dataesr/scientific-paragraphs-categorization huggingface dataset:
The average DT/land RMSE for the surface-3 km TOC reaches values as low as 1. 25 DU (8. 8 %), which is about 20 % smaller than DT/sea, about 30 % smaller than NT/land and about 15 % smaller than NT/sea. To characterise the global data set in terms of the spatial distribution, Figs. 4  and 5  show the average surface-6 km and surface-3 km TOCs, for the whole data set, over Europe. The figures display the MOCAGE pseudo-reality, MOCAGE smoothed with the MAGEAQ-TIR AK, the MAGEAQ pseudo-observations and the difference between MAGEAQ-TIR pseudo-observations and MOCAGE pseudo-reality, for the whole data set and for DT-only and NT-only subsets. The average distributions of lower and lowermost tropospheric ozone are in general well caught by the MAGEAQ-TIR pseudo-observations. The MAGEAQ-MOCAGE differences are typically in the interval ±0.
83-0. 91 over northern European urban locations, 0. 73 at Barcelona and 0. 55 at Milan. There are stronger systematic underestimations than for the general characterisation of the data set, of the order of -3 to -5 %, and even higher values for Barcelona (-6. 5 %) and Milan (-10. 82 %). The RMSEs are of a similar magnitude as for the general characterisation (1. 4-1. 7 DU, i. e. 9-10 %), except for Milan which exhibits higher values, reaching 2. 7 DU (13. 5 %). To summarise, we have found that our MAGEAQ-TIR pseudo-observations are capable of following the pseudoreality lower and lowermost tropospheric ozone column's temporal evolutions at a local scale (at selected locations), even if artifacts in the column's time series, especially for the surface-3 km TOC, at southern European urban locations are observed.
Conclusions We have shown some performance analyses for a future GEO instrument operating in the TIR spectral range, based on the instrumental specifications of MAGEAQ-TIR, following its phase 0 study  (Peuch et al. , 2010). The instrumental design of MAGEAQ-TIR is thought to be adapted to the lower and lowermost ozone monitoring. We have extended the study of  Claeyman et al. (2011b) , to test and specify the performance of MAGEAQ-TIR over a bigger data set, with a variety of different situations, and to test its capability to observe height-resolved phenomena. This has allowed, as well, the identification of the causes of poor performance. In addition, the synthetic data have been analysed at urban and rural locations, and the performance in presence of different phenomena occurring at different timescales, e.
g. STEs, photochemistry, horizontal transport, have been tested. The period 5-28 August 2009, on a 1 h revisit time basis, has been considered. First, we have characterised the vertical sensitivity and the retrieval accuracy of MAGEAQ-TIR pseudo-observations, in a global manner. By analysing the data set as a whole, we have found that the MAGEAQ-TIR would give, on average, independent surface-6 km TOC observations, with mean DOF of about 1. 0 and a maximum sensitivity at about 3. 0 km, thus, at the centre of the column. There would also be an unprecedented sensitivity to the surface-3 km TOC, with about 0. 6 DOF and maximum sensitivity at about 2. 5 km, on average. Even if MAGEAQ-TIR cannot completely separate the surface-3 km TOC information from the information coming from upper altitudes, the DOF surface-3 km can reach values of 0.
Acknowledgements. The authors are grateful to CNRS-INSU for publication support. This work is carried on in the framework of the CNES/TOSCA/GeoQAir (quantification de l'apport d'une plateforme d'observations GÉOstationnaires pour la surveillance de la Qualité de l'AIR). We wish to thank the Institut für Meteorologie und Klimaforschung (IMK), Karlsruhe Institute of Technology (KIT), Germany, for a licence to use KOPRA radiative transfer model. The authors acknowledge the use of resources provided by the European Grid Infrastructure. For more information, please reference the EGI-InSPIRE paper (http://go. egi. eu/pdnon). MOCAGE data have been provided by Météo France. The three anonymous referees are gratefully acknowledged for their constructive criticism. Edited by: T. von Clarmann The publication of this article is financed by CNRS-INSU.
Once a statement is completed, Drafter-II internally maps it to Prolog expressions, which are then automatically executed. As structural ambiguity can be resolved based on the given sequence of modification actions, languages following the conceptual authoring approach do typically not attempt to fully eliminate structural ambiguity. A given text can have multiple parse trees, only one of which corresponds to the way it was created. -P 4 E 1 N 4 S 3 , F W D A E2V (Pratt-Hartmann 2003) is a controlled language that was introduced in 2001 and corresponds to the language E 3 studied in later work (Pratt-Hartmann 2004). The ultimate goal is "to provide useable tools for natural language system specification. " E2V deterministically maps to the two-variable fragment of first-order logic. Because of this, satisfiability of E2V sentences and texts is decidable and computation is NEXPTIME complete.
Concerning the last three properties, the data show similar language counts for academic and industrial CNLs: 50 and 43 languages, respectively. On the other hand, only ten CNLs were found that originated from a governmental environment. It has to Tobias Kuhn A Survey and Classification of Controlled Natural Languages be noted, however, that information about CNLs from industry is typically much scarcer than about languages from academia or governments. It is therefore likely that most of the languages that escaped this survey (because of missing or hard-to-find information) are industrial ones. Such a bias might also be present in the case of some of the other properties discussed above. In any case, academia apparently focuses much more on languages for formal representation than for comprehensibility or translation, whereas industry seems to have an opposite focus.
-P 2 E 5 N 4 S 1 , F W A "Airbus Warning Language"  (Spaggiari, Beaujard, and Cannesson 2003 ) is a language for short industrial warnings, focusing on abbreviations and restricting the word order. This is an exemplary statement: ENG1 REV NOT LOCKED -P 2 E 1 N 3 S 3 , C W D I ALCOGRAM (Adriaens and Schreors 1992) is a CNL developed at Alcatel. It originated from COGRAM as an "algorithmic variant," focusing on the use within a computeraided language learning tool. In contrast to COGRAM, which consists of three components that declaratively define the language, ALCOGRAM is defined based on a fourstaged algorithm. Each of these four stages checks certain aspects: preparatory textual control (e. g. , "Define technical terms and acronyms in advance"), syntactic control (e. g. , "Write one instruction per sentence for single actions"), lexical control (e.
g. , "Avoid Computational Linguistics gender-specific language"), and micro control (e. g. , "Use words for a number when it is the first word in the sentence"). These are two examples of ALCOGRAM sentences: Set the switch to the middle. Press the button on your right. When the test circuit is called, a test tone with the proper transmit level is returned. -P 2 E 5 N 5 S 1 , C T W D A I ASD Simplified Technical English (ASD-STE). See Section 4. 1. -P 2 E 5 N 5 S 1 , C T W D I "Atomate Language" (Van Kleek et al. 2010) is part of the Atomate interface, which lets users define simple automatic tasks and reminders taking context and current activity into account. The language was inspired by CLOnE, ACE, and the GINO and Ginseng systems. This is an example of such a task definition: Alert me when my location is home on/after Tuesdays at 5pm with the message: Trash day! A special editor supports users in writing such sentences, using a mixture of predictive editing and conceptual authoring.
The CTE lexicon consists of about 70,000 terms with a "narrow semantic scope" (compared to CFE's less than 1,000 terms with a broader semantic scope). The syntax is restricted too, including restrictions on the use of conjunctions, pronouns, and subordinate clauses. CTE comes with a language checker that allows for interactive disambiguation on the lexical level, enriches the technical texts with SGML annotations, and uses the syntax analyzer of the KANT system (see KANT Controlled English below). -P 2 E 5 N 5 S 1 , C T W D I Clear And Simple English (CASE)  (Pym 1990 ) was a controlled English introduced in the 1980s at the J. I. Case Company, a manufacturer of construction and agricultural equipment. It descended from CFE. -P 2 E 5 N 5 S 1 , C W D I ClearTalk  (Skuce 2003 ) is a CNL for the Semantic Web first presented in the 1990s.
Its creator claims that documents in ClearTalk can be "almost automatically" translated into a formal logic notation and into other natural languages. It "offers a flexible degree of formality" that lets an author choose to "leave or remove ambiguity. " It has been used to encode more than 25,000 facts in different technical domains. ClearTalk is heavily restricted on the syntactic level (e. g. , basic sentences have the general form subject predicate complement modifier-phrases) as well as on the semantic one (e. g. , the determiner a at subject position represents universal quantification). These restrictions are expressed in a large number of rules. Two examples of sentences are shown here: Any adverb that modifies a verb must be adjacent to (that verb or another adverb).
The system employs a conceptual authoring approach for writing queries, which are then translated in several steps to SQL and given to a database engine. -P 5 E 1 N 4 S 3 , F W D A COGRAM (Adriaens and Schreors 1992) was a controlled language developed in the late 1980s for the telecommunication domain (at Alcatel). It was developed as a response to the finding that the existing controlled languages AECMA Simplified English, Ericsson English, and IBM's controlled English were "incomplete and defective in many ways. " COGRAM consists of a vocabulary of approximately 5,000 words plus another 1,000 technical terms, and a grammar with about 150 rules. These rules fall into three categories: "Do not use X," "Use only X," and "Avoid (try not to use) X. " Grammar rules of the last type can be seen as style-guides that do not restrict the coverage of the language.
The language definition is divided into three components: lexical (e. g. , "Use short infinitives of regular action verbs"), syntactic (e. g. , "Do not use a participle to introduce an adverbial clause"), and stylistic (e. g. , "Expound major topics, restrict minor topics"). The definition of COGRAM was found to be "not the most motivating of texts for technical writers to use in the writing process," which led to the development of ALCOGRAM. -P 2 E 5 N 5 S 1 , C T W D A I Common Logic Controlled English (CLCE)  (Sowa 2004 ) is a language that can be translated into first-order logic with equality in the form of the Conceptual Graph Interchange Format (CGIF). It is defined by a grammar in Backus-Naur form "that allows every ambiguity to be resolved when a sentence is parsed. " Some of the most important syntax restrictions are: no plural nouns, only present tense, and variables instead of pronouns.
This is an exemplary sentence: Several diseases result from asbestos exposure, with latency periods of 10 to 40 years or longer. Writers are supported by a software tool called CASLChecker. -P 2 E 5 N 5 S 1 , C T W D I "Controlled English at Clark" (Adriaens and Schreors 1992) was a language used at the Clark Material Handling Company. It was developed around the late 1980s and was influenced by SMART Plain English. -P 2 E 5 N 5 S 1 , C W D I "Controlled English at Douglas"  (Kleinman 1982 ) was a language developed in 1979 by the McDonnell Douglas aerospace company for their technical manuals. It was based on a dictionary of about 2,000 words (most of them verbs), favoring short and simple words and aiming at a single word per meaning and a single meaning per word. In addition to the words of the dictionary, "nomenclature words" can be introduced.
It is a domain-independent language inspired by ACE. In contrast to ACE, it uses existing linguistic and ontological resources, concretely the SUMO ontology and WordNet. These are two exemplary sentences: Dickens writes Oliver Twist in 1837. Every boy likes fudge. The syntax structure of CELT sentences is deterministically parsed. Heuristics are applied only afterwards to map the words to SUMO and WordNet. The language is implemented as a unification grammar in Prolog. -P 4 E 2 N 4 S 3 , F W I Controlled Language for Crisis Management (CLCM)  (Temnikova 2010 (Temnikova , 2011 (Temnikova , 2012 ) is a language for writing instructions about how to deal with crisis situations. The language is defined by about 80 simplification rules. These simplification rules include restrictions on text structure (e.
This is an exemplary query: Find all passages where a noun phrase contains a verb phrase; the verb phrase precedes a prepositional phrase; the prepositional phrase contains a verb "see"; Such queries are deterministically mapped to AQL, an existing formal query language. The language is defined by 51 simple grammar rules. -P 5 E 1 N 4 S 4 , F W D A Diebold Controlled English (DCE)  (Hayes, Maxwell, and Schmandt 1996; Moore 2000 ) is a controlled language developed at Diebold with the goal to make translation faster and less expensive by assisting human translators with specific translation tools. It was inspired by CTE, but is less strict concerning lexicon and grammar, making the approach more flexible. It consists of three main components: a lexical database, a set of grammar rules, and a checking tool.
(Bernth 1997) , not to be confused with Wycliffe Associates' EasyEnglish, is a language developed at IBM, which might have been influenced by an earlier controlled English at the same company (Adriaens and Schreors 1992). The main goal of EasyEnglish was to improve machine translation. The approach is based on a sophisticated grammar checker that returns suggestions and warnings. Apart from detecting common grammar errors, the system can enforce the use of a certain controlled vocabulary and can spot ambiguities. For such ambiguities, the system can propose alternatives, but it is ultimately up to the user whether to follow the system's suggestions or not. The problems encountered in a given document are quantified in the form of a clarity index, which must be above a certain threshold value.
The fact that the restrictions of the language are not enforced but just suggested does not make the language more precise or simpler than full natural English. EasyEnglish has been extended later to check not only on the sentence level but also on the document level, and this has been implemented in a tool called EasyEnglishAnalyzer  (Bernth 2006 ). -P 1 E 5 N 5 S 1 , C T W I EasyEnglish (by Wycliffe Associates)  (Betts 2003) , not to be confused with IBM's EasyEnglish, is a controlled language used for transcribing biblical texts. The original goal was to improve the translation process into other languages, but EasyEnglish is also directly used by readers with limited knowledge of English. The language is restricted with respect to lexicon, syntax, and semantics. There are two levels: Level A makes use of about 1,200 words, whereas level B has a larger lexicon of about 2,800 words.
In addition, texts should adhere to logical simplicity: "EasyEnglish writers are encouraged to identify the basic idea units in a complex sentence or paragraph and arrange them in logical order. " -P 2 E 5 N 5 S 1 , C T W D Ericsson English (EE) (Adriaens and Schreors 1992) was a language developed at Ericsson in the early 1980s, influenced by ILSAM. It is built on a closed list of acceptable words, but other words can be introduced if accompanied by a definition in EE. -P 2 E 5 N 5 S 1 , C W D I FAA Air Traffic Control Phraseology. See Section 4. 1. -P 2 E 1 N 3 S 2 , C S D G First Order English  (Pool 2006 ) is a controlled natural language that maps to first-order logic. No detailed description of this language is available. -P 5 E 3 N 4 S 3 , F W A Formalized-English (FE). See Section 4.
" The concrete step definitions are not part of Gherkin, but have to be implemented for the particular task at hand. Gherkin is therefore highly customizable and extensible, and the classification given here is meant to apply to a typical concrete language that is based on Gherkin. -P 5 E 3 N 4 S 3 , F W D A "GINO's Guided English" (Bernstein and Kaufmann 2006) is a language used in GINO, a system to query and edit ontologies. The language was influenced by Ginseng and supports the same kinds of queries. In addition, GINO has some limited support for procedural statements to introduce new entities, for instance: There is a subclass of class water area named lake. Query statements are mapped to SPARQL and procedural statements map to OWL axioms to be added or modified. Queries can exhibit structural ambiguity, in which case the system evaluates all possible interpretations and shows to the user the union of their answers.
This is a simple example: If you develop a rash, you should consult your doctor. -P 4 E 3 N 5  S 3 , F W D A iHelp Controlled English (iCE) 5 is a language developed by iHelp Ltd, a documentation consultancy company. iCE consists of "a set of flexible rules and vocabularies for companies wishing to standardise and improve their information. " -P 2 E 5 N 5 S 1 , C T W I iLastic Controlled English (iLastic 2012) is a language to allow non-developers to write intuitive and natural scripts that automatically retrieve, transform, and combine data from the web, databases, files, and other resources. This is an exemplary statement: delete all files under the tmp folder if the space of the disk is lower than 1024. -P 5 E 3 N 4 S 3 , F W I International Language of Service and Maintenance (ILSAM)  (Pym 1990 ) is an influential language similar to Caterpillar Fundamental English, from which it was derived in the 1970s.
", one would write: These icons show the status or activity of the copier. The language is implemented with the help of the MAXit Checker by SMART Communications. -P 2 E 5 N 5 S 1 , T W D I OWL ACE  (Kaljurand and Fuchs 2006 ) is a controlled language for the ontology language OWL. Syntactically, it is a subset of ACE. Semantically, it is tailored towards the expressiveness of OWL and is more specific than ACE with its underspecified semantics, particularly in the case of plurals. Thus, OWL ACE is more precise but less expressive than ACE. -P 5 E 2 N 4 S 3 , F W A "OWLPath's Guided English" (Valencia-García et al. 2011) is a query language for a tool called OWLPath, with which ontologies can be queried. Statements in this language start with the phrase View any. These are two examples: View any COMMODITY has_quoted_price in BMF.
View any COMPANY whose STOCK_PRICE. lastTrade is_greater_than $30 and is_included_in Dow_Jones in 2009-04-24. These statements are translated into the SPARQL query language. Even though their structure roughly follows English grammar, they cannot be considered valid English sentences. -P 5 E 2 N 3 S 4 , F W A OWL Simplified English  (Power 2012 ) is a controlled language for the Semantic Web. In contrast to most other approaches, there is no real lexicon, neither built-in nor userdefined. Only a very small number of function words are predefined, and users have Tobias Kuhn A Survey and Classification of Controlled Natural Languages to list the verbs they intend to use. All other word categories are inferred based on syntactic clues such as capitalization and adjacent words. This is an example (assuming that governed and lives are listed as verbs): London is capital of a country that is governed by a man that lives in Downing Street.
-P 5 E 2 N 4 S 4 , F W A "PathOnt CNL"  (Kim et al. 2005 ; Namgoong and Kim 2007) is a controlled language developed for a tool called PathOnt. The tool is multilingual, supporting English and Korean. Statements in this language are deterministically mapped to RDF triples. These are two exemplary sentences: Nam is a student supervised by a professor named Kim. A received specimen fixed in formalin is a soft tissue mass. The language seems to cover only simple existential statements. -P 5 E 1 N 4 S 4 , F W A PENG  (Schwitter 2002 ) is a controlled language whose name stands for "Processable English. " It is a rich but unambiguous language that can be automatically translated via discourse representation structures into first-order logic with equality. It is inspired by ACE, and the approach has a strong focus on predictive editing.
P 1 and P 2 represent the propositional variables for the respective boxes, and T 1 is a time variable. -P 5 E 2 N 3 S 4 , F W D A I ucsCNL  (Barros et al. 2011 ) is a controlled natural language for use case specifications in the area of automated software testing. The language is intended to be unambiguous and is defined by a small number of simple grammar rules. There are imperative sentences to describe user actions, as well as declarative statements to describe the system state before and after user actions: After creating a message with 100 characters, go to the drafts folder The imported media file is a music file -P 5 E 2 N 4 S 4 , F W D A Voice Actions 7  are a CNL for spoken action commands on the Android mobile phone platform. Currently, the language covers twelve informally defined command patterns such as "map of," "note to self," and "create a calendar event.
Acknowledgments I would like to thank  Norbert E. Fuchs ,  Stefan Höfler ,  Kaarel Kaljurand ,  Rich Morin ,  Rolf Schwitter ,  Simon Spero , and  David Whitten  for comments on the article and general discussions on the topic. I am also thankful for the responses from  Orlando Chiarello ,  Esra Erdem ,  Richard Power ,  Ronald G. Ross ,  Nestor Rychtyckyj ,  Donia Scott ,  Irina Temnikova , and  Andries van Renssen  to questions about specific languages. In addition, the feedback from  Robert Dale , editor-in-chief of the Computational Linguistics journal, anonymous comments from its editorial board, and the anonymous reviews were very helpful to further improve the article. Lastly, I am extremely thankful to  James Tierney  for working with me on the manuscript to improve grammar and style.
Another example is the International Classification of Diseases (ICD), the World Health Organization's standard diagnostic tool for epidemiology, health management and clinical purposes used to monitor the incidence and prevalence of diseases and other health issues. The current ICD-10 version contains more than 12,000 concepts designated with terms in 43 different languages including English, Spanish and French. There is a clear need to establish mappings between these different KOS in order to make inter-operable systems that use them. For instance, the EU-ADR project  [8]  developed a computerised system that exploits data from eight European healthcare databases and electronic health records for the early detection of adverse drug reactions (ADR). As these databases use different medical terminologies (ICD-9, ICD-10, Read Codes, International Classification of Primary Care) to encode their data, mappings are needed to translate a query posed to the global system into queries understandable for the different data sources.
The DT learning model is used to combine different terminological similarity measures, and a similarity propagation method is performed to discover mappings by exploiting structural information of entities. A semantic verification is used to refine computed mappings in order to eliminate those which are inconsistent. All these three systems obtained very good results for the task related to large ontologies matching during the 2012 edition of OAEI. Among the systems which were used at OAEI and are primarily dedicated to matching ontologies in the biomedical domain, the SAMBO system  [24]  achieved the best performance for alignment of the largest task (anatomy track) before the introduction of the Large-Biomed track. This system uses a terminological matcher (based on the textual descriptions of concepts and relations), a structural matcher based on the is-a and partof hierarchies and domain knowledge based on the Metathesaurus of the Unified Medical Language System (UMLS)  [34].