dataesr/scientific-paragraphs-categorization huggingface dataset:
89-82. 40% on test samples. Author further try hybrid methods (lexicon-and embedding-driven) that deliver slightly better results. Rotim and Šnajder (2017) use similar approach for Croatian corpora obtaining 0. 822 as F1 score for game reviews but the results are much worse for the Twitter dataset. In contrast to the authors, we do not train our Word2Vec model for the corpora in Slavic languages. Instead, we employ the pre-trained Google News Word2Vec model after translating texts to English. It makes our approach more universal and easier to apply to the foreign language corpora yielding satisfactory accuracy (see Results). Garten et al. (2018)  compute the cosine similarity between the aggregate vectorial representation of documents and the â ȂIJnegativeâ Ȃİ and â ȂIJpositiveâ Ȃİ dictionaries.
Precision on the IMBD English reviews data varies between 0. 70-0. 75. Our findings show that the introduction of the polarity dictionaries delivers less accurate outputs than using Sentence2Vec. However, our set-up does not foresee unsupervised learning. Feature-based approach for Czech language sentiment classification renders 0. 69 as F1 measure in  Habernal et al. (2013). The method has to be adjusted for other languages if used. Zhang et al. (2017)  report another approach for Twitter sentiment classification employing character-based Convolutional neural networks with different languages. The method transforms the characters in alphabetic order in UTF-8 codes facilitating sentence segmentation. The character embedding matrix is then used as an input for the convolutional neural network.
Methodology Recall from the previous sections that we tend to develop a sentiment analysis approach for multilanguage use. Fig. 1  depicts the proposed method. Word vectorial representation spurs the interest of many researchers in natural language processing due to its capacity to catch the meaning of terms depending on the context. Researchers and practitioners who use deep learning methods for sentiment analysis tend to learn the embedding from the available dataset or pre-trained word embedding models (i. e. , Word2Vec, Glove). Each document is then represented as the stack of vectors. Document padding unifies the number of vectors that serve as the network input. It means that the network deals with [batchsize*size of vectorial stack*number of features] input data dimensions which may be computationally costly.
Instead, our model exploits vectorial representation of the words with a transfer learning approach: Google Word2Vec pre-trained model serves as a source of 300-dimensional dense vectors for each word in the text. Then the model computes an elementwise sum of the vectors divided by the number of terms in the text. The use of Google Word2Vec model has several advantages over learning embeddings from the training data: (i) Google model has been pretrained with the corpora of the news containing circa 100 billion words where each term has been used more than once and in different contexts. It makes the model the state-of-the-art regarding the quality of vectors which plays a crucial role in our study as we use the translated text from Slavic languages to English; (ii) Google model comprises approximately 3 million words and phrases.
This vocabulary covers the lion share of lexicon employed by web-resources and social networks users; (iii) we do not need to construct a large dataset to train our model as the vectors have already been pre-trained with a significant number of terms. Google Translation. Machine translation does not always provide perfect accuracy from the linguistic point of view. However, the resulted translation with recently introduced Googleâ Ȃ Źs Neural Machine Translation approach tends to deliver English text contextually similar to the input document (see  Wu et al. (2016)  for more details).
Machine Learning Methods Used This subsection discusses the machine learning classifiers employed in a supervised learning approach to classify texts on positive or negative. The implementation details are stored in the Github repository. Support Vector Machines Classification. This approach belongs to the family of versatile machine learning methods with high accuracy on non-large datasets. It tries to find the broadest possible margin between positive and negative classes. As in  Giatsoglou et al. (2017)  we use linear Support Vector Classifier (SVC) and Gaussian Radial Basis Function (RBF) in our set-up. Random forest (RF) helps overcome the disadvantages of a single decision tree by summarizing and averaging predictions over the number of trees. It is an ensemble learning approach that uses the outputs of the individual predictors as votes.
Data Multilanguage Corpora. We use the corpora in Polish, Slovenian and Croatian in our experimental set-up. Polish, Slovenian and Croatian languages belong to the Indo-European family as well as English. However, they are members of the Slavic branch that makes these languages share less close ties with English than, for example, French, Spanish or Italian would have. We retrieve the dataset with texts and corresponding polarity scores for tweets in mentioned languages from the website of the European research infrastructure CLARIN. 3 Our dataset comprises 2794 tweets in Polish (1397 positive and 1397 negative), 4272 tweets in Slovenian (2312 positive and 1950 negative) and 3554 tweets in Croatian (2129 positive and 1425 negative). Tweets may contain emojis and/or URLs. We removed them together with digits and punctuation marks as a part of the data preprocessing step.
Results After mentioned datasets have been translated to English, each tweet is converted to the vector creating the averaged representation of the document words. Data is split into training/testing sets as 3 https://www. clarin. si/repository/xmlui/ 80/20 respecting the ratio of positive and negative observations. Table  1  presents the accuracy results for three datasets (Polish, Slovenian, Croatian). The overall accuracy of the best classifiers is more than 76% which may be seen as satisfactory taking into consideration state-of-the-art findings. The treebased methods usually deliver marginally better accuracy to the SVC and DNN classifiers. The accuracy of more than 78% is higher than the one reported in  Garten et al. (2018).
LGV Est is part of the rail network linking France with Germany, Luxembourg and Switzerland. This HSL has been in service since 2007, with French and German rail traffic passing at a commercial speed of 320 km/h. SNCF carried out various tests over 4 years to evaluate the GB impact on maintenance and observe the behavior during climate variations. Different sensors have been installed in both the innovative and conventional sections to measure the temperature, pressure, acceleration, stresses and deformations of the asphalt layer. The data retrieved from this first instrumented test indicated satisfactory performance of the experimental railway section  (Cardona et al. , 2016; Robinet and Cuccaroni, 2012). The presence of the asphalt sublayer has proven to reduce the aggressiveness of the loading applied by passing trains, leading to an increase of the structure durability.
Results are stored in a relational database, enabling fast-multi-criteria searches. A remote webserver authorizes the management of several instrumented sites along with their measurement systems, including the weather station. Acquisition parameters can be configured remotely, through a web application. For each acquisition board, the slow and fast measurements can be visualized in real time. Data acquisition carried out on BPL HSL is divided in two phases. The first phase, entitled "speed up phase", corresponds to a test phase of the track, before the start of commercial traffic. The passing HST characteristics are known, with increasing speeds ranging from 160 to 352 km/h. The HST is composed of 13 bogies and weighs 380 tons. Bogie axle loads are summarized in Figure  5. Their wheelbase is 3 m long.
Data processing during speed-up test phase Before opening the BPL track to commercial traffic, a series of tests were performed on the track, at increasing speeds, between November 2016 and January 2017. These tests were performed with the same HST, with known loads, and train speeds varying from 160 Km/h to 352 Km/h. Since the loads were always the same, responses obtained on different sections, and at different speeds, can be easily compared.
General description A train journey on the BPL railway track generates more than 50 data files that need to be treated and plotted. Scilab, a free numerically oriented programming language is used to automate signal processing. Different routines and functions have been developed, using Scilab, to complete the following steps, leading to visualization of signals: • Scale the measurements of the various sensors installed. • Calculate the speed of the passing HST train at this date. • Filter the recorded sensor signals using a low pass filter. • Select the desired sensor(s) to plot their signal(s). • Visualize the needed time signals. These steps have been programmed with Scilab for all the sections. The section(s) to study can be selected at the beginning of the data processing program. It is possible to run multiple calculation functions, read data files, scale, filter, calculate velocities, process and plot graphs.
On a micro level, the interactions within each scene are governed by one or more predefined dialogue scripts. These scripts correspond to the concepts of scene protocol in ISLANDER and interaction pattern in OPERA. Dialogue scripts are not detailed in Figure  4. The reason is that the intra-scene (micro level) dialogical specifications have distinct natures both in ISLANDER and in OPERA, as will be discussed in the sequel. In Figure  4 , the conceptual pattern identified in the dialogical specifications of ISLANDER and OPERA is captured by means of an Ecore metamodel. This metamodel is described in details in Tables  5  and 6.
In addition, assume that these applications are heterogeneous regarding the organizational technology applied to build them. To put it in more concrete terms, let us suppose two agents organizations: One built upon the S-MOISE+  [53]  organizational middleware, based on the MOISE+ model, and the other by using MADKIT  [54]  platform, based on ARG model. In this setup (and assuming a shared common agent communication language and domain ontology), the agent designers face the following problem: The native S-MOISE+ agents do not interoperate with the MADKIT platform, and vice-versa. Thus, it is not directly possible, for instance, to write an agent code that enter both e-business agent organizations in the search of products and/or services on behalf of its users. Such fact limits the range of applicability of S-MOISE+ and MADKIT agents which, in turn, limits the idea of open MAS.
Funding: "This research was partially funded by  FAPEMA  grant number  127/04  and  CAPES, Brazil , Grant  1511/06-8 ". Jaime Sichman  was partially supported by  CNPq  and  FAPESP ,  Brazil. Anarosa Alves Franco Brandão  was partially supported by grant # 010/20620-5  and # 014/03297-7 ,  São Paulo Research Foundation (FAPESP) ,  Brazil.
On the evaluation of relational models The SentEval framework  (Conneau et al. , 2017)  provides a general evaluation for transferable sentence representations, with open source evaluation code. One only needs to specify a sentence encoder function, and the framework performs classification tasks or relation prediction tasks using cross-validated logistic regression on embeddings or composed sentence embeddings. Tasks include sentiment analysis, entailment, textual similarity, textual relatedness, and paraphrase detection. These tasks are a rich way to train or evaluate sentence representations since in a triple (s 1 , R, s 2 ), we can see (R, s 2 ) as a label for s 1  (Baudiš et al. , 2016). Unfortunately, the relational tasks hard-code the composition function from equation 4. From our previous analysis, we believe this composition function favors the use of contextual/lexical similarity rather than high-level reasoning and can penalize representations based on more semantic aspects.
This bias could harm research since semantic representation is an important next step for sentence embedding. Training/evaluation datasets are also arguably flawed with respect to relational aspects since several recent studies  (Dasgupta et al. , 2018; Poliak et al. , 2018; Gururangan et al. , 2018; Glockner et al. , 2018)  show that InferSent, despite being state of the art on SentEval evaluation tasks, has poor performance when dealing with asymmetrical tasks and non-additive composition of words. In addition to providing new ways of training sentence encoders, we will also extend the SentEval evaluation framework with a more expressive composition function when dealing with relational transfer tasks, which improves results even when the sentence encoder was not trained with it.
Training tasks Natural language inference (T = NLI)'s goal is to predict whether the relation between two sentences (premise and hypothesis) is Entailment, Contradiction or Neutral. We use the combination of SNLI dataset  (Bowman et al. , 2015)  and MNLI dataset  (Williams et al. , 2017). We call AllNLI the resulting dataset of 1M examples. Conneau et al. (2017)  claim that NLI data allows universal sentence representation learning. They used the f ,-composition function with concatenated sentence representations in order to train their Infersent model. We also train on the prediction of discourse connectives between sentences/clauses (T = Disc). Discourse connectives make discourse relations between sentences explicit. In the sentence I live in Paris but I'm often elsewhere, the word but highlights that there is a contrast between the two clauses it connects.
We use  Malmi et al. 's (2017)  dataset of selected 400k instances with 20 discourse connectives (e. g. however, for example) with the provided train/dev/test split. This dataset has no other supervision than the list of 20 connectives. Nie et al. (2017)  used f ,-concatenated with the sum of sentence representations to train their model, DisSent, on a similar task and showed that their encoder was general enough to perform well on SentEval tasks. They use a dataset that is, at the time of writing, not publicly available.
Conclusion We have demonstrated that a number of existing models used for textual relational learning rely on composition functions that are already used in Statistical Relational Learning. By taking into account previous insights from SRL, we proposed new composition functions and evaluated them. These composition functions are all simple to implement and we hope that it will become standard to try them on relational problems. Larger scale data might leverage these more expressive compositions, as well as more compositional, asymmetric, and arguably more realistic datasets  (Dasgupta et al. , 2018; Gururangan et al. , 2018). Finally, our compositions can also be helpful to improve interpretability of embeddings, since they can help measure relation prediction asymmetry. Analogies through translations helped interpreting word embeddings, and perhaps anlyzing our learned t translation could help interpreting sentence embeddings.
The resulting collection of output variables (usually in a gridded form covering the globe and evolving in time) and the associated temporal and/or spatial constraints on them are referred to as the CMIP6 Data Request (DREQ). The modelling centres participating in CMIP6 are now archiving the requested model output and making it available for analysis. The DREQ is significantly more complicated that the data requests from previous CMIP phases, complexity which arises from the size of CMIP6 and the inter-relationships of MIPs. In this paper we describe that complexity, introduce the tools which were provided to capture and communicate the DREQ, provide some headline statistics associated with the DREQ, and outline some of the problems encountered and potential solutions for future exercises.
When used as a planning tool, it allows exploration of the consequences of various levels of commitment in terms of data volumes and numbers of variables. When a center has begun generating data, the DREQ provides the specifications for each variable. The main infrastructure providers who depend on the DREQ are the developers of the Climate Model Output Re-writer (CMOR) package, 4  those developing quality control software, and those doing planning for the Earth System Grid Federation (ESGF) data delivery services. 5 The relationship with the CMOR team is especially important as the DREQ and CMOR intersect in supporting the metadata specifications for CMIP6 output. Users are mainly expected to use portal search interfaces (e. g. the ESGF search interface) to locate existing CMIP6 data, but, especially in early stages, may also rely on the DREQ to determine what data may eventually be found there.
Despite the flexibility, there is a minimum requirement: when a modelling centre commits to participating in a MIP, it is expected to provide all the priority 1 variables needed to address at least one of the scientific objectives of that MIP. This approach ensures that CMIP has a large and representative model ensemble, but it also means that users who would like to have all models running the same collection of experiments and producing the same set of variables will not find the consistency that they want. The data provided by some models will be more limited than for others. To ensure some consistency across the CMIP archive, the DREQ is structured to provide a menu of choices defining blocks of variables with differing priorities and scientific objectives.
Structure The data request contains an extensive range of specifications which define climate data products which will be held in the CMIP6 archive 6. The data products will, when generated in accordance with the full data format specifications 7 , comply with the the data model of the CF Conventions  (Hassell et al. , 2017). The data request on its own does not provide the full format specifications, but does provide enough information for each variable to allow the automated production of compliant data files. That is, where there are multiple options available in the format specifcations, the data request determines which choices should be made for each variable. In order to manage these specifications which are aggregated across the many participating endorsed MIPs, the specifcations themselves are required to fit within an information model, which we call the Data Request Information Model (DRIM) to distinguish it from the data model of the NetCDF files described by  Hassell et al.
(2017) , on the one hand, and the Common Information Model documenting the experiments, simulations and models  (Pascoe et al. , 2019) , on the other hand. The DRIM is expressed through an XSD schema  (Juckes, 2018a)  discussed further in section 4. 2 below. The nature of the process of establishing the CMIP6 Data Request has required that the DRIM itself evolve as information is gathered. In order to manage this process, the DRIM is constrained to stay within a pre-defined framework. • C1: An excel workbook, defining the attributes used in each section of DREQ; C2: The schema is expressed as an XSD document; C3: A sample XML document which complies with the schema is constructed. This allows verification of the logical consistency of the schema and facilitates construction of the full DREQ XML document; C4: A python class is defined for each section, combining the base class with configuration information; C5: The excel workbook (C1) is converted to a structured XML document for robust portability; • P1: An XML document contains the aggregated information content; P2: A python API provides a programmable interface and command line options; P3: Web pages support browsing and searching.
Schema and Content Implementation The reference document for the Data Request content is an XML document  (Bray et al. , 2008)  conforming to an XML Schema Definition  (Gao et al. , 2008)  (XSD) document. The schema has been developed to satisfy the requirements that have emerged during the MIP endorsement process. The configuration-driven approach allows the Data Request Schema to be generated from a framework document, and the same framework document is used to generate Python classes for the Application Programming Interface (API). The Request Document aims to be self descriptive: each record is defined by its attributes, and for each attribute there is a record defining its role and usage. The apparent circularity is resolved as shown in Table  B2 , where the description attribute of the record defining description defines itself.
The foundation is provided by standard names from the CF convention: 927 of these are used in the CMIP6 Data Request, and for 728 of these there is a unique associated MIP variable. The Standard Name may be re-used up to 33 times, though smaller degrees of re-use are the norm (145 standard names used twice, 25 used three times). The standard name re-used most often is the area_fraction, which is used to represent the proportion of a grid cell associated with a particular category of surface type. These different categories are represented in an ancillary variable with standard name area_type. In most cases, when a standard name is re-used there will be additional CF metadata specifying details which distinguish between the different variables, such as the area_type. There are a handful of cases, such as "Upwelling Longwave Radiation [rlu]" and "Upwelling Longwave Radiation 4XCO2 Atmosphere [rlu4co2]" for which the difference is only in descriptive metadata (in this case the rlu4co2 variable uses an atmosphere with carbon dioxide levels enhanced by a factor 4).
Links and Aggregations The DREQ can be thought of in terms of triads (or triples) linking variables, experiments and objectives. That is, whenever a variable is requested from an experiment, it is linked to one or more objectives. There are over 350,000 potential variableexperiment-objective triads in the CMIP6 Data Request, arising from various combinations of 2068 variables, 273 experiments and 93 objectives. These three-way links may be supplemented with additional information, such as a specific sampling periods or a preferred spatial grid. Less than 1% of the possible combinations are used, but this is still too many to manage individually, so rather than explicitly listing all these virtual triads, the Data Request organises them in groups. This results in just 411 request links, with groups of variables needed to address one or more objectives linked to groups of experiments.
Additional implicit structure Much of the DREQ structure is formalised by use of the XSD schema mechanism, however there is a great deal more organised structure within the DREQ that is not explicitly represented by the XSD schema semantics. Prominent examples include constraints on acceptable units, the use of guide values, conditional variable requirements, and vertical domain requirements. CF standard names have a canonical unit, which defines the class of acceptable units for a variable with that standard name. For instance, if the standard name has canonical unit seconds then associated variables can use any valid unit of time, such as day or hr. This and other consistency rules have been incorporated in the python code that, in addition to checking the schema, also checks, for example that: • a vertical coordinate (e.