dataesr/scientific-paragraphs-categorization huggingface dataset:
This give us a bigger amount of samples for the dataset for training. For the constrained run we participate in two task: subjectivity and polarity classification. In the unconstrained run we only participate in polarity classification task. Table  1  shows the results of subjectivity classification Task (B4MSA method), here, Prec 0 is the P recision 0 value, Rec 0 is the Recall 0 value, FSc 0 is F -Score 0 value and Prec 1 , Rec 1 and FSc 1 the same for F -Score 1 values and FSc avg is the average value from all F-Scores. The explanation of evaluation measures can be seen in  (Barbieri et al. , 2016). Table  2 , shows the results on the polarity classification task. In this task our B4MSA method achieves an average F-Score of 0. 6054 and our combination of B4MSA + EvoDAG reaches an 0. 6075 of average F-Score.
Lample et al. (2016) propose a Named Entity Recognizer that obtains state-of-the-art performance in NER on the 4 CoNLL 2003 datasets 1 https://github. com/facebookresearch/fastText. git 2 https://storage. googleapis. com/google-code-archivesource/v2/code. google. com/word2vec/source-archive. zip.
the Irony Detection results are reported. Our systems do not perform well, as all the submitted systems reported a very low recall Irony Detection results for the ironic class: for example, the Unitor-U2 recall is only. 0013, while its precision is. 4286. It can be due mainly to two factors. First, the CNN devoted to the classification of the irony of a message has been trained with a dataset very skewed towards the not-ironic class: in the original dataset only 868 over 7409 messages are ironic. Second, a CNN observes local features (bi-grams, tri-grams,. ) without ever considering global constraints.
Acknowledgments We are thankful to the panelists and to the audience of the panel ' Raising Interest and Collecting Suggestions on the EVALITA Evaluation campaign ' at CLiC-it 2015, for the inspiring and passionate debate. We are also very grateful to  Malvina Nissim  and  Pierpaolo Basile , who accepted with us the challenge to rethink EVALITA and to co-organize the edition 2016 of the evaluation campaign.
Acknowledgments This work is supported by the project " Multilingual Entity Liking " co-funded by the  Apulia Region  under the program FutureInResearch, by the  ADAPT Centre for Digital Content Technology , which is funded under the  Science Foundation Ireland Research Centres Programme  (Grant  13/RC/2106 ) and is co-funded under the  European Regional Development Fund , and by  H2020   FREME  project (GA no. 644771 ).
Acknowledgments This work was supported by the  Deutsche Forschungsgemeinschaft (DFG)  under grant No. GRK 2167 ,  Research Training Group  " User-Centred Social Media ". Acknowledgements This work presented here under the research project Grant No. YSS/2015/000988  and supported by the  Department of Science & Technology (DST)  and  Science and Engineering Research Board  (SERB), Govt. of India. Authors are also acknowledges the  Department of Computer Science & Engineering of National Institute of Tech -Acknowledgments We thank the  CIT of the University of Groningen  for providing access to the  Peregrine HPC cluster. Barbara Plank acknowledges NVIDIA corporation  for support.
Computational rule-based model for Irony Detection in Italian Tweets datasets. The second and third columns Simona Frenda FICLIT -University of Bologna, Italy simona. frenda@gmail. com Abstract English. In the domain of Natural Language Processing (NLP), the interest in figurative language is enhanced, especially in the last few years, thanks to the amount of linguistic data provided by web and social networks. Figurative language provides a non-literary sense to the words, thus the utterances require several interpretations disclosing the play of signification. In order to individuate different meaning levels in case of ironic texts detec- tion, it is necessary a computational model appropriated to the complexity of rhetorical artifice. In this paper we describe our rule- based system of irony detection as it has been presented to the SENTIPOLC task of EVALITA 2016, where we ranked third on twelve participants.
We believe, the unexpectedly little performance gain from utilizing the much larger italian-UD data over the rather small didi-it + postwita data may be rooted in the insertion of Twitter-specific tags into the data (see 4. 1. 3), something we did not account for, i. e. 18, 213 of 289, 416 and more 9 https://creativecommons. org/licenses/ by-nc-sa/3. 0/ 10 -sample 1e-3 -iter 5 -alpha 0. 025 importantly 7, 778 of 12, 677 sentences had imperfect information during training.
RQ. 2: Approaches supporting language product line engineering After reading the articles obtained from the search protocol, we identified a set of approaches supporting (partially or completely) the language product line's life-cycle. Those approaches are listed in table  3. There is a clarification to point out in this table. There are two approaches i. e. , Neverlang+AiDE and ASF+SDF+FeatureHouse that are more than single approaches are the combination of several approaches. Whereas Neverlang and ASF+SDF are approaches for the construction of DSLs, AiDE and FeatureHouse are tools for variability management. In this literature review, we group those approaches since they have been used together to support language product line engineering. This decision facilitates the study of the approaches.
Neverlang is one of the approaches that use exogenous modularity to support extension of language modules. In Neverlang, a base module is specified in a BNF-like grammars that can be enhanced by extension modules introducing new production rules. Figure  9  illustrates this capability with the example of state machines and composite states. The base module is a simple DSL for state machines and the extension module introduces a new production rule for the construct State, thus introduction the notion of composite state. Note that there is a composition script (in the middle of the Figure  9 ) that indicates that a language called CompleteStateMachine is the result of the composition of the language modules called StateMachines (base module) and CompositeStates (extension module). • Endogenous modularity to support aggregation.
Hence, the language constructs used by the requiring language are used at the left of the production rules. • Exogenous modularity to support aggregation. Aggregation of language modules can be supported via exogenous modularity. In this case, the requiring module declares a set of language constructs that are supposed to be implemented in a providing language module. Consider the example introduced in Figure  11  that uses Neverlang in the same example of the state machines language and expressions. In that case, the production rule for the construct transition uses the non-terminal Expression. Since this non-terminal is not implemented in the language module, it represents a declaration that is intended to be fulfilled by another language module. At the bottom of the figure, we introduce an expression language which implements the required language constructs.
Concrete syntax Textual ! - ! ! - ! - - - ! ! ! ! Graphical - - - ! - - ! - - - - - - Semantics Static ! ! - ! - ! - - - - ! ! ! Operational - ! - ! - - - - - ! - ! - Denotational ! - ! - ! ! - ! - - ! - ! Axiomatic - - - - - - - - - - - - - Table  4 : Mapping current approaches with language modularization different semantics. In doing so, language designers would introduce specification clones (repeated segments of specification) all along the language product line, thus increasing maintenance costs. • Multi-dimensional variability with concern-specific features. The approach presented in  [93]  proposes to deal with variability management on top of Neverlang using the Common Variability Language. This approach considers not only abstract syntax variability, but also concrete syntax and semantic variability.
The approach to support variability management is based on feature models to represent multi-dimensional variability  [17]. In other words, this approach supports not only abstract syntax variability, but also concrete syntax and semantic variability. To support multi-dimensional variability, the authors propose an approach in which the variability model has (at the first level) one child feature of the root for each dimension of variability. Figure  16  illustrates this modeling approach. In that example, the sub-tree concrete syntax has two language features (i. e. , feature 1 and feature 2) that represent two different representations of a particular construct (or set of constructs). Each feature is implemented in a language module that The approach presented in  [16]  proposes the use of FeatureHouse as a languages variability management framework on top of ASD+SDF.
This approach supports modular languages design and language modules composition. To this end, MPS enables modularization of the metamodels and provides mechanisms to propagate such modularization at the level of the concrete syntax and semantics. • Modularization on top of ATL. Modularization on top of ATL supports the construction of DSLs where the abstract syntax is defined in metamodels and the semantics is defined denotationally through transformations. This approach supports modular languages design and language modules composition. More concretely, it introduces modularization on top of the ATL transformation language  [96]. • Modularization of top of MetaDepth. MetaDepth supports the construction of textual DSLs where the abstract syntax is defined in metamodels, static semantics is defined in constraints, and dynamic semantics is defined denotationally through transformations.
MetaDepth supports modular languages design and language modules composition. To this end, this approach is based on metamodels extensions, and structural concepts. • Gromp. Gromp supports the construction of graphical DSLs whose abstract syntax is defined in metamodels, and the concrete syntax is defined in Picture (a platform for the definition of graphical DSLs built on top of EMF). Gromp supports modular languages design and language modules composition. To this end, this approach provides a composition language that allows language designers to manually describe the composition of several language modules. • Domain-concepts based modularization. Domain-concepts based modularization supports the construction of graphical DSLs whose abstract syntax is defined in metamodels and dynamic semantics is defined denotationally through transformations.
• Components-based LR parsing. Components-based LR parsing is an approach that supports the modular definition of parsers defined through grammars. From all the articles reviewed in this literature review, this is the only one that uses tooling composition as composition strategy. The authors justify their decision by arguing that it favors low coupling in language modular design. Semantics are not addressed by the approach. • Roles-based modularization. Roles-based modularization supports the construction of textual DSLs where the abstract syntax is defined in metamodels, the concrete syntax in BNF-like grammars, and semantics is specified operationally in Java programs. The ideas proposed in this approach are implemented in the LanGems workbench  [82]. This approach supports modular languages design and language modules composition.
This approach provides support for modular languages design and languages composition. Additionally, we found several approaches that support variability management on top of Neverlang. • ASF+SDF+FeatureHouse. ASF+SDF+FeatureHouse supports the construction of textual DSLs where the abstract and concrete syntax are specified in BNF-like grammars, and semantics is specified denotationally through transformations. This approach is a tool chain composed of ASF, SDF, and FeatureHouse. ASF+SDF provides support for modular languages design and languages composition  [89, 90, 91]. In turn, the approach presented in  [16]  proposes the use of FeatureHouse as a languages variability management framework on top of ASD+SDF. As a summary of the discussion presented in this section, we introduce the mapping that relates each approach with the DSLs technological spaces it supports.
Acknowledgments This work is supported by the  ANR INS  Project  GEMOC  ( ANR-12-INSE-0011 ); the bilateral collaboration VaryMDE between Inria and Thales; the bilateral collaboration FPML between Inria and DGA; and the  European Union  within the  FP7 Marie Curie Initial Training Network RELATE"  under grant agreement number  264840. We thank the anonymous reviewers for their insightful comments, which helped us improve the manuscript.
125 ha area) equipped with insect screens (20/10) on the roof and sidewalls ventilation openings. Simulation conducted by using a commercial CFD package (CFD2000®/Storm), a realistic model based on energy, mass and momentum exchanges was considered. As the regime in greenhouse is turbulent, the turbulent transfers were described by a k-ε model. Likewise, the dynamic influences of insect screens and tomato crop on airflow movement were modeled by means of the concept of porous medium with the Boussinesq assumption. Atmospheric radiations contribution was included in the model by customising the roof cover temperature deducted from its energy balance by means of view factor law. Also, the CFD code was customized in order to simulate in each element of the crop cover the sensible and latent heat exchanges between tomato crop and greenhouse.
The secure integration of renewable generation into modern power systems requires an appropriate assessment of the security of the system in real-time. The uncertainty associated with renewable power makes it impossible to tackle this problem via a brute-force approach, i. e. it is not possible to run detailed online static or dynamic simulations for all possible security problems and realizations of load and renewable power. Intelligent approaches for online security assessment with forecast uncertainty modeling are being sought to better handle contingency events. This paper reports the platform developed within the iTesla project for online static and dynamic security assessment. This innovative and open-source computational platform is composed of several modules such as detailed static and dynamic simulation, machine learning, forecast uncertainty representation and optimization tools to not only filter contingencies but also to provide the best control actions to avoid possible unsecure situations.
More recently, researchers have proposed holistic frameworks  [13] -  [15]  for SA that combine offline and online applications for assessing different phenomena (e. g. steadystate violations, frequency stability, transient stability, voltage stability, small-signal stability) with uncertainty. These frameworks aim at bridging the gap between risk-based methods and traditional deterministic approaches, by introducing the concepts of probability and impact associated This research has received funding from the European Union 7 th Framework Programme under grant agreement nº 283012 (www. iteslaproject. eu) and by the ERDF -European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation -COMPETE 2020 Programme within project «POCI-01-0145-FEDER-006961», and by National Funds through the FCT -Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) as part of project «UID/EEA/50014/2013».
to the contingencies as well as the uncertainty of the energy consumption forecast and of the RES power production. The ultimate goal of these frameworks is to devise overall architectures for online SA integrating probabilistic and deterministic concepts in a unified environment. This rationale has triggered the developments of the iTesla project 1 which is co-funded by the European Commission Framework Program 7 (EC FP7). The project required the development of an online security analysis platform able (a) to cope with European-wide grid models, (b) to account for uncertainty and its impact on the implicit or explicit margins needed to ensure the wanted security level (c) to handle the curative remedial actions needed to reduce the margins that are necessary to face contingencies, and (d) to achieve both static and dynamic simulations in order to ensure the accurate evaluation of the security of the system when operated closer to its limits.
The central value of the fuzzy number corresponds to the output obtained from the AC PF. The FPF module is meant to be run in parallel to the mainstream of the online process. Hence, this module can support validation of the contingency filtering for static phenomena and shows the modularity of the iTesla platform to integrate new functionalities. The FPF module can be run for several contingency scenarios and computes a Security Index (SI) based on the physical limits of the variables (e. g. maximum and minimum voltage magnitude) as follows                       dx x f dx x f H L PB PB SI ( 1 ) where f is the membership function and PBL and PBH are, respectively, the lower and upper physical limits of the variable under analysis. Note that the SI belongs to the interval [0,1].
D. Computational Implementation A high-end software framework was developed to handle the heavy computations required by not-deterministic security analysis, especially the ones of the offline module in the case (not shown in the current article) where a dynamic simulator is used instead of a PF tool. The platform, whose core is in Java 2  , is modular so as to ease the distribution of computations, through a High Performance Computing (HPC) layer built on top of a Message Passing Interface (MPI), and the replacement of modules (for example, the replacement of one PF tool by another). It provides common services such as data management, data conversion, data mining, task management and graphical user interfaces. MongoDB was used to store injection and withdrawal chronicles extracted from grid models and to compute statistical indicators like quantiles for uncertainty analysis.
The stochastic variables comprise the active and reactive power injection of loads and RES totaling 3798 variables. The historical data used was Day-Ahead Congestion Forecast (DACF) and SN files, in CIM Profile 1 format, with persistent identifiers for equipment regardless of the time and type of file (DACF and SN). The iTesla platform detects unsecure situations due to overload by comparing the post-contingency steady-state current in all the lines and transformers with its operational limits in the CIM file: if at least one limit is violated, then the situation is considered unsecure.
A. Offline Computation of Security Rules 1) Methodology for DTs Parametrization/Evaluation The security rules obtained to detect overload problems in the French network were computed with the DATAmaestro® 3 software that was integrated in the iTesla platform. For each contingency/security index pair, this module computes DTs with alternative values for the training parameters, namely the alpha (α) and the true threshold (tT) parameters. The α value allows controlling the size of the tree, by defining the minimum statistical significance (i. e. 0/1 means never/always significant) required to split nodes. Usually the optimal value of α is quite independent of the learning set and of specific problems of the disturbance, such as type, location and size of the system  [24]. According to  [25] , the optimum value for α lies between [0.
2) Execution of the iTesla Platform The offline part of the iTesla platform was run by using the SN of January 2013 to sample stochastic variables and create realistic network states using optimization. Then, for each network state sampled, the post-contingency state was computed to produce a large data set of pre-analyzed states for the parametrization/selection of the best DTs. Finally, the DTs were applied to all the SN and DACF of February 2013 to assess the generalization capabilities of the DTs. The computations were run on 2 x Intel(R) Xeon(R) CPU E5-2670 0 @ 2. 60GHz processors (16 cores) and 128 GB of RAM.
3) Validation of the Sampling of Stochastic Variables The iTesla platform contains a tool that allows a visual comparison between the historical SN used for sampling and the sampled states, in the form of plots on relevant sets of the stochastic variables. Good quality sampling is obtained when these plots reveal a similar dependence structures between historical SN and the sampled states. Fig. 2  presents some of the plots obtained for aggregated stochastic variables. The variable in x-axis of this figure, var1, represents the summation of the first half of the stochastic variables (from 1 to 1899), whereas the variable in the y-axis, var2, comprises the summation of the second half of stochastic variables (from 1900 to 3798). Note that the left plot represents the historical data and the right plot was obtained for the sampled data.
For easier detection of outliers, these plots also present the convex hull of each dataset (in black for historical and in gray for sampled data). The following statistics are also provided to assist the visual comparison: Pearson's correlation (r); Kendall's tau rank correlation () and Spearman's rho (). These plots show that between historical and sampled data: (a) the stochastic aggregated variables occupy roughly the same area of the state space; (b) the dependence structure is largely the same. Hence, the results presented illustrate that the generated sampled stochastic variables seem consistent with the input historical data (SN).
4) Decision Tree Validation Process The DT structure obtained for one of the analyzed contingencies is presented in Fig. 3. (short-circuit in a transmission line leading to its disconnection). This DT was generated with a data set of 1384 pre-analyzed network states, comprising 66% of secure states for the analyzed contingency. The obtained k-fold cross validation statistics for this DT are presented in Fig. 4. From these statistics, the obtained DT misclassification errors seem being reduced to a controlled risk. Namely, the obtained k-fold cross validation errors comprise 1% of MA states and 2% of FA states, defining a probability of MA of 2. 1% and a DT filtering capability of 65% (very close to the 66% of secure states in the data set). The geographical visualization of the analyzed DT is presented in Fig.
2) Fuzzy Power Flow as Additional Uncertainty Representation To validate the FPF approach, the FPF module integrated in the iTesla platform was applied for the same security problem and DACFs used to test the MCLA (see the previous section). The analysis of the FPF results was carried out aiming to compare the fuzzy uncertainty representation with the one provided by the MCLA, namely: (a) to detect if the states sampled by the MCLA are inside or outside the uncertainty region defined by the FPF for the two most important variables in the DT; (b) and to compare the classification of the DACF according to the Security Index (SI) defined for the FPF results. The fuzzy injections used as inputs, i. e. the uncertainty around each injection represented as a fuzzy triangular number, was obtained by using the mean and standard deviation extracted from the uncertainty distribution computed offline by the forecast error module.