dataesr/scientific-paragraphs-categorization huggingface dataset:
Several systems support and provide NLP tools using cloud computing. GATECLOUD  [52]  is a cloud-based version of the GATE set of NLP tools with support for 24 languages, and it provides many specialty services. It is an open-source software that comes with a limited free version and pay-as-you-go options. Lexalytics  [53]  is a text analytics service providing entity extraction, sentiment analysis, document summarization, and thematic extraction; Amenity Analytics  [54]  is cloud-based natural language processing. It offers NLP text analytics and sentiment analysis tools; TEXT2DATA  [55]  is a text analytics platform as a service SaaS. It offers features such as sentiment analysis, document classification, and entity extraction. Another cloud computing service supporting machine learning development is BigML [56], a SaaS approach to machine learning.
Users can set up data sources, create, visualize, share prediction models (only decision trees are supported), and use models to generate predictions. All from a Web interface or programmatically using REST API. It provides a variety of options for text analysis along with other machine learning algorithms. Google Prediction API  [57]  is Google's cloud-based machine learning tool that can help analyze different data. It is closely connected to Google Cloud Storage, where training data is stored. It offers its services using a RESTful interface, client libraries allowing programmers to connect with Java, JavaScript,. NET, Ruby, Python, and other programming languages. Google Natural Language  [7]  provides sentiment analysis, entity analysis, entity sentiment analysis, content classification, and syntax analysis.
Eigen tech  [58]  is a natural language processing/machine learning B2B software platform. Eigen's platform in general and sector agnostic is currently focused on the financial services, legal, and insurance sectors. It can classify, extract, organize, and analyze text data. Matrix  [59]  is built using Apache MahoutTM. It can be accessed as PaaS using a RESTful interface. It can incrementally update the model once new data is available. It is organized in two layers serving (open source and free) and computation (Hadoop-based). A French startup nlpcloud  [60]  specializes in providing NLP focused compute platform as a service. It includes various NLP-related services such as; named entity recognition, classification, summarization, question answering, sentiment analysis, text generation, translation, language detection, part-Of-speech tagging, tokenization, and lemmatization.
Many companies offer cloud platforms for the development, management, and deployment of applications. The core of Google's business is all in Cloud Computing. Services delivered over network connections include search, email, online mapping, office productivity (including documents, spreadsheets, presentations, and databases), collaboration, social networking, and voice, video, and data services. Users can subscribe to these services for free or pay for increased levels of service and support. As the world's largest online retailer, the core of Amazon's business is e-commerce. While e-commerce itself can be considered Cloud Computing, Amazon has also provided capabilities that give IT departments direct access to Amazon compute power. Key examples include S3 and EC2. S3 stands for Simple Storage Services.
Anybody from any discipline may easily use a web interface to analyze language data or construct more complicated NLP systems. In a cloud computing context, developing an NLP PaaS requires consideration of the following requirements: Straightforward deployment and sharing of NLP pipelines-How can we achieve this transparently for the NLP developer, i. e. , NLP applications developed on a desktop machine can run without any adaptation on the PaaS. Furthermore, developers need to be able to share their NLP pipelines easily as SaaS, with on-demand scalability and robustness ensured by the underlying NLP PaaS; Efficient upload, storage, and sharing of large corpora-An NLP PaaS needs to provide users with a safe and efficient way to bulk upload, analyze, and download large text corpora, i. e. , batch processing over large datasets.
The cloud architecture consists of two segments; frontend and backend, which refer to the separation of concerns between the presentation layer(frontend) and data access layer (backend). The backend contains additional components of infrastructure, data storage, cloud computing environment, and service interface/API while the frontend provides a medium for clients to connect with services. The infrastructure is made up of hardware components, data storage is normally databases for handling and storing data, cloud environment defines cloud deployment structure, service interface/API handles operations between client and cloud. The client accesses the API using the frontend over the internet. The visual representation of cloud architecture is given in Fig. 2. In addition to these technical requirements, an NLP PaaS needs to offer comprehensive methodological support to underpin the life cycle of NLP applications: Build an initial NLP pipeline prototype, test a small collection of documents, use an NLP application development environment, run on a regular desktop, or a local server computer; Crowd-source a gold-standard corpus for assessment and/or training, using a Webbased collaborative corpus annotation tool, deployed as a service on the PaaS; Evaluate the performance of the automatic pipeline on the gold standard (either locally within the desktop development environment or through the manual annotation environment on the cloud).
Return to step 1 as required for further development and evaluation cycles; upload large datasets and deploy the NLP pipeline on the PaaS; run the large-scale textprocessing experiment and download the results as XML, JSON, RDF, or schema. org formats. Optionally, an NLP PaaS could also offer scalable semantic indexing and search over the linguistic annotations and the document content; Lastly, analyze any errors and, if required, iterate again over the critical system development stages, either on a local machine or on the NLP PaaS. The depiction of the NLP platform as a service is given in Fig. 3.
A new business sector has arisen with the rapid increase of data and the demand for new ways to store, process, and analyze data. Companies started to view the cloud and big data as valuable business opportunities. Several new companies have recently focused on providing big data as a Service (BDaaS) or DataBase as a Service (DBaaS). Companies such as Google, IBM, Amazon, and Microsoft also supply customers with ways to access big data on demand. Big data not just address the ability to store large amounts of data, but also the means to extract and process knowledge from the stored data  [64]. In practice, a big data database can contain structured and unstructured data that can overlap, vary, and have various volumes at different speeds. Big data features differ from other data in five aspects: volume, velocity, variety, value, and complexity.
Text segmentation is an essential step in any NLP process. Electronic text, in its raw form, is essentially just a sequence of characters. Therefore, it has to be divided into linguistic units. Such units include words, punctuation, numbers, alphanumeric, or other representations. This process is also referred to as tokenization. NLP tasks require linguistic entities (tokens) to be allocated, classes. Parts-of-speech is an essential linguistic concept in NLP, and parts-of-speech taggers are used to assign each word to syntactic categories (e. g. , noun, verb, adjective, adverb, or other categories. ). Software focusing on text mining and text analysis, such as the Apache UIMA project or General Architecture for Text Engineering (GATE), are designed to facilitate unstructured content analysis  [66].
Syntax-centred NLP can be generally classified into keyword spotting, lexical affinity, and statistical methods. Keyword spotting is the most naïve approach and probably the most common because of its accessibility and economy. Lexical affinity is slightly more sophisticated than keyword spotting. It assigns to arbitrary words a probabilistic 'affinity' for a specific category instead of merely detecting apparent words. Since the late 1990s, Statistical NLP has been the dominant course of NLP science. It is based on language models based on popular machine learning algorithms such as maximum-likelihood, expectation-maximization, conditional random fields, and Support Vector Machines. Generally, statistical methods are semantically weak  [1]. Web data analysis aims to automatically retrieve, extract, and evaluate information from cloud documents and services to discover useful knowledge.
Web analysis is related to database, information retrieval, NLP, and text mining. Web data analysis can be divided into three related fields: mining of web content, mining of web structures, and mining of web use. Web content mining is the process of finding practical knowledge from web pages that usually involves several types of data, such as text, image, audio, video, code, metadata, and hyperlinks. Web structure mining includes models for discovering web link structures. We use mining, and it is intended to explore auxiliary data generated through web dialogue or activities. The web search problem is divided into three components: web content gathering (crawling), inverted index building (indexing), and ranking documents are given a query (retrieval). Gathering web content and building inverted indexes are, for the most part, offline issues.
In sentiment analysis, extraction techniques are widely used and include term presence and their frequency, part-of-speech information, negations, and opinion words and phrases  [69]. In the big data environment, a key advantage of construction-based parsing is that only small parts of the text are required to extract meaning; word category information and the generally small size of constructions mean that the parser can still use errorfilled or conventionally unseen text. Massive parallel processing (MPP) structures such as Hadoop, NoSQL, or MPP databases, have been employed to support big data. Table  1 ,  [70]  compares some of the big data technologies in their strengths and weaknesses. Hadoop / MapReduce techniques and clusters of Hadoop are very successful in processing large quantities of textual data  [71].
NLP was initially referred to as natural language understanding (NLU), and although the NLP's ultimate goal is "true" NLU, more research is still needed to achieve that. The ability to logically infer conclusions from textual sources is yet being developed and improved to incorporate the richness of language in terms of imprecise knowledge, causality, and ambiguous meaning. In an NLP system, the most basic level is based on lexical analysis, which deals with the words considered the atomic structure of text documents. In particular, it is the process that occurs when the essential components of a text are analyzed and grouped into tokens, which are sequences of characters with collective meaning. In other words, lexical analysis promotes the understanding of single words depending on the context in which they appear, which can relate to more than one concept.
Information extraction via NLP techniques within big data One of the most investigated data types in big data involves those without a welldefined structure, unstructured. Textual data fall within this category, and NLP techniques allow relevant information to be identified and evaluated between concepts embedded in textual data sources  [75]. The primary objective in defining relationships between concepts, which is crucial in any decision-making and information extraction process, is to select the correct concepts and the type of links that connect them. For instance, similar concepts (e. g. , "disease" and "illness"), or those at different lexical levels, need to be appropriately analyzed and selected to provide the appropriate amount of information. Also, contradictory information is often found in large textual datasets, as it is common to find opposite relationships in the same context  [76].
Many real-world problems fall into this field as it can be expensive to use experts in a specific area to mark an entire dataset. The semi-supervised Learning methodology combines both Supervised and Unsupervised Learning that addresses issues that include both labeled and unlabelled data. Unsupervised learning discovers the data structure, while supervised learning creates best guess predictions for the unlabelled data  [80]. An example of the implementation of Semi-Supervised Learning is the Python VADER sentiment analysis tool, which assesses the sentiment polarity of each word on social media platforms  [81]. The reinforcement learning methodology is also widely used, using sophisticated algorithms to take action based on its current state. Popular reinforcement Learning methods include the Markov decision process (MDP) and a neural network-based NEAT (NeuroEvolution of Augmenting Topologies)  [82].
If the search terms are too narrow, vital information may be overlooked; if it is too broad, the resulting set of 'hits' could include large numbers of completely irrelevant 'false positives  [84]. Automatic Parts-of-Speech taggers have to tackle many challenges, including the ambiguity of word types in their parts-of-speech, and classification problems due to the ambiguity of periods ('. '), which can either be interpreted as a part of a token (for instance, abbreviation), punctuation (full stop), or both  [66]. Mining opinions and sentiments from natural language is a challenge because it requires a detailed understanding of the rules of language that are clear and implicit, regular, irregular, syntactic, and semantic. Sentiment analysis researchers struggle with unresolved problems of NLP: coreference resolution, negation handling, anaphora resolution, named-entity recognition, and word-sense disambiguation.
Opinion mining is a very restricted NLP problem because the system needs only to understand the positive or negative feelings of each sentence and the target entities or topics  [85]. Significant progress has been made in delivering NLP technologies such as extracting information from big unstructured data on the web, sentiment analysis in social networks, or grammatical analysis for grading essays. However, efficiently extracting potentially helpful information and understanding unstructured clinical notes (text) in the proper context remain challenges  [86]. Syntactic parsing of natural language sentences is a central task in NLP due to its significance in mediating between linguistic expression and meaning. There are two common shortcomings in standard approaches  [87]. Simplifying language assumptions, people often develop an algorithm in NLP and machine learning and then push the data into a compatible format with this algorithm.
For example, a common first step in text classification or clustering is representing texts in terms of unordered lists of words (ignoring word order and grammatical structure), a so-called bag of words. This leads to obvious problems when trying to understand a sentence. In feature representation, most learning systems' efficiency depends crucially on the input's feature representation. Each of these features took a long time to develop and integrate for each new task, slowing down the final algorithm's development and run-time. Stream processing is complicated in MapReduce, although Hadoop/Mapreduce techniques and Hadoop clusters are instrumental in processing large quantities of textual data  [71]. The big data problem influences NLP and the main challenge is not only the collection and scale of documents but also the heterogeneous nature of language  [88].
Conclusion Cloud services for Natural Language Processing have complete attention among both academia and industry. It enables researchers to deploy, share, and use language processing components and resources, following the data as a service and software as a service paradigms. In this paper, we performed a survey on NLP in cloud computing. We presented a study on NLP in cloud computing and introduced an overview of NLP by discussing different levels of NLP and components of NLG followed by applications of NLP. This paper also discusses cloud computing with an emphasis on cloud services for NLP. Furthermore, we have highlighted cloud architectural layers and their deployment models. Moreover, we have presented a motivation for the need for NLP as a service in the cloud. A set of technical requirements are identified for developing a platform as a service.
Acknowledgements This work was supported by  National Founding  from the  FCT Fundação para a Ciência e a Tecnologia , through the  MOVES  Project -PTDC/EEI-AUT/28918/2017 , and by  Operação Centro -01-0145-FEDER-000019-C4 - Centro de Competências em Cloud Computing , co-financed by the  Programa Operacional Regional do Centro  ( CENTRO 2020 ), through the  Sistema de Apoio á Investigação Científica e Tecnológica-Programas Integrados de IC&DT.
2022 ). The project has been operational since February 2016, and o v er the past 6 yr has so far acquired o v er 300 billion measurements of o v er 30 million stars. Within this treasure tro v e of data, the NGTS has disco v ered 19 new planetary systems (e. g. Bayliss et al. 2018 ; Bryant et al. 2020 ; Tilbrook et al. 2021 ) , with more yet to be confirmed. A few of the highlights include the disco v ery of the Neptune desert planet NGTS-4b  (West et al. 2019 ) , an ultra short-period Jupiter NGTS-6b  (Vines et al. 2019 ) , and the shortest period hot Jupiter NGTS-10b around a K5V star  (McCormac et al. 2020 ). Here, we add to the success of this project by announcing the disco v ery of a ne w, massi ve hot Jupiter orbiting a low-mass star, NGTS-21b. The manuscript is organized as follows, in Section 2 , we present the photometry extraction from NGTS, TESS, and SAAO light curve and HARPS spectroscopic follow-up.
TESS photometry The Transiting Exoplanet Surv e y Satellite (TESS;  Ricker et al. 2015 )  observed NGTS-21 in sector 1 on universal time ( UT ) 2018 July 26 and sector 27 on UT 2020-7-5, with cadences of 30 and 10 min, respectively. Full Frame Images (FFIs) have been downloaded using the python astroquery module  (Ginsburg et al. 2019 )  to query the TESSCut service  (Brasseur et al. 2019 ). For each sector, a master image was calculated and used to both determine the star aperture and identify pixels for background correction, thus estimating NGTS-21 brightness for each image. Fig. 2  shows the detrended phase-folded light curve, median, and 1 σ best-fitting model from Section 3. 3.
SAAO photometry Follow-up photometry of NGTS-21 was obtained with the South African Astronomical Observatory (SAAO) 1-m telescope equipped with the Sutherland High-Speed Optical Camera (SHOC;  Coppejans et al. 2013 ). The star was observed three times, on the nights of 2020 June 19, July 20 and July 23. All observations were taken in V band with 60 s of exposure times. The data were reduced using the safphot , 2  a custom python package for the reduction of SAAO photometric data. Standard flat-field and bias corrections were applied by safphot , which then utilizes the sep package  (Barbary 2016 )  to extract aperture photometry for both the NGTS-21 and nearby comparison stars with which to perform differential photometry. sep also measured and subtracted the sky background, while masking the stars in the image and adopting box sizes and filter widths that minimized residuals across the frame.
Stellar properties NGTS-21 properties were independently derived using the packages, spectroscopic parameters and atmospheric chemistries of stars ( SPECIES 3  ; Soto & Jenkins 2018 ) and the spectral energy distribution (SED) Bayesian model averaging fitter ( ARIADNE ;  4  Vines & Jenkins 2022 ). SPECIES estimates atmospheric parameters such as ef fecti ve temperature ( T eff ), [Fe/H], surface gravity (log g ), and microturbulence velocity ( ξ t ) from high-resolution spectra. First, SPECIES computes the equi v alent widths ( W ) of Fe I and Fe II lines with the ARES code  (Sousa et al. 2007  ). An appropriate atmospheric grid of models computed from interpolating ATLAS9  (Castelli & Kurucz 2004  ) atmosphere model as well as W are handed to MOOG  (Sneden 1973 ) , which solves the radiative transfer equation (RTE) while measuring the correlation between Fe line abundances as a function of excitation potential and W , assuming local thermodynamic equilibrium (LTE).
While solving the RTE, the correct atmospheric parameters are determined through an iterative process carried out until no correlation is found between the iron abundance with the excitation potential, and with the reduced equi v alent width ( W / λ). The Mass, radius and age are obtained from the isochrone package  (Morton 2015 )  by interpolating through a grid of MIST  (Dotter 2016  ) evolutionary tracks with T eff , [Fe/H], log g priors pre viously deri ved as well as parallax, photometry in several bands, and proper motions. Nested sampling  (Feroz, Hobson & Bridges 2009 )  is used to estimate posterior distributions for M s , R s , and age. Rotation and macro turbulent velocities are calculated from temperature calibrators and fitting the absorption lines of observed spectra with synthetic line profiles.
From our analysis using SPECIES , we deri ved the follo wing stellar properties with their 1 σ confidence interval, T eff = 4746 ± 300 K, [Fe/H] = -0. 26 ± 0. 09 dex, log g = 4. 57 ± 0. 45, Age = 11. 72 + 1. 28 -2. 36 Gyr, M s = 0. 75 ± 0. 01 M , and R s = 0. 74 ± 0. 01 R. Chemical abundances and vsini were not extracted due to the low-S/N achieved at this faint regime ( V = 15. 6). We have also estimated NGTS-21 parameters using the ARIADNE python package  (Vines & Jenkins 2022 ) , which is an automated code that extract stellar parameters by fitting archi v al photometry to different stellar atmosphere models using Nested Sampling through DYNESTY (Speagle 2020 ). The SPECIES derived stellar properties T eff , log g , and [Fe/H] as well as archi v al photometric data were used as ARIADNE input to fit the SED using different models (Fig.
The gyrochronology models we used were based on  Barnes ( 2007 ) ,  Mamajek & Hillenbrand ( 2008 ) , and  Meibom, Mathieu & Stassun ( 2009 ) , which point to an age between 1 and 4. 5 Gyr for a rotation period ( P rot ) of about 18 d (see Section 3. 4 for the P rot calculation). Additionally, we used the stardate  (Angus et al. 2019  ) code, which combines the isochrone package with gyrochronology models, thus computing an age of 4. 94 + 3. 56 -2. 59 Gyr. Since pure gyrochronology models as well as the joint analysis with isochrone fitting yield ages in statistical agreement with ARIADNE , we adopted the ARIADNE age of 10. 0 + 3. 29 -7. 30 Gyr. Yet, NGTS-21 age lower end may be more likely given its moderately low activity supported by its lack of flares as well as measured rotational period and amplitude (Section 3.
138 ± 0. 005) indicates a shallower transit depth likely caused by two stars in the photometric aperture (see Fig. 7  ). To estimate the dilution level, we used the ARIADNE code MNRAS 517, 4447-4457 (2022) (1) F or consistenc y, we hav e also estimated the dilution directly from the phase-folded light curves transit depths offset between TESS and NGTS. We assumed no dilution for the later since the contaminants light contribution inside the NGTS apertures are, if any, negligible due to their relative distances ( > 22. 6 arcsec) to NGTS-21 as well as their faint magnitudes ( V > 17 mag). We found a dilution of ∼ 26. 5 per cent , which is 6 per cent larger than the predicted dilution from the SED fitting. This may be due to some fractional flux entering the aperture coming from the two brighter stars in Fig.