FTC: Subject (SUJ) and Direct Speech (cf. Table 3). Example 12 illustrates both syntactic relations. ( 12) -D'accord, merci beaucoup, répondit Jérémie. trans.

-Ok, thanks a lot, Jérémie answered. Two other facts must be pointed:

• The high number of those "surface" patterns can be explained by the absence of a syntactic relation. For instance, the absence/presence of IOBJ (the addressee as pronoun) is responsible for the split of pattern 1 and 2. Since an answer is in most examples addressed to someone, the addressee is eluded because the reference can be inferred from context. Ellipsis does not necessarily influence verb meaning.

• Different syntactic relations may happen to split identical functions. For example the syntactic relation realized by the preposition "à" (to) often designates the addressee (IOBJ).

The conclusion which can be drawn from this verb (other experiments have confirmed this point) is that this syntactic scheme tends to create (unnecessary) scattering.

Semantic Annotation

The semantic annotation performed on the FTC answered two different needs:

• Limiting syntactic scattering by merging similar syntactic relations: this is the task of semantic role annotation and will be discussed in subsection 4.3.

• Restraining syntactic relations by introducing semantic features or categories (e.g. selectional restrictions), which is the task presented in the next subsection. it could be proposed that only members of the Human category may be thinkers, or that only liquids can be drunk. This is only part of the story: however strong these relations are in the real world, their application to texts and words is problematic. Words regularly refer different things and shift meaning. Pustejovsky has, among others, proposed to account for regular sense alternations, called regular polysemy or logical metonymy (Pustejovsky, 1998). The model, named the Generative Lexicon, helps to tackle cases such as container/content alternations (as in drink tea/a cup) and has recently been confronted to corpus data (Pustejovsky and Ježek, 2008). To do so, the possible set of categories that a verb argument belongs to (that "tea" is a liquid or that "cup" is a kind of container) needs to be known: identifying strong semantic relations is a first step, resolving metonymy is the next.

Referential Annotation

If a semantic category can be easily picked from nouns in the context of a sentence, pronouns, on the contrary, do not convey semantic information other than gender or number: they refer. In the FTC for example, pronouns account for more than a quarter of the subjects and of the indirect objects of the verb dire (to say). In a corpus-based framework, before identifying relevant semantic relations, anaphoric references should either be resolved or explicitly discarded. A similar reasoning could be applied to proper names: our analysis of the name "Christophe" in the FTC revealed that more than a third of its 18 occurrences referred to an animal. For the FTC, it was decided that each anaphoric reference should be annotated with a semantic category. It is worth mentioning that reference resolution is generally approached as co-reference resolution (Orȃsan et al., 2008): linking co-referential entities, that is by assigning the same identifier to various linguistic expressions (13).

(13)<entity id='1'>Harold</entity> came back. <entity id='1'>He</entity> had forgotten his hat. This was not our aim, since it does not provide for a semantic characterization of the reference. Pronouns, in their variety of forms (demonstrative, possessive, relative and personal) as well Proper Names were annotated with a word corresponding to the most specific category expressed in the text (called a referential category). For instance, if, in the text, a referent is introduced and is afterwards refered to with a pronoun, the most specific linguistic description was used to annotate the pronoun. Example 13 would turn out as Example 14. ( 14)<entity class='prince'>Harold</entity> came back. <entity class='prince'>He</entity> forgot his hat.

When a word refers to more than one entity, all the possible categories are included in the annotation (e.g. plural pronoun "les" in Example 15). ( 15) <entity class='éléphant'>Tu</entity> es le seul qui sois assez costaud pour <entity class='garc ¸on;fille'>les</entity> porter. trans. You are the only one strong enough to carry them.

Since referential categories are nouns, the main benefit of this scheme is to provide a common and comparable basis for pronoun verb arguments, Proper Name arguments and (regular) noun arguments at the semantic level. All in all, 24668 anaphoric occurrences have been labelled according to these conventions. The most frequent observed categories are shown in Table 5

Ontological categories

Referential categories were then classed into ontological categories (Human, Animal, Imaginary, etc.) and could belong to only one of them. For example, the prince in ( 14) is human because princes only belong to this category. When animal princes were found, the Animal category was kept. These ontological categories do not cover all the words in the FTC but only those arguments syntactically linked with previously selected verbs (cf. section 3). A sample is provided in Table 6.

Ontological Category

Freq 40 general ontological categories were selected (corresponding to a total of 17151 verb argument occurrences) from the Brandeis Linguistic Ontology (Hanks, 2008). They were tested as selectional restrictions by combining them with syntactic relations (El Maarouf, 2009). For example the subject relation of the verb "dire" (to say combines with Humans (60%), but also Animals (15%) and Imaginary creatures (13%). This behaviour was found to be common with other speech verbs and cognitive verbs (to know, to think, etc.), hence, pointing out the fact that:

• Selectional restrictions were only partially useful in the FTC corpus.

• Ontological categories could be merged for the FTC corpus.

• Ontological categories could also entail scattering in the FTC corpus.

Whether these alternations (some of which could be interpreted as personifications) are cases of regular polysemy or genre-specific regular polysemy is open to discussion.

Semantic Role Annotation

In order to reduce syntactic and semantic scattering, work has also been initiated to identify semantic roles for the most frequent verbs. Semantic roles refers to functional categories regardless of their syntactic realization in the clause and each verb sense is associated with a small set of roles. The FrameNet database 5 (Baker et al., 1998;Fillmore, 1982) was used to annotate roles (or frame elements) and 5 framenet.icsi.berkeley.edu predicates (or frames). For this task, syntactic patterns were mapped onto predicate-role tuples, in order to reveal the discrepancies between both levels. For example, the verb "appeler" (to call) in its non-pronominal form occurs in 17 patterns whereas it only concerns two frames, namely CONTACTING (establishing a communication) and BEING-NAMED (giving a name to an entity). Sometimes the mapping between semantic roles and syntactic relations is straightforward: in the patterns SUJ-OBJ ( 16) and SUJ-DirectSpeech (17), SUJ, OBJ and DirectSpeech always stands for the Communicator, the Addressee and the Communication, respectively.

( As discussed earlier, different syntactic relations may perform the same function, and thus be labelled with the same semantic role. Currently, 63 Frames have been defined for 41 verbs, either directly linked to FrameNet data, or inspired from this resource when a given meaning pattern did not exist.

Conclusions and Perspectives

This paper describes a French corpus of fairy tales and the schemes used for syntactic and semantic annotation. Syntactic dependency annotation focused on 120 verbs and semantic role labelling on 41 verbs. The corpus is also referentially annotated: each anaphoric occurrence is linked to a referential category. Since only one annotator took part in the annotation process, some decisions may be subject to discussions. In order to provide a sounder ground for the annotations, another round of annotation could be forecast and the agreement rate tested.

The Fairy Tales Corpus and its detailed syntactic and semantic annotation has been used in work on the interface between discourse and semantics (El Maarouf, 2009). (El Maarouf et al., 2009b) have also compared it to a press corpus to analyse the distribution of semantic categories according to text genre. Perspectives on using this corpus by NLP applications include evaluating systems on syntactic relations (Dependency parsing), semantic classification (Information Extraction) and semantic frames (Semantic Role Labelling). With the growing interest on fairy tales, this corpus may become an asset for the NLP community and facilitate research on high level semantic analysis. The resources will be freely distributed by early 2012.

A. Aarne and S.

Table 1

 1

					frequency, especially when Classic storytellers are taken
					into account (like the Little Red Ridding Hood). Chil-
					dren tend to write shorter stories, if we consider story mean
					length (758, as opposed to 2634 words per story for modern
					adults)
			). Only one annotator
	(linguist) took part in the annotation, therefore it was not
	possible to test the agreement rate.
	Verbs	Freq. trans.
	dire	813	to say
	trouver	299	to find
	savoir	292	to know
	venir	288	to come
	prendre	275	to take
	passer	254	to pass
	demander	244	to ask
	appeler	225	to call
	partir	224	to leave
	donner	199	to give
	regarder	197	to look
	entendre	181	to hear
	sortir	169	to go out
	répondre	165	to answer
	manger	159	to eat
	rester	149	to stay
	décider	141	to decide
	chercher	141	to look for
	devenir	129	to become
	penser	123	to think
	commencer 121	to begin
	tomber	121	to fall
	Table 1: Annotated verbs wrt frequency
	2.3. Corpus description
	The FTC is a collection of tales extracted from a website
	and the text is copyrighted. 139 tales were manually col-
	lected, cleaned and checked. The corpus contains about
	160 000 running words (the number of words per tale vary
	from 120 to 17000 words). As the website provided in-
	formation regarding authorship (mainly age and place) for
	most of the tales, this information was conserved and used
	to classify the corpus (see Table 2).
	Author	Freq.	%	Tales %
	Modern Adult 63217 39% 24	17%
	Children	53109 34% 70	51%
	Unknown	34314 21% 37	27%
	Classic Adult 9900	6%	7	5%
	Table 2: Author Categories wrt frequency and nb of Tales
	The proportion of adult writing (either professional writ-
	ers or not) is slightly greater than the proportion of chil-
	dren writing (e.g. in classroom activities) in terms of total

Table 4 :

 4 Table 4 shows non-hapax patterns which account for nearly 90% of the data. Patterns of the verb "répondre".

	Syntactic Patterns	Freq. Prop.
	SUJ-DirectSpeech	83	50%
	SUJ-IOBJ-DirectSpeech 23	14%
	SUJ-IOBJ	9	5%
	SUJ	9	5%
	SUJ-à	7	4%
	SUJ-OBJ	6	4%
	SUJ-IOBJ-ThatClause	6	4%
	SUJ-MAN-DirectSpeech 4	2%
	SUJ-en-DirectSpeech	3	2%
	SUJ-avec-DirectSpeech	2	1%
	SUJ-à-DirectSpeech	2	1%
	...

Table 3 :

 3 Syntactic relations wrt frequency; verb between square brackets and argument between curly brackets.

	RELATION	FREQ.	EXAMPLES
	SUJ	12110	Oui, {je} [sais].	Yes, I know.
	OBJ	4668	{Fouad} s'est fait [arrêter].	Fouad has been arrested.
	Direct Speech 1030(3) {"Pousse-toi, triple idiot !"} [dit] Jennyfer.	"move over, you prize idiot !" said Jennyfer.
	IOBJ	855	je ne {vous} le [dirai] pas.	I won't tell it to you.
	INF	686	Les enfants, [courez] faire {sonner} le tocsin.	Children, run and ring the bell.
	That-clause	593	On [raconte] {que} les festivités durèrent trois mois. It is told that the party lasted 3 months.
	...

A common claim in linguistics is that there exists a strong relationship between verb meaning (predicate) and the semantic categories or types of its arguments. For example,

Table 5 :

 5 . Referential Category wrt frequency.

	Referential Category Freq. trans.
	homme	3907 man
	enfant	3451 child
	fille	2161 girl
	femme	1546 woman
	garc ¸on	734	boy
	lutin	521	goblin
	animal	448	animal
	prince	429	prince
	chat	421	cat
	bûcheron	412	lumberjack
	princesse	411	princess
	cheval	354	horse
	tigre	335	tiger
	souris	282	mouse
	roi	282	king
	extra-terrestre	280	alien
	fée	274	fairy
	sorcière	270	witch
	hérisson	238	hedgehog
	...

Table 6 :

 6 Ontological Category wrt frequency.

		.
	HUMAN	7723
	ANIMAL	2926
	IMAGINARY-CREATURE 1457
	PLACE	960
	OBJECT	901
	VEGETAL	376
	BODY-PART	353
	FOOD	224
	EVENT	221
	INFORMATION	192
	TIME-PERIOD	180
	...

  16) Si seulement je pouvais appeler cet oiseau. trans. If only I could call this bird. (17)"C ¸a y est Kléber! Je suis prêt", appela son père. trans. "All right Kléber! I am ready", his father called.

	However, the same syntactic relation may insome cases
	correspond to more than one semantic role. For example,
	Prepositional phrases introduced by "de" may either be
	Depictive (18) or Source (19).
	(18) "Tireloui, Tireloui" , appela-t-elle de toutes ses forces.
	trans. "Tireloui, Tireloui", she called with all her strength.
	(19) Il entend Mélisa l'appeler de la cuisine.
	trans. He heard Mélisa calling him from the kitchen.

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