dataesr/scientific-paragraphs-categorization huggingface dataset:
Data set collection and annotation Being aware of the challenges related to this dialect, we decided to create the first publicly available SA data set for Tunisian dialect. This data set is collected from Facebook users comments. Tunisian are among the most active Facebook Users in the Arab Region 3. In fact, Tunisia is the 8th Arabic country in terms of penetration rates of Tunisian Facebook users, and almost tied as 2nd in the region alongside the UAE (United Arab Emirates) on the percentage of most active users out of total users  (Salem, 2017). This corpus is collected from comments written on official pages of Tunisian radios and TV channels namely Mosaique FM, JawhraFM, Shemes FM, HiwarElttounsi TV and Nessma TV during a period spanning January 2015 until June 2016. The collected corpus, called TSAC (Tunisian Sentiment Analysis Corpus), contains 17k user comments manually annotated to positive and negative polarities.
Training Data and features extraction Table  5  presents the training and evalaution sets. For each corpus we report the dialect, the number of comments per polarity (positive /negative) and the vocabulary size (|V |). We used 3 different training corpus, OCA (Opinion Corpus for Arabic), LABR (Large-scale Arabic Book Review) and TSAC. The OCA corpus contains 500 movie reviews in MSA, collected from forums and websites. It is divided into 250 positive and 250 negative reviews. In this work, we used a sentence level segmented version of OCA corpus described in  (Bayoudhi et al. , 2015)  4. The LABR corpus is freely available 5 and contains over 63k 4 Please contact Bayoudhi et al. to obtain a copy of the OCA sentence level segmented corpus 5 http://www. mohamedaly. info/datasets/ labr book reviews written in MSA and different Arabic dialects.
Classifiers In SA literature, the most widely used machine learning methods are Support Vector Machines (SVM) and Naive Bayes (NB). On top of these methods, we investigated MLP classifier. All the experiments were conducted in Python using Scikit Learn 6  for classification and gensim 7  for learning vector representation. The input of the final sentiment classifier is the set of features vectors from Doc2vec toolkit. The output is the sentiment class S ∈ {P ositive, N egative}.
SA experiments and evaluation To evaluate the performance of SA on the Tunisian dialect validation set, we carried out several experiments using various configuration. Seven experiments were carried out for each classifier depending on the training dataset: (1) using the Tunisian dialect training set, (2) using the MSA training set, (3) using the mixed MSA and Arabic dialects training set and (4 to 7) using different combination of these datasets. The performance of our different SA experiments are evaluated on the Tunisian dialect evaluation set and results are reported using precision and recall measures. Precision and recall are defined to express respectively the exactness and the sensitivity of the classifiers.
Results and Discussion The results of the different classifiers with different experimental setups are presented in Table  6. As expected, the best classification performance of all the classifiers are obtained when the Tunisian dialect SA system is trained using (or including) the Tunisian dialect training set. We obtained an error rate of 0. 23 with SVM, 0. 22 with MLP and 0. 42 with BNB. As shown in table 6 SVM and MLP obtain similar results for all experimental setups. However, lower results are obtained with BNB classifier. We notice also no improvement when the SA systems are trained with additional training data from LABR and OCA. Overall, poorer results are obtained when SA systems are trained without the TSAC corpus. This is mainly due to : • The OCA and LABR data sets are limited to one domain (movies and books respectively), while the evaluation set is multi-domain.
This is what we propose in this paper. Contribution. The paper focus is on crosslanguage semantic textual similarity detection which is the main part (with source retrieval) in cross-language plagiarism detection. The evaluation dataset used  (Ferrero et al. , 2016)  allows us to run a large amount of experiments and analyses. To our knowledge, this is the first time that full potential of such a diverse dataset is used for benchmarking. So, the paper main contribution is a systematic evaluation of cross-language similarity detection methods (using in plagiarism detection) on different languages, sizes and genres of texts through a reproducible evaluation protocol. Robust conclusions are derived on the best methods while deeply analyzing correlations across document styles and languages. Due to space limitations, we only provide a subset of our experiments in the paper while more result tables and correlation analyses are provided as supplementary material on a Web link 1.
Outline. After presenting the dataset used for our study in section 2, and reviewing the stateof-the-art methods of cross-language plagiarism detection that we evaluate in section 3, we describe the evaluation protocol employed in section 4. Then, section 5. 1 presents the correla-tion of the methods across language pairs, while section 5. 2 presents a detailed analysis on only English-French pair. Finally, section 6 concludes this work and gives a few perspectives.
Dataset The reference dataset used during our study is the new dataset 2  recently introduced by Ferrero et al. (2016). The dataset was specially designed for a rigorous evaluation of cross-language textual similarity detection. The different characteristics of the dataset are synthesized in Table  1 , while Table 2 presents the number of aligned units by subcorpus and by granularity. More precisely, the characteristics of the dataset are the following: • it is multilingual: it contains French, English and Spanish texts; • it proposes cross-language alignment information at different granularities: document level, sentence level and chunk level; • it is based on both parallel and comparable corpora (mix of Wikipedia, scientific conference papers, amazon product reviews, Europarl and JRC); • it contains both human and machine translated texts; • it contains different percentages of named entities; • part of it has been obfuscated (to make the cross-language similarity detection more complicated) while the rest remains without noise; • the documents were written and translated by multiple types of authors (from average to professionals); • it covers various fields.
Cross-Language Character N-Gram (CL-CnG) is based on  Mcnamee and Mayfield (2004)  model. We use the CL-C3G Potthast et al. (  2011 )'s implementation. Only spaces and alphanumeric characters are kept. Any other diacritic or symbol is deleted and the texts are lower-cased. The texts are then segmented into 3-grams (sequences of 3 contiguous characters) and transformed into tf. idf vectors of character 3-grams. The metric used to compare two vectors is the cosine similarity. Cross-Language Conceptual Thesaurus-based Similarity (CL-CTS) aims to measure the semantic similarity using abstract concepts from words in textual units. We reuse the idea of  Pataki (2012)  which, for each sentence, build a bag-ofwords by getting all the available translations of each word of the sentence. For that, we use a linked lexical resource called DBNary  (Sérasset, 2015).
It was reused by  Potthast et al. (2008)  in the context of cross-language document retrieval. Our implementation uses a part of Wikipedia, from which our test data was removed, to build the vector representations of the texts. Translation + Monolingual Analysis (T+MA) consists in translating suspect plagiarized text back into the same language of source text, in order to operate a monolingual comparison between them. We use the  Muhr et al. (2010) 's implementation which consists in replacing each word of one text by its most likely translations in the language of the other text, leading to a bags-of-words. We use DBNary  (Sérasset, 2015)  to get the translations. The metric used to compare two texts is a monolingual matching based on strict intersection of bags-of-words. More recently, SemEval-2016  (Agirre et al.
Detailed Analysis for English-French The previous sub-section has shown a consistent behavior of methods across language pairs (strongly consistent) and granularities (less strongly consistent). For this reason, we now propose a detailed analysis for different sub-corpora, for the English-French language pair -at chunk and sentence level -only. Providing these results for all language pairs and granularities would take too much space. Moreover, we also run those state-of-the-art methods on the dataset of the Spanish-English cross-lingual Semantic Textual Similarity task of SemEval-2016  (Agirre et al. , 2016)  and SemEval-2017  (Cer et al. , 2017) , and propose a shallower but equally rigorous analysis. However, all those results are also made available as supplementary material on our paper Web page.
Beyond their capacity to correctly predict a (mis)match, an interesting feature of the methods is their clustering capacity, i. e. their ability to correctly separate the positives (cross-lingual semantic textual similar units) and the negatives (textual units with different meaning) in order to minimize the doubts on the classification. To verify this phenomenon, we conducted another experience with a new protocol. We built a data subset by concatenating some documents of the previously presented dataset  (Ferrero et al. , 2016). More precisely we used 200 pairs of each sub-corpora at sentence level only. We compared 1000 English textual units to their corresponding unit in French, and to one other (not relevant) French unit. So, each English textual unit must strictly leads to one match and one mismatch, i.
Conclusion We conducted a deep investigation of crosslanguage plagiarism detection methods on a challenging dataset. Our results have shown a common behavior of methods across different language pairs. We revealed strong correlations across languages but also across text units considered. This means that when a method is more effective than another on a sufficiently large dataset, it is generally more effective in any other case. This also means that if a method is efficient on a particular language pair, it will be similarly efficient on another language pair as long as enough lexical resources are available for these languages. We also investigated the behavior of the methods through the different types of texts on a particular language pair: English-French. We revealed strong correlations across types of texts.
This means that a method could be optimized on a particular corpus and applied efficiently on another corpus. Finally, we have shown that methods behave differently in clustering match and mismatched units, even if they seem similar in performance. This opens new possibilities for their combination or fusion. More results supporting these facts are provided as supplementary material 6. represents the number of (mis)matches found for a given similarity score. In white, in the upper part of the figures, the positives (units that needed to be matched), and in black, in the lower part, the negatives (units that should not be matched). Cross-Language-Dataset/tree/master/study.
Therefore, males and females may assess each other using different signals and/or different aspects of the same signal. However, few studies have investigated whether the two sexes differ in the signals/signal aspects they use  (Hegyi et al. , 2015; Laczi, Török, Rosivall, & Hegyi, 2011). When studying coloration, the influence of different signals on mate choice is difficult to unravel because of multicollinearity among traits. In this study, we used a commonality analysis to identify potential multicollinearity among explanatory variables  (Ray-Mukherjee et al. , 2014). Exploring trait associations can help reveal any complexity hidden in mating patterns. Evidence for assortative mating is usually based on the value of the Pearson correlation coefficient for pairs of traits. Although it is straightforward to calculate, its use may be problematic when dealing with longitudinal data because of the risk of pseudoreplication.
, 2013; Parker et al. , 2011  for females). If ornaments influence mate choice differently for males and females, multicollinearity can occur among traits. Consequently, we anticipated that the final pattern might be more complex than that normally expected from "simple" assortative mating. Two previous studies on assortative mating in blue tits  (Andersson, Örnborg, & Andersson, 1998; Garcia-Navas, Ortego, & Jose Sanz, 2009)  focused on a single ornament, the blue crown, and obtained data from a single population (using 18 and 26 breeding pairs, respectively) over a single breeding season. They found opposite results:  Andersson et al. (1998)  found evidence for positive assortative mating, whereas Garcia-Navas and colleagues did not. Here, not only did we use two ornaments, but we also collected data from four different populations over 10 successive breeding seasons; we sampled a mean of 44 breeding pairs per year per population.
Our reflectance data (R) were relative to a white standard (WS-1; Ocean Optics, Dunedin, FL, USA) and a dark current (a black felt background). We measured each sample three times, the fiber being removed between each measure. For each ornament on each bird, we made measurements on two different samples and found the mean of six reflectance spectra obtained from two sets of three blue feathers and four yellow feathers  (Doutrelant et al. , 2008 (Doutrelant et al. , , 2012)). Samples within a year and a population were measured randomly, controlling a potential effect of random drift due to measuring male and female samples consecutively. We used Avicol v6 software  (Gomez, 2006)  to estimate the color trait values based on the shape of the spectra  (Andersson et al. , 1998; Doutrelant et al.
| Assortative mating database characteristics In all our analyses, we only used pairs for which coloration data was available for both the male and the female. Most of the time (97. 4%), feathers were collected from both members of a mated pair within 3 days. Within each population and breeding season, pairs that reproduced 30 days after their first attempt were excluded from the analysis because such clutches were potentially second or replacement clutches  (Nager & Vannoordwijk, 1995). Between 2005 and 2014, we analyzed color trait values for 1,657 mated pairs (Table  1 ), including 1,117 males and 1,110 females. The correlation (Pearson correlation coefficient) between trait values never exceeded 0. 42 (Appendix S2, Table  S2. 2), except in the case of blue hue and blue UVchroma, which ranged from -0.
| Commonality analysis We used commonality analysis to explore trait associations (i. e. , which predictors explain most of the variance in a given dependent variable) and to identify potential multicollinearity  (Ray-Mukherjee et al. , 2014). We used the five standardized color traits for one sex as predictors in additive multiple regression models in which the trait values for the other sex were dependent variables (Table  2 ). The commonality analysis was then performed on each of the 10 multiple regression models using the yhat package  (Nimon, Oswald, & Roberts, 2013)  in R (R Core Team 2016) to determine which variables should be retained in the analyses described below.
ACKNOWLEDGMENTS We wish to thank all the people who collected feathers in the field and who have participated in the blue tit long-term research program, in particular  P. Perret ,  M. Lambrechts ,  J. Blondel ,  D. Réale ,  D. Garant ,  M. Porlier ,  G. Dubuc Messier ,  C. de Franceschi ,  P. Giovannini , and our many field assistants over the years. We appreciate the efforts of the people who measured the color traits and compiled the color trait data set; special thanks are due to  E. Mourocq ,  J. Bisson , and  A. Midamegbe. We also thank two anonymous reviewers from previous submissions for insightful comments and  Jessica Pearce  for English language editing. Funding was provided by the  European Research Council  (AC: starting grant  ERC-2013-StG-337365-SHE ), the  French National Research Agency  (AC: grant  ANR-12-ADAP-0006-02-PEPS ,  CD :  ANR 09-JCJC-0050-0 ), the  Regional Government of Languedoc-Roussillon  ( Chercheur d'Avenir grant  to CD), and the  OSU-OREME.
Results Data selection and preprocessing. We have carried out a threedimensional (3D) joint seismic tomography study to image the physical properties of the crust, mantle wedge and subducting slab over a 350-km-long segment of the LASZ between Martinique and Montserrat (Fig. 1 ). This section was affected by the two largest thrust earthquakes  15  and is characterized by the most vigorous volcanic activity along this arc  17. We have assembled all available active source seismic data and local earthquake data collected in this region over the last two decades into one of the largest combined traveltime databases in any subduction setting. The active source seismic dataset includes data from several marine and amphibious experiments (EW9803 (ref. 18), Sismantilles 1 (ref. 19), TRAIL  20  , Sismantilles 2 (ref.
21), SEA-CALIPSO  22  ) carried out between 1998 and 2007. The complete active source traveltime dataset includes 110,807 airgun shots resulting in 461,648 P-wave traveltimes recorded on 348 seismic stations at offsets of up to 200 km. The network of stations includes ocean bottom seismometers in the forearc and land stations on the islands from Martinique to Montserrat. We also picked traveltimes on some of the permanent seismometer stations from the Montserrat Volcano Observatory (MVO) network and from the Geoscope station FDF on Martinique. Shots were decimated at a minimum inline shot spacing of 1,000 m to reduce computing load and redundancy, leading to a final selection of 8,097 shots and 71,568 P-wave traveltimes (Fig. 2a ). Picking uncertainties were estimated qualitatively, based on signal to noise ratio, source-receiver offset, and spatial continuity of arrivals.
Since the damped least squares approach used in the inversion does not allow for accurate modelling of the water layer and seabed topography we shifted the shot locations to the seabed and corrected the traveltimes assuming vertical propagation at seawater velocities. The local earthquake data were compiled from previously published studies  23, 24  , using data from the Sismantilles 1, Sismantilles 2, OBSAntilles  25  and OBSISMER  26  projects, and supplemented by traveltimes recorded by the MVO network. P and S phase picks were quality checked and the events were initially relocated in a 1D model using the linearized least squares 1D joint inversion code Velest 27. The resulting joint pick database contains 2,440 events in total, including 38,382 P phases and 27,092 S phases. Earthquakes with an insufficient number of picked phases (less than eight in total), large azimuthal gap (4200°), large RMS residual (41 s) were not used in the inversion.
70). Different crustal velocity/depth profiles were used for the arc, forearc and oceanic plate. Mantle velocities with a constant vertical gradient of 0. 005 s À 1 were added below a smooth Moho discontinuity constrained from receiver functions  71  and 2D wide-angle profiles  20. No slab was introduced in the starting model. The starting V P /V S model had a homogeneous value of 1. 76, determined from linear regression of T S vs. T P distribution (the Wadati diagram, see Supplementary Fig. 1 ). This starting pseudo-1D model (Fig. 3a ) was preferred to a true minimum 1D model as it represents already a 50% data variance reduction, improving stability and convergence. Inversion method. We used the code Simulps  28  , which employs approximate ray tracing and pseudo-bending to solve the forward problem  29  and a combination of parameter separation and damped least squares to tackle the inverse problem.
S-wave data were used to calculate S-P traveltimes, which were inverted jointly with P-wave traveltimes for the 3D V P and V P /V S structure as well as hypocenter parameters (coordinates and origin times) and station corrections. The inversion functional is designed to minimize traveltime residuals and a damping term through several iterations. At each iteration the forward and inverse problems are solved and the model and hypocenter parameters are updated. The inversion is stopped when the improvement in data variance is smaller than 1% of the initial data variance. The final RMS residual is 0. 26 s (0. 24 s for P-traveltimes, 0. 39 for S-P traveltimes) and represents a data variance reduction of 83% and 70% for P and P-S traveltimes, respectively, compared to the initial model (Supplementary Table  1 ; Supplementary Fig.
76 to 1. 82. Following the same workflow used in the checkerboard tests, we calculated synthetic traveltimes through this model and performed a full inversion on the 15x15 km grid with the synthetic data as input, to test how well the anomaly can be recovered. We find that the shape of the anomaly is well recovered, with an amplitude of 50-70% of the input anomaly, and that its termination depth can be correctly determined to within 10 km. We carried out a second test with a deeper anomaly reaching 120 km depth (Supplementary Fig. 8 ). In this case the termination depth cannot be determined with confidence. We also carried out checkerboard tests for the top of the slab. We added 5% anomalies to the top of the slab in our final model and followed the same procedure described above to calculate the synthetic data and run the inversion to recover the input anomaly.
Anisotropy. We do not account for anisotropic effects, which are recognized to cause artifacts in tomography models when not accounted for during inversion  74. The largest effect is expected for the sub-arc and back-arc mantle wedge and subslab asthenospheric mantle, where mantle flow causes alignment of olivine crystals. Recent constraints on anisotropy in the LASZ have been obtained from shear wave splitting measurements  75  , and indicate a relatively isotropic mantle wedge. We strive to minimize the impact of unaccounted anisotropy on our result by specifically concentrating our interpretation on the properties of the slab and shallow mantle wedge, and on the longer wavelength structures. Data availability. The traveltime database assembled as part of this study and coming from the SISMANTILLES 1 and 2, OBSAntilles, OBSISMER, TRAIL and SEA-CALIPSO experiments are available from the corresponding authors on request.
Acknowledgements This research was supported by an  AXA Research Fund Postdoctoral Fellowship  and the  OBSIVA  Project funded by the  Prix de la Fondation Simone et Cino del Duca of the French Academy of Science. This work has been initially funded by the  European Union   FP6 NEST (New and Emerging Science and Technology)-INSIGHT programme, under project  ' THALES WAS RIGHT ' no. 029080. We are grateful to the  Montserrat Volcano Observatory  for sharing their data and to the PIs, scientist, students and technical staff that designed and participated to the expeditions EW9803, SEA-CALIPSO, Sismantilles 1 and 2, TRAIL, OBSAntilles and OBSISMER, in particular to  Ernst Flueh ,  Alfred Hirn  and  Gail Christeson. We have benefited from the work of  Mario Ruiz  who extracted and initially relocated part of the local earthquakes database.