C Hernandez-Aguilar

A Dominguez-Pacheco

M Palma Tenango

C Valderrama- Bravo

M Soto Hernandez

A Cruz-Orea

J Ordonez-Miranda

Lentil Sprouts: a Nutraceutical Alternative for the Elaboration of Bread

Keywords: bread, lentil sprout, wheat flour, secondary metabolites

The pro-health action of germinated lentils could be useful to be added with wheat flour in the production of box bread. In this work, we spectroscopically evaluate the germinated and nongerminated lentils, and use them at the concentrations of 5 and 10% for the production of box bread. The chemical and physical tests of the bread and its determination of phenolic acids and flavonoids (by HPLC) were also performed. As well as the evaluation of the quality of flour and dough used to produce the bread and the acceptance of the germinated lentil bread with a population of 20 people with diabetes or with diabetic relatives It is shown that: i) The amplitude of photoacoustic signal obtained by photoacoustic spectroscopy is modified as a function of the percentage of germinated lentil (GL) flour (0, 5 or 10%) add to the bread; being higher the photoacoustic amplitude to higher concentration of GL in the absorption band of 300 -425 nm, which is related to higher content of phenols and flavonoids. ii) The contents of phenolic acids (Sinapinic, βresorcylic, Chlorogenic and Ferulic) and flavonoids (Quercetin and Isorhamnetin) tended to increase in the germinated lentil bread with 10% concentration of germinated lentil flour with respect to the control bread (0% GL). iii) The addition of germinated lentils flour to 5 and 10% into wheat flour to produce bread with higher hardness and less cohesiveness than bread based on wheat flour only. The Falling number indicate that there is no significant difference between the control sample and the 5% GL flour, while in the 10% GL flour there was a reduction of 21s, with respect to the control. The effect of the germinated lentil flour percentage on the pasting properties of the flours was significant between the control and 10% GL flour. In general, the quality of the dough and flour are modified due to the addition of germinates lentils, and this affectation increases with the increase in the concentration of GL. iv) The bread added with germinated lentil has sensory acceptance with a group of people with diabetes and/or diabetic relatives in their attributes in general. The obtained results thus support the production of wheat bread with mixed germinated lentils flour, as a nutraceutical option for human consumption.

INTRODUCCIÓN

Lentils (Lens culinaris) are leguminous with high nutritional value, bioactive components, antioxidants, and other phytochemicals that render pro-health properties, [START_REF] Srivastava | Saponins and lectins of Indian chickpeas (Cicer arietinum) and lentils (Lens culinaris)[END_REF]. The consumption of this popular legume has been increasing around the world, due to the emerging interest on functional food with nutraceutical value, [START_REF] Jahreis | Legume flours: Nutritionally important sources of protein and dietary fiber[END_REF]. Lentils are a rich and cheap source of proteins, Ladjal-Ettoum et al. (2016) that are often used as a meat substitute, [START_REF] Jallinoja | Future of sustainable eating? Examining the potential for expanding bean eating in a meat-eating culture[END_REF] and they can complement the intake of cereal proteins with several essential aminoacids, Migliozzi et al. (2015). The regular consumption of lentils is thus able to enrich the human nutrition as well as prevent and reduce the development of chronic diseases, such as cardio-vascular maladies, cancers, overweight and obesity, syndrome metabolic, and diabetes, [START_REF] Bouchenak | Nutritional quality of legumes, and their role in cardiometabolic risk prevention: a review[END_REF]. Lentils are therefore vital to improve the nutrition of people in developing countries specially, however their consumption is sometimes limited by the presence of some anti-nutritional constituents, such as saponins, which are natural compounds present in pulses having surface active properties, [START_REF] Ayet | Effect of germination, under different environmental conditions, on saponins, phytic acid and tannins in lentils (Lens culinaris)[END_REF]. High levels of saponins reduce the micronutrient bioavailability, but also have some beneficial effects on the human health, as reported in the literature, Liener (1994). In order to boost the lentils as a human food, the removal of their antinutrients are thus necessary, [START_REF] Ayet | Effect of germination, under different environmental conditions, on saponins, phytic acid and tannins in lentils (Lens culinaris)[END_REF], which can be done by cooking, soaking, and sprouting, [START_REF] Ruiz | Effect of soaking and cooking on the saponin content and composition of chickpeas (Cicer arietinum) and lentils (Lens culinaris)[END_REF]. In fact, the sprout of lentils is an old method applied to improve and expand their nutritional and nutraceutical quality, [START_REF] López-Amorós | Effect of germination on legume phenolic compounds and their antioxidant activity[END_REF] and could hence be used nowadays to increase their acceptance and consumption. The pro-health action of lentil sprouts and other plants is determined by their secondary metabolite content, including phenols, which are a group of compounds with antioxidant, anti-tumour, and anti-inflammatory capabilities, [START_REF] Haminiuk | Phenolic compounds in fruits-an overview[END_REF].

The incorporation of lentil sprouts into basic food, such as bread has the potential to provide functional, nutritional, and nutraceutical benefits in the diet of millions of people around the world, [START_REF] Devani | Study on quality of white bread enriched with finger millet flour[END_REF]. Wheat bread, as one of the most popular types of bread, [START_REF] Cauvain | Bread: the product[END_REF], is an excellent source of energy and abundant in carbohydrates, but it usually contains a low quantity and quality of proteins, vitamins, minerals, and dietary fiber, Devani et al. [START_REF] Jezewska-Zychowicz | Impact of health and nutrition risks perception on the interest in pro-healthy food on the example of bread[END_REF]( ), Jeżewska (2016)), as well as a high glycemic index, [START_REF] Roberts | High-glycemic index foods, hunger, and obesity: is there a connection?[END_REF]. Taking into account that the basic ingredients of bread are wheat flour, water, yeast and salt, [START_REF] Martin | Aeration during bread dough mixing: II. A population balance model of aeration[END_REF]; the mixture of wheat flour with germinated lentils, D' Appolonia (1977) could thus improve the nutritional value of bread with a higher content of proteins and the amino acid lysine present in lentils, [START_REF] Savage | Lentils: a forgotten crop[END_REF].

The goal of this work is to perform the optical characterization of germinated and nongerminated lentils along with the bread made with them. This is done by means of the photoacoustic spectroscopy (PAS) technique, which is a spectroscopic and non-destructive method, [START_REF] Bicanic | On the photoacoustic, photothermal and colorimetric quantification of carotenoids and other phytonutrients in some food: a review[END_REF] that has been applied to study several foodstuffs, such as grains, fruits, vegetables, liquids, semiliquids, condiments, tortillas, flours, and legumes, Hernández et al.

(2019), [START_REF] Bicanic | Correlation of trans-lycopene measurements by the HPLC method with the optothermal and photoacoustic signals and the color readings of fresh tomato homogenates[END_REF].

MATERIALS AND METHODS

BIOLOGICAL MATERIAL

Lentil seeds (Lens culinaris Medik) were acquired in a local supermarket of the state of Mexico and their weight was of 5 g per 100 seeds. The seeds were washed using purified water and then left for 24 h inside a glass bottle with distilled water and covered with a cotton cloth. After this hydration and imbibition process, the seeds were washed again and the excess water was left to drain from the seeds during 10 min. Next, the germinated lentils were allowed to develop, washing them every 24 hours for 5 days. The germinated lentils (GL) were then placed in kraft paper bags and dried in a forced air oven at 40 °C during 72h. The dried GL were afterwards milled to powder by means of a T-Fal blender (550 W) for 3 min, to be used later in bread making. In the same way, Non-GL were also milled to powder and both, the Non-GL and GL were characterized through PAS.

PAS EXPERIMENTAL SETUP

The experimental setup of PAS consists of a Xenon lamp used as the light excitation source, a monochromator applied to obtain a monochromatic light beam, and a mechanical chopper employed to modulate the light beam at the fixed frequency f = 17 Hz, as shown in Fig. 1a. The modulated beam was focused onto an optical fiber to guide it to the photoacoustic (PA) cell, whose signal was then detected by an electret microphone (photoacoustic sensor) connected to the cell through a fine channel (1 mm diameter). The PA signal was amplified by means of a lock-in amplifier (EG&G, Mod. 5210) before being recorded in a personal computer, as a function of beam wavelength. Experiments were performed for wavelengths between 250 nm to 700 nm and by placing the samples (GL and Non-GL powders) into the PA cell with a diameter of 6 mm. Three samples of bread (Fig. 1b) were prepared with the form of circular slides with a diameter of 5 mm, such that they do not obstruct the microphone sensor. In addition, an analysis of the first derivative of the photoacoustic signal was realized.

PHYTOCHEMICAL SCREENING ON GERMINATED AND NON-GERMINATED LENTILS

The extraction of secondary metabolites was carried out from the germinated and nongerminated lentils that were dried and milled. The plant material was weighed and completely submerged into 10 ml of three different solvents (hexane, methanol, dichloromethane), during 72 h. The filtering and separation of water and solvent from the plant material was then carried out by means of a rotary evaporator. The identification of secondary metabolites in germinated and non-germinated lentils was made through the qualitative test proposed by [START_REF] Bhandary | Preliminary phytochemical screening of various extracts of Punica granatum peel, whole fruit and seeds[END_REF] and using these solvents to observe the presence or absence of different compounds.

Each crude extracts obtained with hexane, methanol, dichloromethane was tested to recognize the presence of tannins, alkaloids, flavonoids, saponins and phenols.

ELABORATION OF BREAD

Three types of bread with 0% (B1), 5% (B2) and 10% (B3) of germinated lentils were elaborated with common ingredients of egg (one), salt (1.25 g), honey (5 ml), olive oil (75 ml), and yeast (11 g). The ingredients were mixed for 5 min, in a blender with a spiral hook. After its fermentation in warm water (30 °C), the yeast was incorporated into the mixture, which was then kneaded for 10 min and placed in a mold covered with a soaked cloth during 25 min. Next, the dough was again kneaded for 10 min and put in a greased mold for bread, where increased its volume during 20 min. Afterwards, each type of the three doughs was placed in different molds and simultaneously baked in a pre-heated electric oven (Black Decker) at 180° C, for 45 min. After baking, the bread was allowed to cool down to room temperature during 24 h and subsequently cut it into slices (1.27 cm thick) through an electric knife (Hamilton beach type EK08, 121 V, 11 Hz).

BREAD QUALITY

Bread chemical analysis. -Chemical analysis was determined for germinated lentils flour and bread samples. Moisture content, ash, protein (N x 5.85), fat and fiber were evaluated according to AACC methods 44-15, 08-01, 46-13, 30-25 and 32-07, respectively (AACC, 2000). Water activity (aw) was evaluated by an electronic hygrometer (Acqua Lab, MB45).

Bread physical analysis. -The specific volume of the bread was determined as bread volume divided by bread weight. Bread volume was determined by AACC rapeseed displacement method, 10-05 [START_REF] Aacc | Approved methods of the American Association of Cereal Chemists[END_REF], which was modified using linseed. After mechanically compacting the bread to exclude all empty spaces. Weights were measured using a 2-decimal precision scale. Bread porosity was evaluating by equation:

𝑷𝒐𝒓𝒐𝒔𝒊𝒕𝒚 = ( 𝑹𝒆𝒂𝒍 𝒅𝒆𝒏𝒔𝒊𝒕𝒚 -𝑨𝒑𝒑𝒂𝒓𝒆𝒏𝒕 𝒅𝒆𝒏𝒔𝒊𝒕𝒚 𝑫𝒆𝒂𝒓 𝒅𝒆𝒏𝒔𝒊𝒕𝒚 ) * 𝟏𝟎𝟎
Where, apparent density was obtained as inverse of specific volumen and real density is solid density without empty spaces.

Color. -Bread crumbs were measured using a Minolta CR-300 colorimeter (Minolta, Osaka, Japan). The color parameters CIELAB (L*, a, and b, where L*=luminosity (100= white, to 0=black), the a= greenish-reddish, and the b*= yellowish-bluish.

Texture profile analysis and statistic analysis. -Bread texture analysis was performed using a Texture Analyzer Brookfield Model CT3 25 K USA. Bread samples were cut into cubes of 3

x 3 x 3 and it were kept in a polyethylene bag for 15 min, and then the texture profile analysis (TPA) of the bread was performed. The samples were measured to a 33% compression cycle using a TA General Probe Kit with a TA25/1000 test probe cylinder 50.8 mm diameter and 20 mm length at a speed of 1 mm/s. Two compression cycles were measured. Hence, it was possible to measure hardness, elasticity, cohesiveness and chewiness. Three replications were conducted at 24 ± 1°C. The variance of the obtained data was analyzed with a generalized linear model (GLM) and the software SAS (2008 version). A comparison of the mean values of the low significant differences (LSD) was also done.

MASS AND FLOUR QUALITY

Falling number. -El Falling number se realizó a las muestras de harina y se empleó una máquina de Falling number (Perten Falling number FN1310, Perten Instruments, Suiza). The method approved by AACC 56-81.03, AACC International (2010) was used. 7 g of sample were weighed and placed in the viscometer tube, which was mixed with 25 mL of distilled water. The sample was stirred with the viscometric tube until a homogeneous suspension was obtained. The tube is placed in a water bath and the device is activated and after 5 s the agitation begins automatically and after another 55 s the agitator is automatically released in its upper position and begins the descent due to its weight. The total time in seconds, elapsed from the moment the appliance activates until the agitator descends a certain distance is the Falling number.

Pasting properties. -The starch pasting profiles of nine water suspensions samples were analyzed using Anton Paar MCR 102 Rheometer, equipped with a starch cell using the methodology proposed by, Rincon-Londoño et al. (2016). The starch samples (3g) were suspended in 18 mL of water. The suspension was heated over 5 min from 50-90°C. Next, suspension was maintained at constant temperature of 90°C for 5 min and finally the samples were cooled down to 50°C for 5 min. The rotation speed of the system was 194 rpm. preferences and attributes in general; chosen to evaluate each of the breads using a hedonic scale, [START_REF] Laureati | Sensory and hedonic perception of gluten-free bread: comparison between celiac and non-celiac subjects[END_REF]. The third section was about of asked direct questions related to their purchase decision on the proposed breads and their healthy preferences, as well as their health status of them and / or relatives mainly. Before the application of the questionnaire, the participants were informed about the purpose of the test, as well as the general indications about it for a better evaluation of each of the attributes of each bread. Additionally, the participants were provided with a bowl of water and other of coffee grains to be employed during the test.

RESULTS AND DISCUSSION

The amplitude values of the photoacoustic signal (PA) of germinated and non-germinated lentils are reported in Table 1 (section A), for the wavelengths of 480, 500, 550, 600 and 650 nm. Note that the amplitude of Non-GL is lower than the one of GL, in the range of 300 -410 nm (Figure 2a), such that the difference of both amplitudes take its maximum values within the interval of 300 -350 nm. For the wavelength range of 410 -700 nm, on the other hand, the amplitude of non-GL is higher than that of GL, such as their difference is maximum at 650 nm.

At this latter wavelength, the amplitudes of the non-GL and GL were of 0.49 and 0.2, respectively, which represents a variation of 59%. Significant statistical differences were also found for the wavelengths of 480, 500, 550, 600 and 700 nm.

According to Figure 2a1, the germinated lentils show a maximum absorption peak at 310 nm, while the non-germinated ones exhibit two peaks at 275 and 325 nm. This could be due to the increased content of secondary metabolites (i.e. phenols) that constitute the GL, Bartolome et al. (1997) and typically generate spectral peaks for wavelengths between 250 -265 nm and 280 -300 nm, [START_REF] Amarowicz | Antioxidant activity of phenolic fractions of lentil (Lens culinaris)[END_REF]. This fact is confirmed by the phytochemical evaluation summarized in Table 2, which shows that the phenols had a minimal presence (+) in non-GL and a moderate one (++) in GL, when methanol was used as solvent. On the other hand, taking into account the presence of tryptophan usually shows up for wavelengths around 277 nm, [START_REF] Amarowicz | Antioxidant activity of a red lentil extract and its fractions[END_REF]. The PA signal in Fig. 2a1 indicates that the GL contains a bigger quantity of the essential amino acid than the non-GL. These results are consistent with previous works based on PAS, [START_REF] Dóka | Rutin in buckwheat grain meal determined by UV photoacoustic spectroscopy and HPLC[END_REF], [START_REF] Bergevin | Assessment of strawberry maturity by photoacoustic spectroscopy[END_REF], [START_REF] Dóka | Photoacoustic approach to direct determination of the total phenolic content in red sorghum flours[END_REF] which reported absorbance peaks at the wavelengths of 275 and 278 nm for buckwheat and strawberries, respectively.

The significant variations in the second (430 -544 nm) and third (650-700 nm) absorption bands can be associated to the changes in the contents of saponins, [START_REF] Du | Quantitative Analysis Method of the Tea Saponin[END_REF] and green pigments, respectively. According to Table 2, the GL contain less saponins and more phenols and flavonoids than the non-GL.

The differences among the spectra of germinated and non-germinated lentils can be enhanced through the first and second derivatives of the PA signal. The intersections of the first derivative with the horizontal axis correspond to the absorption peaks, which are different for the GL (300, 565, 585, 625, 635, and 671 nm) and non-GL (285, 325, 561, 585, 600, 632 and 675 nm). On the other hand, the negative peaks at 310 and 370 nm of the second derivative are related to the absorption band (shoulder) of the PA signal. Figure 2a2 shows that the peak at 0.12 mV of the data density for the non-GL is shifted to 0.09 mV after germination. This indicates that, on average, the GL tends to generate a photoacoustic signal of less intensity. In addition, the probability density functions estimated by "Kernels" present a multimodal behavior, which points out that the data set of the PA signals come from different populations because of the complex and inhomogeneous nature of non-GL and GL with different components evidenced in the different absorbent bands. For non-GL, we have two bands and one shoulder, which correspond to phenols, flavonoids and saponins. On the other hand, for GL, there is two bands and two shoulders due to phenols, flavonoids, saponins and green pigment.

Other results in this research are related to the bread made with germinated lentils.

Photoacoustic spectrum (Fig. 2b1) and the Kernel estimator (Fig. 2b2) of the wheat bread elaborated with 0 (B1), 5 (B2) and 10% (B3) of germinated lentils is shown in Figure 2b. It is possible to observe how the level of the photoacoustic signal is modified as a function of the concentration of GL flour (0, 5 or 10%) and the wavelength. There is a maximum absorption band (at the beginning of the spectrum of each one of the photoacoustic signals of each bread), which tends to decrease with the increase of the wavelength (Fig. 2b1). Being higher the photoacoustic signal to higher concentration of GL; this increase is accentuated in the absorption band of 300 -425 nm. ).

Figure 2b1, shows maximum peak at 310 nm, where the photoacoustic signal of wheat bread added with GL is increased by 34% with respect to the signal obtained from wheat bread (0% GL). Bread spectra made with germinated lentils (Figure 2b1) do not show the absorption band corresponding to green pigment that was observed in non-germinated lentil and germinated lentils spectra (Figure 2a1). In the Figure 2b2 is shown that in the case of the wheat bread there is a maximum in the region close to the value of 0.031 mV corresponding to the photoacoustic signal intensity, which indicates in this region the values occur with higher frequency. In the case of germinated lentils bread at 5 and 10 %, it is possible to observe that the maximum occurs for a value of 0.026 and 0.035, respectively. In the density function of the PA signal a population is clearly distinguished, unlike of the density function of germinated and non-germinated lentils. This may be due to the bread baking process. So, in the elaboration of wheat bread with added germinated lentil, the bread components are modified.

The absorbent bands found, according to these results with significant statistical differences are basically in the band corresponding to phenols and flavonoids. It could be said that the elaboration of bread, with added germinated lentils (GL), improves its nutraceutical characteristics.

Chemical and physical analysis

Table 3 shows the parameters obtained from the chemical and physical analysis i.e. moisture (%), water activity, protein, fat, fiber, ashes, specific volume, porosity, color (L, a, b) and texture profile analysis (TPA). The results of the chemical analysis of the germinated lentil flour in percentage were, protein (23.51 ± 0.21), fat (3.40 ± 0.04), ashes (2.61 ± 0.16) and fiber (8.11 ± 0.22), which show a high value of protein and fiber content. Regarding the chemical analysis of bread, it is possible to see in Table 3, that the amount of protein in bread B3 increases with respect to B1 by 20% and the amount of fat and fiber decreases 30 and 34%, respectively.

On the contrary, the ashes increase as the amount of germinated lentil added to the bread increases. Lentils are a rich source of proteins, Ladjal-Ettoum et al. (2016) and they can complement the intake of cereal proteins, Migliozzi et al. (2015). In this case, it complements wheat for bread making. Bread being a food of daily consumption by most people, the population could enrich their diet by eating bread added with germinated lentil flour. Which, as noted, improves nutritional quality and also, by increasing fiber and decreasing fat, it is beneficial when the persons have health problems

or in preventive stages of several sicks, Bouchenak and Lamri-Senhadji ( 2013).

The specific volume data are similar to those reported by [START_REF] Ho | Quality and antioxidant properties of bread containing turmeric (Curcuma longa L.) cultivated in South Korea[END_REF] who studied bread with turmeric, obtaining values of 5.2 to 4.0 mL / g. The lowest specific volume value was for bread with 10% (B3) germinated lentils because during the germination period the activity of proteases is limited [START_REF] Marti | Sprouted wheat as an alternative to conventional flour improvers in bread-making[END_REF] possibly due to the high activity of -amylase that it could affect the gluten network. In relation to the porosity of the bread, it was found higher in the wheat bread added with 10% GL (B3), increasing with respect to the wheat bread (0% GL) of almost 14%.

On the other hand, the color of the breadcrumbs showed significant difference that were affected by the addition of germinated lentil flour. The value of L decreases significantly, the value of "a" increases and in "b" there is no significant difference. The changes may also be associated with an increase in Maillard reactions [START_REF] Marti | Flour from sprouted wheat as a new ingredient in bread-making[END_REF]. As well as properly the color of the germinated lentil that is added. Color is one of the relevant attributes for consumer acceptance and deciding on its purchase, Herrera et al. (2007).

Regarding texture profile analysis (TPA) of the bread made, it was found that the bread added with 10 % germinated lentils flour presented the highest values of strength in the two compression cycles (Fig. 3a).

Table 3 shows the parameters obtained from the TPA and it is observed that for the cohesiveness there was no significant difference ( ), being the bread added with 10% of flour of germinated lentils those that presented the highest values. [START_REF] Schiraldi | Mechanism of staling[END_REF], reported that the hardness of bread is attributed to the matrix formed between amylose and amylopectin. However, [START_REF] Ghumman | Impact of germination on flour, protein and starch characteristics of lentil (Lens culinari) and horsegram (Macrotyloma uniflorum L.) lines[END_REF] indicated that germination results in the decomposition of high molecular weight proteins to low molecular weight proteins that have a large number of polar groups, which increases the water retention capacity, which can influence the hardness values obtained and chewiness of breads added with germinated lentils flour. Likewise, lentil sprouts increase in fiber content and decrease in fat, [START_REF] Chung | In vitro starch digestibility, expected glycemic index, and thermal and pasting properties of flours from pea, lentil and chickpea cultivars[END_REF]. The addition of fiber in baked goods is a critical ingredient because although it is used to prolong the freshness of the bread given its water retention capacity, it can modify its volume, smoothness and firmness, Sangnark and [START_REF] Noomhorm | Chemical, physical and baking properties of dietary fiber prepared from rice straw[END_REF]. The addition of excess fiber can modify the gluten network due to the interaction between gluten and fiber, [START_REF] Noort | The effect of particle size of wheat bran fractions on bread quality e evidence for fibre protein interactions[END_REF].

The highest elasticity was of the bread added with 10% germinated lentils flour, while between the bread (control) and the bread added with germinated lentils flour to 5% there was no significant difference.

According to what was reported by [START_REF] Hoseney | Principles of Cereal science and technology[END_REF], during baking there are interactions between the gelatinized starch and the wheat flour gluten. The bread obtained with 10% of germinated lentils flour formed a crumb with greater hardness and elasticity, possibly due to the greater resistance of the swollen starch during the cooking process. However, the cohesive value was the lowest, probably originated by the dilution of the structure of the gluten (interaction between gluten and fibers), reducing the number of disulfide bonds that modify the structure of the gluten completely, forming a little mass structure cohesive.

The results indicate that adding germinated lentils flour in the two percentages 5 and 10%, to the wheat flour modifies the textural properties of the bread in comparison with the bread elaborated only of wheat flour. Bread added with germinated lentils is obtained with greater hardness and for the case of bread with 10%, less cohesive. However, breads elaborated from sprouted seed flours contain significant amounts of polyphenols, [START_REF] Caccialupi | Bowman-Birk inhibitors in lentil: Heterologous expression, functional characterization and anti-proliferative properties in human colon cancer cells[END_REF]. Lentil and sprouted seeds are a good source of high quality starches and proteins, as well as fiber and low fat content. They have low glycemic index, slowly release the glucose in the blood, which transmits and ensures a more stable insulin response. For these reasons lentil sprouts are desirable for a population with diabetes and overweight, [START_REF] Chung | In vitro starch digestibility, expected glycemic index, and thermal and pasting properties of flours from pea, lentil and chickpea cultivars[END_REF].

DOUGH AND FLOURS QUALITY

Falling Number

The Falling number data shown in the Table 4 indicate that there is no significant difference between the control sample and the 5% GL flour, while in the 10% GL flour there was a reduction of 21s, with respect to the control. This behavior is due to the mixture of GL flour that has a higher activity of -amylase, which acts on the amylase breaking glucosidic bonds, causing changes in starch.

Pasting properties

The effect of the germinated lentil flour percentage on the pasting properties of the flours was significant between the control and 10% GL flour. Maximum viscosity values decreased 8.5%

between control flour and 10 GL%. The maximum viscosity indicates the ability of the starches to swell before physical decomposition (break down) [START_REF] Ghumman | Impact of germination on flour, protein and starch characteristics of lentil (Lens culinari) and horsegram (Macrotyloma uniflorum L.) lines[END_REF]. This behavior is attributed to the increase in germination that causes the degradation and de-branching of starch to simpler units [START_REF] Ingbian | Proximate compositions, pasting and rheological properties of mumu e a roasted maize meal[END_REF]. The decrease in the maximum viscosity of the mixtures of wheat flour with lentil sprout flour could be related to the higher amylose content of lentil starch (35%) with respect to wheat starch, because it seems that starch interactions and proteins are modulated by the amylose to amylopectin ratio [START_REF] Joshi | Rheological and microstructural characteristics of lentil starch-lentil protein composite pastes and gels[END_REF]. In the viscosity values of the break down if there was a significant difference between the three treatments.

Between the control sample and the 10% GL there was a higher decrease in viscosity of 16%. [START_REF] Singh | Influence of early and delayed transplantation of paddy on physico-chemical, pasting, cooking, textural and protein characteristics of milled rice[END_REF], reported that the breakdown is a measure of the ease with which swollen granules can detach during continuous heating and shearing. In the final viscosity, which is the ability for the material to form a viscous paste, the control sample had the highest values and the 10% GL flour decreased 12%. These data agree with those obtained in Falling number in which the 10% GL flour showed a higher drop and this is attributed to a higher activity of amylase.

Rheological parameters

The elastic (G´) and viscous (G´´) modules are shown in Figure 3b. In all cases the values of G´, predominates with the values of G´´. Then the mass is predominantly considered a gel or solid; this behavior is considered a weak gel. [START_REF] Rubel | Fibre enrichment of wheat bread with Jerusalem artichoke inulin: Effect on dough rheology and bread quality[END_REF] mention that high values of G´ indicate a higher character of a solid. The results show that the control and 5% GL mass have no significant difference. However, the 10% GL mass showed higher values of the modules.

The addition of a higher percentage of germinated lentil flour alters the starch-gluten matrix, affecting the viscoelastic behavior of the dough and limiting its handling [START_REF] Belghith | Wheat bread enrichment by pea and broad bean pods fibers: Effect on dough rheology and bread quality[END_REF].

Alveograph testing

The parameters evaluated in the alveograph showed significant difference by increasing the percentage of germinated flour. The dough strength or tenacity (P) It is indicative of the ability of the dough to retain gas [START_REF] Belghith | Wheat bread enrichment by pea and broad bean pods fibers: Effect on dough rheology and bread quality[END_REF]. The results (Table 3) show that there is no significant difference between the control and the 5% GL sample. The extensibility of the mass (L) is related to the handling of the mass. In this research, it is found that the values of L of the mass 10% GL is 30% lower than with the control sample. These parameters affect the P / L ratio that for this study the highest value was for 10% GL (1.24); which indicates that this mass has a higher tenacity than extensibility. This result justifies the data obtained in the elastic modules (Fig. 3b), because the highest values was also for 10% GL, which represents a mass of greater rigidity, which is not handling due to its low extensibility value. Deformation work values (W), swelling index and elasticity index decreased as the percentage of lentil sprouts increased. These results show us that there is a weakness in the gluten network, manifesting itself to a higher extent in sample 10% GL. One of the reasons is that germinated lentil proteins compete for water, delaying gluten hydration or it could also be that GL proteins interact with gluten proteins [START_REF] Roccia | Influence of soy protein on rheological properties and water retention capacity of wheat gluten[END_REF].

PHENOLIC ACIDS AND FLAVONOIDS BY HPLC

The phenolic acid most abundant in the bread control (without GL) was the Protocatechuic acid (0.385 µg/mL) followed by βresorcylic acid (0.096 µg/mL) and Rosmarinic acid ( 0.094 µg/m). In the case of bread with a higher percentage of GL (10%), the most abundant were Protocatechuic acid (0.380 µg/mL) followed by Sinapinic acid (0.117 µg/mL) and βresorcylic acid (0.098 µg/mL).

The contents of phenolic acids: Sinapinic and βresorcylic in the bread B3, tended to increase more than 100 and 90%, respectively with respect to the control bread, without significant statistical differences. In relation to the content of Chlorogenic acid and Ferulic acid when comparing the three types of bread (0, 5 and 10% of GL), significant statistical differences were presented with an increase at 22 and 5%, respectively. Unlike phenolic acids of the type Rosmarinic and Protocatechuic, tended to decrease when compared with respect to the content found in the control samples.

In the case of flavonoids: Quercetin and Isorhamnetin in the control bread (without GL) were not found. They were only detected in the bread with the highest percentage of GL (B3), with the flavonoid most abundant Quercetin (0.245 µg / mL) followed by Isorhamnetin (0.211 µg / mL). Of this way, the obtained results support the production of wheat bread with mixed germinated lentils flour; as a nutraceutical option for human consumption due to its content of phenolic acids and flavonoids (see Table 4).

BREAD SENSORY ACCEPTABILITY

The ANOVA results revealed significant (P <0.05) differences among, the evaluations according to the hedonic scale in 6 attributes of bread sensory acceptability (Table 5): porosity, sponginess, hardness, cohesiveness, healthy option and general attributes. It can be seen, based on the attributes in general, the order of preference according to the scores was first the wheat bread added with 5% GL (B2), followed by the bread added 10% (B3) and finally the control wheat bread (0 % of GL-B1).

According to the information provided by the participants through the questionnaire; the majority have relatives with diabetes or hypertension, so they look for generally healthy foods; at least in this sample of participants in which the study was conducted of the sensory acceptance. In the score for the bread attribute that is considered healthier, the sample of participants choose, with the score most higher the wheat bread 10% GL, followed by the B2 bread (5% GL) and in the lowest score is the wheat bread of control (0% GL). It should be noted that all consumers (100%) surveyed replied that they would accept to consume the types of bread made with germinated lentil flour proposed in this research due to their nutritional properties, to that could be of lower glycemic index (for the information provided according to the literature scientific) and also for not having conservatives. Another attribute of interest to consumers surveyed in this study is the use of preservatives in the food. All participants prefer to consume bread without preservatives.

Sensory acceptability for porosity, hardness and cohesiveness attributes was evaluated in a higher score for wheat bread without germinated lentil flour (B1), with a score of 7.53, 7.06 and 7.09, for each one of these attributes. The bread with the lowest sensory acceptability according to these attributes is B3 (10% of GL) with a score obtained of 6.26, 6.53 and 6.12. It is important to point out that sensory acceptability decreased around 20% in these sensory properties of bread when germinated lentil flour is added in the elaboration of the bread. With respect to the flavor attribute, the trend of sensory acceptance of consumers, it is mostly for bread B2 (5% GL), since it tends to have the highest score (7.06), compared to the others, mainly control bread (0% GL-score 6.33). In this way, consumers in the sensory acceptance analysis prefer 10% more to bread with 5% GL, compared to control bread (0% GL). With respect to color of the bread, the consumers tend to have higher acceptability for B1 bread (0% GL-score 7.13) and the less acceptability for B3 bread (10% GL-score 6.73; being 5%, preferred B1 with respect to B3. It is possible to observe that consumers are looking for more nutritious products and that these could have less impact on the level of sugar in the blood. This is associated by the cases of diabetes of them or their relatives, according with the answers from of the questionnaire.

CONCLUSIONS

The addition of germinated and non-germinated lentils flours to the wheat flour has spectroscopically been evaluated and used for the production of box bread. It has been shown

that: (i) The level of photoacoustic signal obtained by photoacoustic spectroscopy is modified as a function of the concentration of germinated lentil (GL) flour (0, 5 or 10%) in the bread;

being higher the photoacoustic amplitude to higher concentration of GL in the absorption band of 300 -425 nm, which is related to higher content of phenols and flavonoids. (ii) The addition of germinated lentils flour with concentrations of 5 and 10% into wheat flour allows to produce bread with higher hardness and less cohesiveness than bread based on wheat flour only. (iii)

The contents of phenolic acids (Sinapinic, βresorcylic, Chlorogenic acid and Ferulic acid) and flavonoids (Quercetin and Isorhamnetin) tended to increase in the germinated lentil bread with 10% concentration of germinated lentil flour with respect to the control bread (0% GL). iv) The addition of germinated lentils flour with concentrations of 5 and 10% into wheat flour to produce bread with higher hardness and less cohesiveness than bread based on wheat flour only.

v) The Falling number indicate that there is no significant difference between the control sample and the 5% GL flour, while in the 10% GL flour there was a reduction of 21s, with respect to the control. The effect of the germinated lentil flour percentage on the pasting properties of the flours was significant between the control and 10% GL flour. vi) In general, the quality of the dough and flour are modified due to the addition of germinates lentils, and this affectation increases with the increase in the concentration of GL. vii) The bread added with germinated lentil has sensory acceptance with a group of people with diabetes and/or diabetic relatives in their attributes in general.

  Alveograph testing. -Viscoelastic behavior of dough was studied by the alveograph test, using an Alveolab Graph 2017 equipment (Chopin, France), following standard method, AACC,(2000). The following alveograph parameters were automatically recorded by a computer software program: tenacity (P), dough extensibility (L), curve configuration ratio (P/L ratio), swelling index (G), elastisity index (I.e) and the strain work (W).Rheological parameters. -Rheological parameters of the masa were measured by running oscillatory tests in a TA Instruments Rheometer, model RT 20 Haake, New Castle (USA) with a rough plate of 2.5 mm. A strain sweep test was performed to determine the linear viscoelastic region (LVR) in a range of 20 to 150 Pa at 25°C. Frequency sweep, which increased from 0.1 to 30 Hz were evaluated to determine storage (elastic) modulus (G´) and loss (or viscous) modulus (G´´) using the accompanying software.ANALYSIS OF PHENOLIC ACIDS AND FLAVONOIDS BY HPLCExtracts were obtained with 50 mg of dry and pulverized material in 1 mL of methanol of HPLC grade (Sigma-Aldrich number 36860) at 80%, incubated for 20 min in an ultrasound bath (BRANSON at Smithkline company 50/60 Hz, model B-220, USA), Meneses Reyes et al.,(2008). The crude extracts were centrifuged at 731 g (Centrifuge 5804 eppendorf, model 5804) for 10 min,[START_REF] Irakli | Optimization of ultrasound-assisted extraction of phenolic compounds: Oleuropein, phenolic acids, phenolic alcohols and flavonoids from olive leaves and evaluation of its antioxidant activities[END_REF]. Supernatants were filtered with 25 mm diameter acrodiscs with nylon membrane and 0.45 mm pore size (Titan). These extracts were injected immediately for the analysis by HPLC of phenolic acids and flavonoids. The samples were analyzed in a Hewlett Packard® chromatograph mod. 1100 provided with diode array detector and an Agilent Technologies automatic injector model 1200. The column was a Hypersil ODS HP column of distilled water adjusted to pH 2.5 with trifluoroacetic acid (A) and acetonitrile (B),[START_REF] Bilia | Characterization of Calendula Flower , Milk-Thistle Fruit , and Passion Flower Tinctures by HPLC-DAD and HPLC-MS[END_REF]. The analysis was by gradient: T1 0.10 min (85% A) (15% B); T2 20 min (65% A) (35% B) and T3 25 min (65% A), (35% B), λ= 254, 280, 330 and 365 nm, Column temperature 30 ° C and flow of 1 mL min -1 . Calibration curves were performed for standards: the sinapinic acid, βresorcylic acid, chlorogenic acid, ferulic acid, rosmarinic acid, protocatechuic acid, quercetin and Isorhamnetin (Sigma-Aldrich®)The interpolations of all the extracts were calculated with ChemStation software © Agilent Technologies, Inc. 2004.SENSORY ACCEPTABILITYMexican consumers (20) who regularly consumed bread, were selected to participate in this study. Consumers participating mostly reside in the city and state of Mexico and between 25 and 70 years old. The test was conducted in the experimental classroom of the "ESIME, Zacatenco". This classroom had lights illumination natural and fluorescent tube lamps. Each consumer was given an evaluation instrument (questionnaire) and received a set of three complete pieces of bread placed inside a plastic bag (low density polyethylene) hermetically sealed one hour before carrying out the test. The bread was made 7 h before being placed in the bags. The questionnaire applied was divided into three parts: the first consisted of general data of each consumer, the second corresponded to the list of 11 attributes (color, porosity, sponginess, hardness, cohesiveness, aroma, stickiness, chewiness, flavor and healthy

Figure 1 Figure 2 .

 12 Figure 1. a) Scheme of the PAS experimental setup used in our experiments and b) bread made with 0, 5 and 10 % of germinated lentils flour.

  Table 1 shows significant statistical differences in this absorption band, when is compared in 300, 350 and 400 nm. It is observed the comparison of

	p		05 . 0

mean amplitude of the photoacoustic signal at various wavelengths of the photoacoustic spectrum of bread (Section B), where means with different letter in a column are statistically different (LSD (Lows significant differences,

Table 1 .

 1 Comparison of the mean amplitude of the photoacoustic signal generated by A) non-germinated and germinated lentils as well as B) bread, for various wavelengths.

	Material		300		350		400		450		480		500		550		600		650		700
				A) Non-germinated and germinated lentils

Table 2 .

 2 Identification of presence of secondary metabolites of germinated and non-germinated lentils.

	Secondary	Hexane extract	Methanol extract	Dichloromethane
	metabolites			Extract
		Non-germinated
		lentils
	Tannins	_	_	_
	Alkaloids	_	_	_
	Flavonoids	_	+	+
	Saponins	_	++	+
	Phenols	+	+	+
		Germinated Lentils
	Tannins	_	_	_
	Alkaloids	_	_	_
	Flavonoids	_	++	+
	Saponins	_	+	-
	Phenols	+	++	+
	+++ Abundant presence, ++ Moderate presence, + Minimum presence and -Absence

Table 3 .

 3 Chemical, physical and quality analysis of bread and quality of dough and flour used in its preparation.Mean ± standard deviation values followed of different letter in each column, are significantly different (ρ < 0.05).

			Control	Lentil bread	Lentil bread
	Test		bread	5%	10%
	Moisture (%)		36.80 ± 0.22 a 36.92 ± 0.23 a 35.95 ± 0.11 b
	Water activity		0.924 ± 0.001 a 0.927 ± 0.002 a 0.919 ± 0.002 b
	Protein (%)		10.51 ± 0.05 b 11.76 ± 0.15 a 12.59 ± 0.52 a
	Fat (%)		12.16 ± 0.52 a 10.37 ± 0.94 b	8.60 ± 0.15 b
	Fiber (%)		2.79 ± 0.10 a	3.05 ± 0.15 a	1.85 ± 0.12 b
	Ashes (%)		0.89 ± 0.02 a	0.90 ± 0.06 a	0.96 ± 0.02 a
	Specific volume (mL/g)	4.67 ± 0.19 a	4.59 ± 0.17 a	4.03 ± 0.11 b
	Porosity (%)		27.91 ± 1.05 b 27.59 ± 0.21 b 31.67 ± 0.36 a
	Color	L	70.68 ± 1.75 a 68.66 ± 0.70 ab 62.21 ± 0.84 b
		a	-0.88 ± 0.03 c -0.65 ± 0.03 b -0.35 ± 0.04 a
		b	18.62 ± 0.88 b 17.55 ± 0.08 b 17.63 ± 0.18 b
	Hardness (N)		3.20 ± 0.61 c 13.36 ± 1.03 b 25.93 ± 4.96 a
	Elasticity (mm)		2.97 ± 0.23 b	2.54 ± 0.07 b	3.99 ± 0.45 a
	Cohesiveness		0.78 ± 0.07 a	0.62 ± 0.02 a	0.59 ± 0.09 a
	Chewiness (mJ)		7.33 ± 1.32 c 21.27 ± 1.52 b 57.17 ± 5.33 a
			0%GL	5% GL	10% GL

Table 4 .

 4 Concentration of phenolic acids and flavonoids in extracts of bread added with different percentages of germinated lentil (0, 5 and 10%). There were significant differences between treatments according to the analysis of variance (p ≤0.05).The different letters indicate significant differences between the Tukey means test (α = 0.05, n=9). ND:

	Phenolic acid	B1	B2	B3		Media
	(µg/mL)	(Control)	5%	10% DMS	R 2
	Sinapinic acid
		0.058 a 0.117 a	0.118 a 0.197	0.42	0.098
	β-resorcylic acid
		0.096 a	0.098 a	0.183 a 0.468	0.21	0.126

*

Table 5

 5 Mean consumer scores for sensory acceptability of 3 breads (0, 5 and 10% of GL).

										Healthy	General
		Color Porosity Sponginess Hardness cohesiveness Aroma Stickiness Chewiness Flavor	option	attributes
	0%	7.13a 7.53a	7.06ba	6.13b	7.09a	6.4a	6.6a	6.66a	6.33a	5.2b	6.4b
	5%	7.06a 7.4a	7.4a	6.86ba	6.5ab	6.93a	6.4a	6.93a	7.06a	7.4a	7.93a
	10%	6.73a 6.26b	6.53b	7.2a	6.12b	7.06a	6.06a	6.06a	6.86a	8.13a	7.4a
	LSD
	(0.05%)	0.69 0.75	0.76	0.89	0.83	0.78	0.96	0.98	0.94	1.03	0.86
	Media	6.97 7.06	7	6.73	6.57	6.8	6.35	6.55	6.75	6.93	7.24
	Significance 0.45ns 0.0033* 0.04*	0.05*	0.05*	0.20ns 0.52ns	0.19ns	0.27ns 0.0001**	0.004*
	C.V.	13.55 14.35	14.68	17.69	17.3	15.45 20.27	19.9	18.77 19.98	16.04
	R 2	0.68 0.63	0.61	0.5	0.52	0.71	0.57	0.56	0.56	0.68	0.57

ACKNOWLEDGMENTS

The authors are grateful for the support of the National Polytechnic Institute through the projects SIP, EDI, and COFFA. Claudia Hernandez-Aguilar thanks the collaboration of Cinvestav and Fes-Cuautitlán, through of Esther Ayala and I.A. Miriam Edith Fuentes Romero, for his assistance and support during the use of the infrastructure in the respective laboratories.


Cindy Aloui
email: cindy.aloui@gmail.comalexis.nasr@lis-lab.fr

Carlos Ramisch
email: carlos.ramisch@lis-lab.frlucie.barque@univ-paris13.fr

Alexis Nasr

Lucie Barque

SLICE

Keywords:

published or not. The documents may come

Introduction

The form-meaning association relating words to their senses is a fundamental component of human languages. Hence, lexical semantics, that is, the representation of the meaning of words, is an important research topic in computational linguistics. Processing word meaning is essential for the (compositional) interpretation of larger units such as phrases and sentences. Therefore, computational lexical semantics is, explicitly or implicitly, at the core of higher-level NLP tasks such as textual understanding, information extraction, and automatic summarisation.

Much effort has been put in the manual and semi-automatic construction of resources encoding lexical semantics (i.e., word meaning). These include semantic lexicons with inventories of possible senses that lexical units can assume (e.g., Wordnet) and sense-annotated corpora specifying which of these senses are employed in context (e.g., SemCor). Alternatively, real-numbered vectors can encode contextual cooccurrence, acting as a proxy for a lexical unit's semantics. This principle has guided the development of numerous distributional semantic models, that is, semantic vector representations inferred from corpus co-occurrences, e.g., [START_REF] Landauer | A solution to plato's problem: The latent semantic analysis theory of the acquisition, induction, and representation of knowledge[END_REF]. Advances in neural networks shifted the focus of computational semantics to representation learning, so as to obtain vectors as by-products of neural networks [START_REF] Mikolov | Efficient estimation of word representations in vector space[END_REF], In this booming field, a myriad of models have emerged, efficiently learned from corpora, benefiting from high-performance neural architectures and libraries. Thus, vector representations, rebranded word embeddings, have become the dominant technique to represent lexical units, at the core of state-of-the-art neural approaches.

Traditional static embeddings, such as word2vec and Fasttext, assume that each word's meaning can be represented as a single vector, independently of its context. While generic and reusable, these models usually conflate the different meanings of a given unit into a single vector [START_REF] Camacho-Collados | From word to sense embeddings: A survey on vector representations of meaning[END_REF]. Contextual models, such as ELMo, GPT-2, BERT, and their variants, encode each word's occurrence as a context-dependent vector, assuming that each context corresponds to a different sense [START_REF] Yarowsky | One sense per collocation[END_REF]. In short, while static models create one generic embedding per lexical unit, contextual models provide a fine-grained distinct representation for each occurrence. Both models, but especially the latter, are increasingly complex and opaque [START_REF] Rogers | A primer in bertology: What we know about how bert works[END_REF], requiring advanced techniques to help humans understand their strengths and limitations [START_REF] Jawahar | What does BERT learn about the structure of language[END_REF][START_REF] Serrano | Is attention interpretable[END_REF].

Given this landscape, we introduce SLICE: an alternative semantic model which constitutes a trade-off between static, interpretable symbolic senses and contextual word embeddings. We propose a weakly supervised technique to build dense low-dimensional embeddings whose dimensions represent coarsegrained semantic classes i.e., supersenses such as ANIMATE ENTITY and NATURAL OBJECT (Sec. 3). Our lightweight model embeds both lexical units and their contexts into the same semantic space. Thus, words and their contexts are represented as two compact vectors of directly interpretable scores, one per supersense, automatically learned from a non annotated corpus. Our embeddings are assessed in a supersense tagging setting (Sec. 4). Thanks to the model's interpretability, we are able to perform a rich linguistic analysis of the results, providing insights to understand the model's predictions (Sec. 5).

Related Work

Our work is positioned at the crossroads of word and sense embeddings, interpretable semantic representations, and weakly supervised semantic classification. We briefly review a sample of relevant work on these topics.

Word and sense embeddings The literature on vector-space semantic representations is enormous, ranging from traditional models such as LSA [START_REF] Landauer | A solution to plato's problem: The latent semantic analysis theory of the acquisition, induction, and representation of knowledge[END_REF] to sophisticated deep contextualised embeddings such as BERT [START_REF] Devlin | Bert: Pre-training of deep bidirectional transformers for language understanding[END_REF]. Although techniques are being constantly improved, the main principle is stable across models: vectors represent a word's usage (and meaning) based on its distributional context [START_REF] Harris | Distributional structure[END_REF]. Embeddings have become commonplace in NLP, as they naturally represent input (words) in state-of-the-art neural models. Although they can be randomly initialised and learned, unsupervised pre-training on raw corpora is common [START_REF] Turian | Word representations: A simple and general method for semi-supervised learning[END_REF].

Embeddings can be pre-trained as by-products of predictive neural language models [START_REF] Mikolov | Efficient estimation of word representations in vector space[END_REF], by factorisation of the co-occurrence matrix [START_REF] Landauer | A solution to plato's problem: The latent semantic analysis theory of the acquisition, induction, and representation of knowledge[END_REF][START_REF] Pennington | Glove: Global vectors for word representation[END_REF], etc. Sub-lexical units (character n-grams) address linguistic variability, e.g., due to rich morphology, non-standard text, and out-of-vocabulary forms [START_REF] Bojanowski | Enriching word vectors with subword information[END_REF]. Most of the models prior to 2018 are static, assuming a single vector per word. These models suffer from meaning conflation, i.e., a single vector is created for ambiguous units, ignoring polysemous and multi-facet words.

Advances in neural networks triggered the development of contextual embeddings, with representations conditioned on the surrounding words. They can be obtained using stacked recurrent layers as in ELMo [START_REF] Peters | Deep contextualized word representations[END_REF], or attention-based transformers as in BERT [START_REF] Devlin | Bert: Pre-training of deep bidirectional transformers for language understanding[END_REF] and GPT-2 [START_REF] Radford | Improving language under-standing by generative pre-training[END_REF]. In addition to their outstanding performances, these models address meaning conflation: contexts correspond to (slightly) different senses and are modelled with a custom embeddings. On the downside, they are computationally heavy and opaque [START_REF] Rogers | A primer in bertology: What we know about how bert works[END_REF], requiring sophisticated techniques such as probing to interpret predictions [START_REF] Jawahar | What does BERT learn about the structure of language[END_REF].

Particularly relevant to our work are sense embeddings [START_REF] Camacho-Collados | From word to sense embeddings: A survey on vector representations of meaning[END_REF], in which a lexical unit is associated to several vectors (as in contextual models), but with some generalisation across occurrences (as in static models). Unsupervised sense (multi-prototype) embeddings can be obtained by adapting the objective of the learning procedure [START_REF] Neelakantan | Efficient non-parametric estimation of multiple embeddings per word in vector space[END_REF], or with word sense induction methods based on clustering, e.g., [START_REF] Panchenko | Unsupervised does not mean uninterpretable: The case for word sense induction and disambiguation[END_REF]. For interpretability, resources such as Wordnet can be used to semantically enhance static embeddings [START_REF] Faruqui | Retrofitting word vectors to semantic lexicons[END_REF] or to learn representations for Wordnet synsets [START_REF] Rothe | AutoExtend: Extending word embeddings to embeddings for synsets and lexemes[END_REF], supersenses [START_REF] Flekova | Supersense embeddings: A unified model for supersense interpretation, prediction, and utilization[END_REF], or Babelnet senses [START_REF] Camacho-Collados | Nasari: Integrating explicit knowledge and corpus statistics for a multilingual representation of concepts and entities[END_REF]. Contextual models such as BERT can be enriched with supersenses, predicted jointly with masked words during training, with observed improvements in tasks requiring lexical semantics (?).

Interpretable semantic representations One of the most popular sense inventories in NLP is Wordnet [START_REF] Miller | Wordnet: An online lexical database[END_REF], in which words are grouped into synsets and linked to each other via lexical-semantic relations (e.g., hypernymy, synonymy). For many years, the English Wordnet has been the basis of sense-annotated corpora [START_REF] Landes | Building semantic concordances[END_REF] and WSD research [START_REF] Navigli | Word sense disambiguation: A survey[END_REF]. Babelnet [START_REF] Navigli | Babelnet: The automatic construction, evaluation and application of a wide-coverage multilingual semantic network[END_REF]) is a semi-automatic multilingual lexicon similar to Wordnet and also quite popular for performing WSD in languages other than English [START_REF] Moro | Entity Linking meets Word Sense Disambiguation: a Unified Approach[END_REF].

Supervised WSD relies on sense-annotated corpora specifying which of the senses in the inventory are employed in context [START_REF] Pasini | A short survey on sense-annotated corpora[END_REF]), e.g., SemCor for English Wordnet [START_REF] Landes | Building semantic concordances[END_REF] and Eurosense for Babelnet [START_REF] Delli Bovi | EuroSense: Automatic harvesting of multilingual sense annotations from parallel text[END_REF]. The fine granularity of sense inventories is often criticised as unrealistic [START_REF] Navigli | Word sense disambiguation: A survey[END_REF]. One alternative is to represent senses using top-level synsets in Wordnet's taxonomy (e.g., ANIMAL, EVENT), referred to as supersenses, reached via hypernymy relations [START_REF] Ciaramita | Supersense tagging of unknown nouns in wordnet[END_REF][START_REF] Schneider | Semeval-2016 task 10: Detecting minimal semantic units and their meanings (dimsum)[END_REF]. This reduces the number of labels at the expense of missing potentially relevant distinctions, often with positive impact on downstream applications such as dependency parsing [START_REF] Agirre | Improving dependency parsing with semantic classes[END_REF] and personality profiling [START_REF] Flekova | Personality profiling of fictional characters using sense-level links between lexical resources[END_REF]. 1 In our evaluation, we employ the FrSemCor corpus, a French corpus in which nouns are annotated using Wordnet supersenses as semantic tags [START_REF] Barque | Annotating a french corpus with supersenses[END_REF].

Although the set of 25 Wordnet top-level categories is quite popular, alternative representations with even coarser granularity can be useful for downstream applications [START_REF] Jahan | A new approach to animacy detection[END_REF], such as a threeway classification of adjectives [START_REF] Boleda | Regular polysemy: A distributional model[END_REF] or animate vs. inanimate nouns (Øvrelid, 2006). We understand supersenses as general coarse semantic distinctions. Our set of six semantic labels is related to Wordnet supersenses, but there is not a 1:1 relation between our supersenses and Wordnet's ones.

Weakly supervised semantic classification Many models have been proposed to induce lexical semantics from raw corpora without supervision, e.g., [START_REF] Lin | Automatic retrieval and clustering of similar words[END_REF], usually performing unsupervised WSD as a by-product. Most methods rely on distributional clustering algorithms, e.g., [START_REF] Biemann | Text: now in 2d! a framework for lexical expansion with contextual similarity[END_REF]. While automatically induced word senses are hard to interpret, they may be automatically labeled, for example, using hypernym-induction patterns [START_REF] Ustalov | Watset: Local-global graph clustering with applications in sense and frame induction[END_REF].

There have been several proposals to integrate interpretable representations such as supersenses with continuous (unsupervised) representations, but they often rely on annotated corpora, such as Semcor [START_REF] Flekova | Supersense embeddings: A unified model for supersense interpretation, prediction, and utilization[END_REF] or sense inventories such as Wordnet (?). Our embedding learning procedure is not fully unsupervised, but uses weak supervision to bootstrap semantic classes from corpora. Typical or non-ambiguous words can be used to produce sense-annotated data, which in turn enable training classifiers for inducing lexical knowledge. This has been proposed in several studies, e.g., [START_REF] Mihalcea | The role of non-ambiguous words in natural language disambiguation[END_REF], especially for polysemy pattern detection [START_REF] Boleda | Regular polysemy: A distributional model[END_REF], and adapted to semantic frame induction using predicate-argument pairs [START_REF] Kumar | Embedded semantic lexicon induction with joint global and local optimization[END_REF].

The method of [START_REF] Thelen | A bootstrapping method for learning semantic lexicons using extraction pattern contexts[END_REF] is similar to ours. They learn representations for six coarse supersenses using pattern-based bootstrapping based on a small list of seed words. The features used to learn senses are based on lexical patterns, syntactic co-occurrence, web queries, etc. [START_REF] Qadir | Ensemble-based semantic lexicon induction for semantic tagging[END_REF]. Instead of focusing on the features, our approach is more in line with current neural methods, with features learned from the data jointly with the supersense classifiers.

Contextual and Lexical Signatures

The heart of SLICE consists in a series of binary classifiers, one per supersense. Each classifier takes as input a context C and produces a score that indicates how likely C could be associated to a given supersense s i . This score, noted cs i (C), is called a context score. A context C can be associated to a d-dimensional vector, called its signature CS(C) = (cs 1 (C), . . . , cs d (C)) T , where d is the number of different supersenses. 2 The classifiers are also used to model the overall tendency of a word w to occur in contexts that are representative of a given supersense s i . This information is modeled by the lexical scores ls i (w), computed by aggregating the context scores of all occurrences of w in a large corpus. A word w is therefore associated to a d-dimensional vector, also called its signature3 LS(w) = (ls 1 (w), . . . , ls d (w)) T . Such vectors can be compared to word embeddings produced by deep learning methods. The difference, however, is that each dimension of a word signature corresponds to an interpretable supersense.

As stated above, SLICE relies on classifiers that themselves require, to be trained, supersenseannotated corpora. Such corpora, when they exist, usually are of limited size and do not allow to build reliable context and word signatures. This is why we propose a semi-supervised method not requiring annotated corpora, but a list of representative words for each supersense, easier and cheaper to constitute.

Outline of the Method

We use as a starting point d disjoint sets of non-ambiguous words representative of each supersense; these words are referred to as seeds. Seeds' occurrences are deterministically annotated with their corresponding supersenses in a corpus C, yielding a pseudo-annotated corpus that is used to train d classifiers, one per supersense. More precisely, the method is composed of the following steps:

1. Manually compile d disjoint sets S 1 . . . S d of
i (C) returns a score 0 ≤ cs i (C) ≤ 1, indicating how representative context C is of class s i .
This score is the context score mentioned above. Contexts that are representative of class s i will have scores close to 1.

5.

For each word w, extract from C all contexts C 1 . . . C n in which w occurs (contexts (W, k) such that w k = w) and predict scores cs 1 (C j ). . . cs d (C j ), 1 ≤ j ≤ n with P 1 . . . P d . For each supersense s i , all scores cs i (C j ), 1 ≤ j ≤ n are combined to form the lexical score ls i (w), which reflects the tendency of word w to appear in contexts representative of supersense s i . Finally, w is associated to a d-dimensional vector, its lexical signature, composed of the lexical scores ls 1 (w) . . . ls d (w).

The preceding description outlines the main steps of our method, but leaves unspecified two important aspects: the nature of the classifiers P i used to compute context scores cs i (c), and the way context scores are aggregated into lexical scores ls i (w). They are discussed in the two following sections.

Context Scores

For each supersense s i , context scores are computed by a binary classifier P i trained to predict the classes 0 or 1 of the positive and negative seed occurrences w k in the pseudo-annotated corpus C i . The classifiers are trained on a variant of a masked language modelling task, trying to predict the pseudo-annotated supersense of the masked word based on its context. In other words, we expect them to discriminate between contexts that are representative of a given supersense (1) vs. context that are irrelevant (0).

In practice, the input of P i is a context C = (W, k). Each word w j ∈ W is represented as a triple (f, l, m) where f is the surface form of the word, l its lemma and m its morphological features (e.g., number=plural). 6 Each element of this triple is represented as a randomly initialised embedding of size 500 for f and l and 64 for m.

The classifiers are made of two LSTMs: a left LSTM that processes the sentence from the first word w 1 to w k-1 , and a right LSTM that processes the sentence backwards, from the last word w |W | to w k+1 . The hidden-state vector size of both LSTMs is equal to 300. Notice that the LSTMs ignore the pseudoannotated word w k . The final states of the two LSTMs are concatenated, along with the morphological features of word w k , represented as an embedding of size 64. The resulting 664-dimensional vector is fed to a multilayer perceptron (MLP) with one hidden dense layer of size 150. The output layer is of dimension 2 corresponding to classes 0 (w k ∈ S - i ) and 1 to predict (w k ∈ S i ), with softmax activation. The LSTMs and the subsequent dense layer form a single network trained jointly. The loss function used to train each P i is categorical cross entropy, and the optimiser is Adam. We use a dropout of 30% to prevent overfitting, that is, for each prediction, each lemma and form in the input have a 30% probability of being masked. The size of the batches is equal to 128, and every 30,000 examples, the accuracy on the development corpus is computed. If this accuracy is the best up to now, the model is saved, and if it does not increase for the next 10 steps of 30,000 examples, training is stopped and the best model is kept.

Lexical Scores

Lexical scores ls i (w) reflect the tendency of word w to appear in contexts representative of class s i . It is a function of the contextual scores cs i (C 1 ) . . . cs i (C n ), where C 1 . . . C n are all the contexts in which w occurs in the corpus C. A context C is representative of class s i if its score cs i (C) is close to 1 and is not representative of class s i when cs i (C) is close to 0. Intermediate scores, close to 0.5, are less informative, so their contribution to the lexical score should be lower than that of representative scores.

We use the parabolic function h(a) = (1 -2a) 2p to model this behaviour. It reaches its minimum value 0 in the range [0 . . . 1] for s = 0.5 and its maximum value 1 for s = 1 and s = 0. Parameter p controls the extent to which intermediate scores are taken into account, the higher the value of p, the less intermediate values contribute to the lexical score (in our experiments, we arbitrarily set p = 8 upon observation of the distribution of the predicted context scores). The lexical score ls i (w) is defined as the average of its context scores cs i (C , j) weighted by h(cs i (C j )):

ls i (w) = 1 n j=1 h(cs i (c j )) n j=1 h(cs i (c j )) × cs i (c j )

Experimental Setup

We describe in this section the data we have used to build contextual and lexical signatures and the data we will use in the following section to evaluate our method.

Supersense Tagset To guarantee a sufficient scope (with respect to the size of the seed lists) and to simplify analysis, we grouped Wordnet supersenses into six coarser categories for our experiments on French nouns (details in Appendix A). Animate entity (ANI) includes nouns referring to living and animate entities, namely persons (e.g., agriculteur 'farmer') and animals (e.g., chiot 'puppy'); Natural object (NAT) is for nouns referring to natural entities (e.g., étoile 'star'), plants (e.g., peuplier 'poplar tree') and body parts (e.g., hanche 'hip'); Manufactured object (MAN) is composed of nouns denoting human-made or transformed entities: artifacts (e.g., chronomètre 'stopwatch') or built places (e.g., isoloir 'voting booth'); Informational object (INF) includes nouns denoting abstract objects having informational contents (e.g., théorie 'theory'), those referring to knowledge areas (e.g., ethnologie 'ethnology') and financial assets (e.g., budget 'budget'); Dynamic situation (DYN) gathers nouns denoting things that happen or that are carried out, like actions (e.g., ablation 'removal'), activities (e.g., cyclisme 'cycling') and events (e.g., explosion 'outbreak'); Stative situation (STA) is for nouns that denote properties (e.g., dignité 'dignity'), states (e.g., endettement 'easement') and feelings (e.g., tendresse 'tenderness').

Seeds We used data provided by the Wolf, a French lexical resource automatically built from the Princeton Wordnet [START_REF] Sagot | Building a free French wordnet from multilingual resources[END_REF], to draw up the six seed lists S i . A list of monosemous French nouns has been extracted from this resource and then we manually selected 200 nouns for each coarser category described above. For example, the seed list for the DYN class, contains nouns manually selected from the Wolf nouns having only one supersense among those that denote dynamic situations. Selecting monosemous seeds for pseudo-annotation of the corpus can bias the classifiers, which never encounters polysemous words at training time, but only in test data. However, this should not be a problem, as the we learn to classify contexts, not words. That is, the absence of polysemous words among the seeds should not be problematic, assuming that most polysemous words in context are disambiguated.

Corpus and Preprocessing

Experiments have been conducted on the frWaC corpus, which contains about 1.6 billion words crawled from the web [START_REF] Baroni | The wacky wide web: a collection of very large linguistically processed web-crawled corpora[END_REF]. The corpus has been POS tagged, lemmatised and morphologically analysed by an in-house parser trained on the French corpora of Universal Dependencies [START_REF] Nivre | Universal dependencies v1: A multilingual treebank collection[END_REF]. The corpus is divided into 55 parts of about 1M sentences each. Part 54 is used as development corpus for early stopping, all other parts are used for training.

Positive and negative seed sets S i and S - i are split into a training set (80% of the lemmas) and a development (20% of the lemmas). The training seeds are used to annotate the training corpus, while the development seeds are used to annotate the development corpus. This is a deterministic process: each occurrence of a word in S i (resp. S - i ) is annotated as 1 (resp. 0). We artificially balance the number of training contexts in each corpus C i to avoid biases related to different distributions of positive and negative examples. Given the seed lists S i and S - i , we count the total number of occurrences of lemmas from each list, N i and N - i in C. If N i < N - i , all sentences containing a lemma from S i are added to C i . Then, sentences containing lemmas from S - i are randomly added until at least N i occurrences from S - i appear in C i . If N - i < N i the seed lists are inverted. All other sentences are discarded. Evaluation data The FrSemCor corpus was used for evaluation [START_REF] Barque | Annotating a french corpus with supersenses[END_REF]. 7 It contains manual annotations for more than 12,000 nouns in the Sequoia Treebank, a corpus of 3,009 sentences from different sources including morphological and syntactic annotations [START_REF] Candito | Le corpus Sequoia : annotation syntaxique et exploitation pour l'adaptation d'analyseur par pont lexical[END_REF]. Noun tokens have been annotated with 24 supersenses adapted from the Wordnet supersense tagset. 8 For this experiment, we used 7,188 annotated nouns: 5,160 have been used for training, 1,015 for development and 1,013 for evaluation.

Supersense Tagging

We have evaluated SLICE on a supersense tagging task because our model produces interpretable senses that can be directly compared to senses used in semantically annotated corpora (FrSemCor, in our case). Our model produces, for every word in context, a description of the context through the context signature, and a description of the word usage through its lexical signature. Comparing different ways to combine these two pieces of information is interesting from a linguistic point of view since it can lead to interesting analyses of complex linguistic phenomena such as polysemy, multi-facet nouns, and unusual contexts (e.g., manufactured objects MAN in contexts typical of animate beings ANI).

As a comparison point for the performances reached by SLICE, we have used a simple baseline, which selects for every noun occurrence its most frequent supersense (MFS) in the training corpus. When the word does not occur in the training corpus, the most frequent supersense across all words is selected. This crude method gives better results as the training corpus grows, since the coverage grows with size of the training corpus and selecting the most frequent supersense is a good heuristic [START_REF] Navigli | Word sense disambiguation: A survey[END_REF].

We also compare our model to a state-of-the-art model in other WSD tasks: a French-specific version of BERT called FlauBERT [START_REF] Le | FlauBERT: Unsupervised language model pretraining for French[END_REF]. We use the 1024-dimensional embeddings available in FlauBERT-large as part of the HuggingFace library.9 For each target noun, we obtain its contextualised embedding from the top layer and provide it to an MLP identical to the one described in Section 5.2. Tokenisation incompatibilities due to BPE encoding are rare (e.g., 50/1,013= occurrences in the test corpus); they are resolved by taking the noun's last subtoken before the word separator as its embedding.

In our model, the decision to tag word w in context C with a given supersense is taken based on the lexical signature of w and the context signature of C.10 They are combined to yield a word-in-context signature Ψ(LS(w), CS(C)), which is also d-dimensional. The component corresponding to the highest score is selected as the predicted supersense for w in C: The main missing part in this model is the nature of function Ψ that combines lexical and contextual signatures. We discuss in the two following sections two instantiations of function Ψ.

ŝ(w, C) = argmax 1≤i≤d Ψ i (LS(w), CS(C)) α 0.0 0.

Linear Model

The linear model (LM) simply performs a linear combination of vectors LS(w) and CS(C):

Ψ(LS(w), CS(C)) = αLS(w) + (1 -α)CS(C)
. This model has one parameter: α, which value has to be estimated on the training corpus. The accuracy on the training set for different values of α has been represented in Table 1. We observe that, when only the lexical score is taken into account (α = 1), the model achieves an accuracy of 64.3%. It is equal to 51.83% when the decision is based on the only account of the context score. The optimal value is α = 0.7, that reaches an accuracy of 66.35% on the training set and an accuracy of 65% on the test set.

In order to get a better understanding of the results obtained by the linear model, we have grouped the noun occurrences into 5 configurations, described in Table 2 and calculated the accuracy of the linear model for each of them. The configurations compare for each noun occurrence, the correct supersense (column Ref), the best lexical scoring supersense (column Lex) and the best contextual scoring supersense (column Cont). In configuration AAA, all three candidates are equal (Ref=Lex=Cont). In configuration AAB, Lex is correct and Cont is wrong while in configuration ABA, Lex is wrong and Cont is correct. In configuration ABC both Lex and Cont are wrong but they are different, while in configuration ABB they are both wrong and equal to each other. Column 5 reports the number of occurrences that fall in each category. Column 6 gives the ratio of each configuration and column 7 shows the accuracy of LM for every configuration.

The table reveals that in 25% of the cases (configurations ABC and ABB), both L and C are wrong and the linear model behaves very poorly. This was expected, since the model just makes a linear combination of the lexical and contextual scores. The model also behaves poorly in configuration ABA, where Cont should be selected. This is due to the high value of α that tends to favour lexical scores over contextual ones. Linearly combining lexical and contextual signatures with a fixed weight is clearly not an adequate model.

Multilayer Perceptron

In the MLP model, Ψ is a complex non linear function learned by a neural network that combines the 12 scores that constitute the lexical and contextual signatures. The model chosen is a simple MLP with two hidden layers. Its parameters are learned on the training part of FrSemCor by minimising the categorical cross entropy between the six supersenses. The MLP model achieves an accuracy of 83.02% on the test corpus, an increase of 18.02% absolute with respect to the linear model. The behaviour of the MLP model in the 5 configurations is indicated in the last column of Table 2. As one can see, the predictions 0.5 1 1.5 2 2.5 3 3.5 4 4. made in configurations ABC and ABB are much more satisfactory. Accuracy jumps from 10% to 65% in configuration ABC and from 0% to 65% in configuration ABB.

Figure 1 shows the learning curve of SLICE+MLP, the most frequent supersense baseline (MFS), and FlauBERT models. With 300 words in the training set, the MLP model reaches an accuracy of 70%, while the MFS model reaches 31.5%. The difference between the two models decreases as the size of the training set increases. The MFS model accuracy exceeds the MLP's when the size of the training data reaches approximately 4,000 words. FlauBERT is the best performing method after 600 words in the training set, reaching a maximum accuracy of 89.8% on the full training corpus. Notice, however, that FlauBERT embeddings are 85 times larger than ours and were trained on a corpus about 6 times larger than ours. Moreover, the analyses presented in Section 5.3 are only possible in our model, thanks to its interpretability.

Table 3 gives a more detailed view on the MLP predictions. The table on the left hand side displays the precision, recall and F-measure for each supersense. It shows that the model behaves very differently on the different supersenses: supersense ANI obtains the best result, with an F-score of 94.59%, while STA behaves poorly and reaches an F-score of 64.86%, mainly due to its low precision. The confusion matrix on the right hand side reveals that STA is mostly confused with DYN, a confusion partly due to nouns pertaining to both categories, such as déshydratation 'dehydration', reconnaissance 'recognition/gratitude' or grossesse 'pregnancy'.

Error Analysis

A manual analysis of the results revealed that one key source of errors are nouns having multiple meanings, be they polysemous or multi-facet nouns11 . They account for 43.4% of the lemmas involved in errors. As a reminder, polysemous nouns have distinct and mutually exclusive meanings. For example, organisme 'body/organisation' can denote a natural object (NAT) or an institution (ANI) but a single occurrence of this noun cannot denote both. Multi-facet nouns, on the other hand, have multiple but compatible meanings, a property that can be highlighted by copredication [START_REF] Cruse | Aspects of the micro-structure of word meaning[END_REF][START_REF] Ježek | Nominals, polysemy and co-predication[END_REF]. For instance, demande 'request' denotes both the request (DYN) and the subject of the request (INF). In some contexts, both facets are triggered, such as La demande effectuée par la présidente n'a pas été acceptée. 'The request from the president was not granted'. Table 4 shows five cases of errors involving multiple meaning words and details their lexical and contextual scores 12 . In row 1, an occurrence of the polysemous noun organisme 'body/organisation' is incorrectly labelled ANI instead of NAT. An analysis of the scores reveals that although the context gives a clear preference for the correct sense (NAT), its lexical score is extremely low, while the score of sense ANI is high, provoking the selection of the incorrect sense.

The lexical signature of the word demande 'request', in row 2, clearly reflects its multi facet nature, both facets (INF and DYN) obtain the best and second best scores. However, contrary to annotators, the model considered the context as more representative of the INF class.

Another source of errors concerns questionable annotations in the gold data, where decisions made on class delimitation can be debated. For instance, verger 'orchard' or potager 'vegetable garden' refer to natural objects, but because they are also human creations, they have been classified as MAN in the reference. Our model, however, votes for the NAT class, relying both on contextual and lexical cues. It is interesting to note that the human-made aspect of this natural object seems to be captured in the lexical signature (second best score for MAN).

Gold annotation can also be questioned for nouns that are hard to classify. Notable among those are general nouns such as fait 'fact' or cas 'case', that can be used to characterise multiple referents and do not clearly pertain to one of the considered supersenses. The two occurrences of cas, rows 4 and 5, illustrate these noun properties: the best lexical score is rather low (0.68 for INF) and the gold supersenses, determined by the reference-driven annotation method, are not clearly captured in the contextual signatures.

Linguistic phenomena responsible for the association of several meanings to a single lexical form are thus numerous: homonymy, polysemy, facets, general units having heterogeneous referents. Our interpretable embeddings allow to observe these phenomena and investigate whether these different types of ambiguity or indeterminacy appear as structural properties of our embeddings. They also allow us to take a critical look at the linguistic data we used to learn them, namely the composition of seed lists with respect to the target semantic class, the corpus used to learn lexical signatures or the method used to compute lexical scores. For example, knowing that our model does not classify organisme as NAT, surely because the word is not detected as pertaining to this class in the lexicon, leads us to the following assumptions: nouns related to the body domain may not be well represented in the seed list for NAT; or the body meaning of organisme is not frequent enough in frWac, at least in contexts discriminant for the NAT class; or the method we used to compute lexical scores does not properly take into account differences between balanced vs. biased meanings for a given noun. In other words, our model of lexical representation opens the way to several linguistic studies that could allow the prioritisation of ambiguities (e.g., a confusion between the meaning of a polysemous word is more problematic than a confusion between facets of a multi-facet word), and hopefully help supersense tagging and WSD.

Conclusions

We have presented a method to learn interpretable embeddings using as weak supervision a list of seed nouns for each supersense. We use the occurrences of seed (prototypical) nouns to train a classifier which associates contexts to supersenses. The context scores are aggregated to generate a single lexical score per supersense. Each of these scores are seen as an interpretable dimension of a dense word embedding.

We have evaluated our method on a supersense tagging task to predict in-context coarse supersenses. In addition to a good performance with very little training data, our method's interpretability allows us to analyse the results in terms of the (supersense) dimensions of the input embeddings. Moreover, our model is considerably faster and lighter than state-of-the-art contextualised embeddings, e.g., we represent inputs as a set of 12 scores whereas FlauBERT uses 1024-dimensional opaque vectors.

We have also built and released the lexicon containing the 10K most frequent French nouns of the frWaC and their corresponding embeddings. We hope that this resource can be complementary to existing embeddings and lexical semantic resources. The lexicon, along with the seed lists, predictions and evaluation data are freely available. 13We have applied our method to nouns only, and our embeddings are 6-dimensional (one per coarse supersense), certainly lacking expressive power to cover the full range of semantic distinctions. In theory, there is nothing that prevents us from increasing the number of dimensions (e.g., to cover the traditional Wordnet supersenses), and to experiment with other parts of speech (e.g., verbs and adjectives). In practice, the list of seeds for some supersenses may be too small (e.g., TIME), and we lack annotated corpora to evaluate the method for other POS in French. The sensitivity of the method to the number of seed elements needs to be studied in more detail in the future. Another issue that remains open is the integration of embeddings with different POS tags: should we build a different model per POS (with different interpretable dimensions) or one single models in which inapplicable dimensions are empty?

As future extensions, we envisage integrating our embeddings in other downstream tasks such as semantic parsing. We would like to generalise our method to other syntactic and semantic categories, e.g., can we build interpretable embeddings in which each dimensions represents a given POS using seed lists of verbs, nouns, etc.? Transformer models are a promising alternative to recurrent neural networks to focus on relevant contexts for the classifiers.

Figure 1 :

 1 Figure 1: Learning curves for FlauBERT (green), SLICE with the MLP (red), and MFS (blue).

  positive examples, each containing lemmas that are prototypical of supersenses s 1 . . . s d . 4 2. Automatically compile d sets S - 1 . . . S - d of negative examples, each containing lemmas that do not pertain to supersenses s 1 . . . s d . The set S - i is built by selecting lemmas randomly from ∪ j =i S j . 3. For each supersense s i , locate in a non annotated corpus C all occurrences of the words whose lemmas are elements of S i or S - i . Words that come from S i are labelled 1 and those from S - i are labelled 0. As a result, d pseudo-annotated corpora C 1 . . . C d are produced. 5 4. Train d classifiers P 1 . . . P d respectively on C 1 . . . C d . The classifier P i takes as input a context C =

(W, k)

, where W = w 1 . . . w |W | is a sentence and k is the position corresponding to the pseudoannotated word. P

Table 1 :

 1 Accuracy of the linear model for different values of α on the training set.

	1	0.2	0.3		0.4	0.5	0.6	0.7	0.8	0.9	1.0
	acc. 51.83 54.99 58.05 61.01 62.59 64.46 65.55 66.35 66.34 65.84 64.36
		config. Ref Lex Cont occ ratio LM MLP
		AAA	A	A	A	424 0.42 1.00 0.96
		AAB	A	A	B	228 0.23 0.84 0.86
		ABA	A	B	A	101 0.10 0.41 0.67
		ABC	A	B	C	155 0.15 0.10 0.65
		ABB	A	B	B	105 0.10 0.00 0.65

Table 2 :

 2 Grouping word occurrences of the test set in 5 configurations. Column Ref shows the correct supersense for a word occurrence, column Lex shows its best lexical scoring supersense and column Cont, its best contextual scoring supersense. Column LM reports the accuracy of the Linear Model (with α equals to 0.7) and column MLP, the accuracy of the MultiLayer Perceptron.

  ).

	Supersense Rec.	Prec. F		ANI NAT MAN INF	DYN STA Σ
	ANI	94.02 95.16 94.59	ANI	236 1	2	2	7	3	251
	NAT	72.73 86.49 79.01	NAT	2	32	1	0	3	6	44
	MAN	75.00 83.61 79.07	MAN 2	2	51	5	7	1	68
	INF	79.07 76.84 77.94	INF	3	0	2	136 17	14	172
	DYN	88.82 81.84 85.19	DYN	2	2	3	18	302 13	340
	STA	60.87 69.42 64.86	STA	3	0	2	16	33	84	138
	Macro-avg. 78.42 82.23 80.11	Σ	248 37	61	177 369 121 1013
	Table 3: On the left, MLP's precision (Prec.), recall (Rec.) and F-measure (F) per supersense. On the
	right, supersenses confusion matrix, where reference supersenses index rows and predicted ones index
	columns.

Table 4 :

 4 Contextual and lexical signatures for noun tokens in the test set, where the reference class (4th column) is not the one predicted by the MLP model (5th column).

			Contextual signature				Lexical signature		Ref	MLP
		ANI	NAT	MAN	INF	DYN	STA	ANI	NAT	MAN	INF	DYN	STA
	organisme 0.22 0.87 0.19	0.15 0.17 0.09 0.92 0.04 0.04	0.29 0.05 0.01 NAT	ANI
	demande	0.16 0.04 0.04	0.46 0.11 0.04 0.02 0.01 0.07	0.91 0.60 0.44 DYN	INF
	verger	0.19 0.90 0.16	0.01 0.58 0.08 0.29 0.87 0.61	0.02 0.13 0.02 MAN NAT
	cas (1)	0.01 0.84 0.87	0.26 0.12 0.42 0.09 0.08 0.41	0.68 0.23 0.27 STA	MAN
	cas (2)	0.58 0.11 0.34	0.74 0.57 0.03 0.09 0.08 0.41	0.68 0.23 0.27 DYN	INF

A comprehensive list of downstream applications for supersense tagging can be found in[START_REF] Flekova | Supersense embeddings: A unified model for supersense interpretation, prediction, and utilization[END_REF].

In our case, d = 6, but the method can be applied to any number of semantic categories d ≥ 2.

If necessary, we use the terms lexical signature and context signature to distinguish between these objects.

Avoiding polysemous seeds is crucial to minimise the number of (inevitable) errors in automatic annotation.

Sentences not containing any word in Si∪S - i are discarded.

6 Morphological features are represented as one-hot vectors whose positions correspond to lists of key=value pairs.

https://frsemcor.github.io/FrSemCor/

The Wordnet supersenses tagset, also known as Wordnet Unique Beginners[START_REF] Miller | Wordnet: An online lexical database[END_REF], is composed of 25 nominal supersenses. Small adjustments have been made for the annotation of French nouns[START_REF] Barque | Annotating a french corpus with supersenses[END_REF].

https://huggingface.co/

In practice, our experiments use a word's lemma signatures instead of surface forms.

In a word supersense disambiguation task, which consists in selecting the appropriate supersense of a word in a list of predefined supersenses (generally from the Princeton Wordnet), errors are always due to words having multiple meanings. As for supersense tagging, monosemous words can be misclassified, as well as polysemous ones, since word supersenses are not predefined.

https://pageperso.lis-lab.fr/carlos.ramisch/?page=downloads/slice

Acknowledgements

This work was funded by the French PARSEME-FR project (ANR-14-CERA-0001). 14 We thank Aline Villavicencio and Marco Idiart for their suggestions, as well as the anonymous reviewers.

B Analysed Sentences

• organisme (emea-fr-dev 00335)

En tant que peptide, la bivalirudine est logiquement catabolisée en ses acides aminés constitutifs, avec recyclage ultérieur des acides aminés dans le pool de l'organisme. 'As a peptide, bivalirudin is logically catabolized into its constituent amino acids, with subsequent recycling of the amino acids into the body's pool.'

• demande (frwiki 50.1000 00458)