dataesr/scientific-paragraphs-categorization huggingface dataset:
INTRODUCTION The Research Topic on "NLP-enhanced Bibliometrics" aims to promote interdisciplinary research in bibliometrics, Natural Language Processing (NLP) and computational linguistics in order to enhance the ways bibliometrics can benefit from large-scale text analytics and sense mining of papers. The objectives of such research are to provide insights into scientific writing and bring new perspectives to the understanding of both the nature of citations and the nature of scientific papers and their internal structures. The possibility to enrich metadata by the full-text processing of papers offers a new field of investigation, where the major problems arise around the organization and structure of text, the extraction of information and its representation at the level of metadata. Recently, the ever growing availability of datasets and papers in full text and in machine-readable formats has made possible a change in perspective in the field of bibliometrics.
From preprint databases to the Open Access and the Open Science movements, the development of online platforms such as ArXiv, CiteSeer or PLoS and so forth, largely contribute to facilitating the experimentation with datasets of articles, making it possible to perform bibliometric studies not only considering the metadata of papers but also their full text content. The field of NLP offers methodological frameworks and tools for the full text processing of papers that can enlighten bibliometric studies. Some of the open source tools for text processing that have been recently applied to such tasks include NLTK, Mallet, OpenNLP, CoreNLP, Gate, CiteSpace, AllenNLP, and others. Many datasets are now freely available for the community: e. g. , PubMed OA, CiteSeerX, JSTOR, ISTEX, Microsoft Academic Graph, ACL anthology, etc.
PAPERS IN THIS RESEARCH TOPIC The seven papers published in this Research Topic were all reviewed by two independent reviewers. In the paper "Is the Abstract a Mere Teaser? Evaluating Generosity of Article Abstracts in the Environmental Sciences, " Ermakova et al. examines the abstracts of scientific papers. In fact, the abstract points out the information that is the most important for the reader and is often used as a proxy for the content of an article. The authors propose the GEM score that measures the representativeness of an abstract or its "generosity. " To obtain this score, sections in the papers were weighted according to their importance to the reader and sentences in the abstracts were assigned to different sections based on their similarity with the content of the sections. More than 36,000 papers in environmental sciences, retrieved from the ISTEX database, were processed to observe the trends in the GEM score over an 80year period of time.
They use a gold standard consisting of manually annotated instances of inline terms (multi-word nominal expressions) of different types of documents (e. g. , patent, journal article). In the paper "Deep Reference Mining From Scholarly Literature in the Arts and Humanities, " Rodrigues Alves et al. work on a deep learning architecture for the detection, extraction and classification of references within the full text of scholarly publications. The authors explore word and characterlevel word embeddings, different prediction layers (Softmax and Conditional Random Fields) and multi-task over singletask learning components. Their experiments are based on a published dataset of annotated references from a corpus of publications on the historiography of Venice (books and journal articles in Italian, English, French, German, Spanish and Latin) published from the nineteenth century to 2014.
In the paper "Resolving Citation Links With Neural Networks, " Nomoto presents a novel way to tackle the citation resolution through the application of neural network models and identifying some of the operational factors that influence their behavior. The author introduces the notion approximately correct targets which is "an idea that we should treat sentences that occur in the vicinity of true targets as equally correct, whereby we try to identify an area which is likely to include a true target, instead of finding its exact location. " Experiments in the paper are conducted using three datasets developed by the CL-SciSumm Shared Task (ACL repository) and a cross validation style setup. The two papers "The NLP4NLP Corpus (I and II): 50 Years of Publication, Collaboration and Citation in Speech and Language Processing" by  Mariani et al.
and Mariani et al. , present  the results of an extensive study of a dataset in the field of Natural Language Processing (NLP) and Spoken Language Processing (SLP) for the period 1956-2015. The authors investigate various trends that can be observed from the publications in this specific research domain. The study is presented in two companion papers that each provides a different perspective of the analysis. The first paper describes the corpus and presents an overall analysis of the number of papers, authors, gender distributions, co-authorship, collaboration patterns and citation patterns. The second paper investigates the research topics and their evolution over time, the key innovative topics and the authors that introduced them, and also the reuse of papers and plagiarism. Together, the two papers provide a survey of the literature in NLP and SLP and the data to understand the trends and the evolution of research in this research community.
Taken together, these papers identify some of the new challenges in this area and pave the way for future theoretical frameworks. The development of deep learning techniques is emerging in this field with approaches based on neural network models and can play a fundamental role in the exploitation of citations and their contexts in the scientific literature. While the development of neural network models requires large resources, the increasing number of datasets that are available today allows the implementation of this type of technology for the analysis of citations. Indeed, two of the articles in this Research Topic deal with the implementation of neural network models for citation analysis  (Rodrigues Alves et al. and Nomoto) , and other two with the construction and exploitation of a large scale corpus of papers  (Mariani et al.
Integrated approaches: GRASP×ELS solution vs. CPLEX optimal RCPSPR resolution The linear formulation of the RCPSPR  (Lacomme et al. , 2017) , encompasses a formulation with a large number of constraints and variables: for the instance LMQV_U1, for example, the linear formulation is composed of 54,186 variables and 140,974 constraints, including binary variables to model the disjunction of activities. Only 14 instances are solved to optimality in less than one day of computation time (Table  6 ). In the optimal solution of instance LMQV_C9, the linear program has to deal with six activities and 26 transport operations, representing 32 operations to be scheduled. The notation t. l. is used in tables for computational time exceeding 48 hours. Table  7  provides a comparative study between the best found solutions with CPLEX MILP and the GRASP×ELS resolution where both methods solve the RCPSPR using an integrated approach.
In this context, the H2020 eDREAM project  [45]  contributes to the transformation of the traditional energy systems into novel decentralized and community-driven ones, by leveraging on blockchain technology to exploit local capacities and constraints fully at micro-grid level to preserve the continuity and security of supply at affordable costs at smart grid level. The grid is modeled as a collection of DEPs' resources able to coordinate through a blockchain based infrastructure to support fully decentralized management, control and stable grid operation. The eDREAM project presents a blockchain decentralized management relying on the implementation of  [34] : -A distributed ledger for storing DEPs' energy data at the micro-grid level. All monitored energy data recorded at the level of a DEP are registered and stored as immutable transactions.
Specific Challenge B: Social Media Feeds' Annotation and Dictionary Definition A market sentiment dictionary for financial social media data applications is presented in  [27] , and then a fine-grained analysis of financial Tweets is described in  [26]. The dictionary in  [27]  with 8,331 words, 112 hashtags, and 115 emojis is available publicly at http://nlg. csie. ntu. edu. tw/nlpresource/NTUSD-Fin/ under CC BY-NC-SA 4. 0 license. It is built from posts in the StockTwits dataset  [71] , at that time providing 334,798 labeled posts from 13,059 users and crawled through StockTwits API (https://api. stocktwits. com/developers/docs). The dictionary stores unstemmed tokens appearing at least ten times and showning significant difference in chi-squared test (significance level 0. 05) between expected and observed frequency.
Also, stopwords, punctuations, digits, URLs, user ids, and tickers are removed from the input posts, while still specifically processing emojis. Input posts with less than two words are also removed. As the paper also analyses sentiment, it is discussed that based on this dictionary, the sentiment of investors may depend on the positions they hold (e. g. positive investor sentiment does not imply bullish market sentiment due to the target of the investor). The FiQA 2018 Task 1 (https://sites. google. com/view/fiqa/home) is supported by Social Sentiment Index (SSIX) Horizon 2020 project (https://ssixproject. eu/), and it includes 675 training instances to predict continuous sentiment. In  [26] , utilizing the dictionary  [27] , the FiQA 2018 Task 1 is evaluated over different neural network models with Keras (https://github.
In this section, we will focus on the third step: The possible methods to extract features specific for Sentiment Analysis, while the ranking of these features is addressed in Sect. 9. For a complete review of the possible type of features, we refer to  [1]  and  [55] , and for a description of the weight models and for a complete review on feature selection on text mining we refer to  [25, 49, 54]. The most common metric in feature selection is the χ 2 (chi-squared), a measure expressing the divergence between the feature and the class. A drawback of this metric is that it works only when the dataset is big and there are no rare features. Another two popular metrics are the Accuracy, measuring the expected accuracy of a simple classifier built from the single feature, and the F 1 , the harmonic mean of the precision and recall.
As observed in  [49] , all these metrics are equivalent, and one outperforms the others according to the dataset. In the review where twelve metrics are compared, Forman  [49] , proposed a new metric, the Bi-Normal Separation (BNS), an extension of the χ 2 where also the frequency of the appearance of the feature is considered. This metric is good in most common cases, but, when predictions move outside of this set, good results are not guaranteed. For example, in a highly skewed data context, the BNS does not work better and two information theoretic measures outperform it: Information Gain and IT F • IDF 2 ,  [77].
-Hybrid approaches leverage on both knowledge representation and Machine Learning techniques. These methods take advantage of knowledge representation models like ontologies and semantic nets, being able to extract implicit information  [19]. On the one hand there are open source tools that use Machine Learning techniques, statistics, and natural language processing to automate the processing of huge amounts of data  [41] , including web pages, online news, discussion fora, social networks, web blogs, micro-blogging, etc. On the other hand, knowledge based systems use public access resources like SentiWordNet  [7]  or SenticNet  [20]  to extract semantic and affective information linked to natural language concepts. Sentiment Analysis can also be performed on visual content, i. e. , images and videos (denoted as Multimodal sentiment analysis).
One of the first approaches in this direction was SentiBank  [14] , utilizing an adjective noun pair representation of visual content. In order to measure the precision and recall of a Sentiment Analysis tool, it is usually compared with human judgments about others' opinions  [59]. However, humans when evaluated in the same way only show an 80% of precision on average, and this means that a program with a 75% of precision works almost as well as humans  [99].
e. f (s t ) = x t+1. A simple linear regression model could be summarized by the equation: x t+1 = βs t + , where other unobserved factors determining x t+1 are captured by the error term. The dependent variable x t+1 in the textual sentiment finance literature is typically Bitcoin price and/or trade volume  [2, 37, 51, 61, 67, 136]  or Bitcoin return and volatility, as in  [30]. Logistic regression is used if the response variable is dichotomous. Here, the dependent variable is a binary variable, whose value is equal to 1 if a certain event has happened, and 0 otherwise. The idea is to examine if Sentiment Analysis is associated significantly with a certain event. In almost all the examples listed above in this section, the models used to estimate the function f are restricted to the linear ones: Linear or logistic regression in case the features are correlated, or Naive Bayes when the dataset is relatively small  [141].
Acknowledgements. This chapter is based upon work from COST Action IC1406 High-Performance Modelling and Simulation for Big Data Applications (cHiPSet), supported by COST (European Cooperation in Science and Technology). The author AZ acknowledges the financial support from the Slovenian Research Agency (Research Core Funding No. P2-0041). AZ also acknowledges EU support under Project No. 5442-24/2017/6 (HPC -RIVR). This chapter is also based upon work from COST Action CA15140 "Improving Applicability of Nature-Inspired Optimisation by Joining Theory and Practice (ImAppNIO)" supported by COST. The authors CP, TC and IS also acknowledge that the work presented in this chapter is supported financially by the eDREAM Project Grant number 774478, co-funded by the European Commission as part of the H2020 Framework Programme.
Beyond the peak of inflated expectations If machine learning and predictive analytics have potential to help us improve quality of care, they are not magic bullets. If you supply the best predictive algorithm with inaccurate information (mistakes are frequent in clinical databases, physiologic signals are often damped or distorted), you will likely end up with wrong predictions (Fig. 1 ). This is the classical concept of "garbage in, garbage out. " Also, even if machine learning algorithms are able to capture complex, nonlinear relationships in the data, no amount of algorithmic finesse or computer power can squeeze out information that is not present  [15]. Finally, predictive algorithms are not crystal balls predicting events before they occur, but rather sniffers detecting that something started to happen-like seismographs telling us that an earthquake is coming before we feel it.
This evidence might also reveal a plausible mechanism, namely to excrete excess sodium the body must increase blood pressure. In terms of study design and data quality, the authors might consider various possibilities for data collection. How should they recruit subjects? Should they randomize them to a low-sodium versus high-sodium diet? Or should they track them longitudinally, say via routine annual checkups over the course of years? Or is such data already available from some prior study? When and how often should sodium and blood pressure be measured? And how? The authors might measure sodium through a dietary recall questionnaire (noisy), through asking participants to maintain a food diary (somewhat less noisy), or through collection of urine to measure urinary sodium excretion (precise but restricted to a limited time point).
ASTERICS was the occasion to build a closer relationship with large ESFRI and ESFRI-like facilities, and moreover to include the astroparticle physics community, which is evolving from the development of experiments such as HESS or MAGIC to the implementation of observatories such as CTA. ASTERICS Work Package 4, Data Access, Discovery and Interoperability (DADI), included VO teams from France, Germany, Italy, Spain and UK, and teams working on CTA, EGO-VIRGO, KM3Net, SKA and their pathfinders. ESO (ELT) was associated to ASTERICS, and ESA continues to be an active participant in the VO development in close collaboration with the other European VO teams. EST joined the project in 2018. A large palette of wavelengths and messengers was thus represented. The aim was to optimize the scientific usage of data from the future large projects, and to make them active participants in the VO development.
The last few years have seen the fast and ubiquitous diffusion of JSON as one of the most widely used formats for publishing and interchanging data, as it combines the flexibility of semistructured data models with well-known data structures like records and arrays. The user willing to effectively manage JSON data collections can rely on several schema languages, like JSON Schema, JSound, and Joi, or on the type abstractions offered by modern programming languages like Swift or TypeScript. The main aim of this tutorial is to provide the audience with the basic notions for enjoying all the benefits that schemas and types can offer while processing and manipulating JSON data. This tutorial focuses on four main aspects of the relation between JSON and schemas: (1) we survey existing schema language proposals and discuss their prominent features; (2) we review how modern programming languages support JSON data as first-class citizens; (3) we analyze tools that can infer schemas from data, or that exploit schema information for improving data parsing and management; and (4) we discuss some open research challenges and opportunities related to JSON data.
g. , Twitter, New York Times), as a common format for the remote interaction of modern web applications (e. g. , Facebook's GraphQL is entirely based on JSON), as a common format for exchanging scientific data as well as public open data (e. g. , the U. S. Government's open data platform: https://www. data. gov). Given the wide diffusion of JSON and its use in scientific as well as mainstream applications, the need to directly manipulate JSON data inside applications rapidly emerged. To this aim, a schema language, specifically designed for JSON, has been introduced, but its adoption is not growing at a fast pace, since its specification is somewhat complex and many modern programming languages, like Swift and TypeScript, directly support JSON data through their own, simple type systems; furthermore, Walmart Labs endowed JavaScript, which is inherently untyped, with a powerful schema language for JSON objects by means of JavaScript function calls.
In this tutorial proposal, we will present and discuss existing schema and type languages for JSON data, and compare their features; we will also discuss several schema-related tools, with a particular focus on approaches for schema inference. The main aim of this tutorial is to provide the audience and developers with the basic notions for enjoying all the benefits that schemas and types can offer while processing, analyzing, and manipulating JSON data. Outline. This 1. 5-hour tutorial is split into five main parts: (1) JSON primer (∼ 10 min. ). In this very introductory part of the tutorial, we review the basic notions about JSON together with its JavaScript legacy, and present a few examples, coming from publicly available datasets, that we will use throughout the remaining parts of the tutorial.
(2) Schema languages (∼ 20 min. ). In this part of the tutorial we focus on existing schema languages for JSON data collections and discuss their most prominent features. (3) Types in Programming Languages (∼ 15 min. ). In this part of the tutorial we review how modern programming languages support JSON data as first class citizens. In particular, we focus on programming and scripting languages for web and/or mobile applications, where JSON data interchange is a crucial task. (4) Schema Tools (∼ 30 min. ). In this part of the tutorial we analyze tools that exploit schema information for improving JSON data processing. We focus on the problem of inferring a meaningful schema for schemaless JSON collections, as well as on the exploitation of schema information for improving data parsing and management.
TYPES IN PROGRAMMING LANGUAGES Unlike XML, which found no space as a first class citizen in programming languages, with the obvious and notable exception of XQuery, JSON has been designed starting from the object language of an existing scripting language. Therefore, given its wide use in web and mainstream application development, JSON support has been introduced in several strongly typed programming and/or scripting languages. To directly and naturally manage JSON data a programming language should incorporate the ability to express record types, sequence types, and union types. While record and sequence types can be easily found in many programming languages, union types are quite rare and usually confined to functional languages only. In this part of the tutorial we will discuss the support for JSON objects inside the type systems of TypeScript  [9]  and Swift  [8].
Schema Inference Several schema inference approaches for JSON data collections have been proposed in the past. In  [10] [11] [12]  authors describe a distributed, parametric schema inference approach capable of inferring schemas at different levels of abstraction. In the context of Spark, the Spark Dataframe schema extraction  [7]  is a very interesting tool for the automated extraction of a schema from JSON datasets; this tool infers schemas in a distributed fashion, but, unlike the technique described in  [10] [11] [12] , its inference approach is quite imprecise, since the type language lacks union types, and the inference algorithm resorts to Str on strongly heterogeneous collections of data. Other systems, like Jaql  [13] , exploit schema information for inferring the output schema of a query, but still require an externally supplied schema for input data, and perform output schema inference only locally on a single machine.
There are also a few inference tools for data stored in NoSQL systems and RDBMSs. Indeed, in the context of NoSQL systems (e. g. MongoDB), recent efforts have been dedicated to the problem of implementing tools for JSON schema inference. A JavaScript library for JSON, called mongodb-schema, is presented in  [22]. This tool analyzes JSON objects pulled from MongoDB, and processes them in a streaming fashion; it is able to return quite concise schemas, but it cannot infer information describing field correlation. Studio 3T  [19]  is a commercial front-end for Mon-goDB that offers a very simple schema inference and analysis feature, but it is not able to merge similar types, and the resulting schemas can have a huge size, which is comparable to that of the input data. In  [23] , a python-based tool is described, called Skinfer, which infers JSON Schemas from a collection of JSON objects.
Skinfer exploits two different functions for inferring a schema from an object and for merging two schemas; schema merging is limited to record types only, and cannot be recursively applied to objects nested inside arrays. Couchbase, finally, is endowed with a schema discovery module which classifies the objects of a JSON collection based on both structural and semantic information  [3]. This module is meant to facilitate query formulation and select relevant indexes for optimizing query workloads. When moving to RDBMSs, in  [16]  Abadi and al. deal with the problem of automatically transforming denormalised, nested JSON data into normalised relational data that can be stored in a RDBMS; this is achieved by means of a schema generation algorithm that learns the normalised, relational schema from data.