Available online at www.sciencedirect.com Available online at www.sciencedirect.com ScienceDirect AvailableScienceDirect online at www.sciencedirect.com Procedia Computer Science 00 (2018) 000–000
Procedia Computer Science 00 (2018) 000–000 www.elsevier.com/locate/procedia ScienceDirect www.elsevier.com/locate/procedia Procedia Computer Science 127 (2018) 82–91
The First International Conference On Intelligent Computing in Data Sciences The First International Conference On Intelligent Computing in Data Sciences Data science in light of natural language processing: An overview Data science in light of natural language processing: An overview Imad Zerouala* and Abdelhak Lakhouajaa Imad Zerouala* and Abdelhak Lakhouajaa aFaculty of Sciences, Mohamed First University, Av Med VI BP 717, Oujda 60000, Morocco aFaculty of Sciences, Mohamed First University, Av Med VI BP 717, Oujda 60000, Morocco
Abstract Abstract The focus of data scientists is essentially divided into three areas: collecting data, analyzing data, and inferring information from data.The focus Each ofone data of thesescientists tasks is requires essentially special divided personnel, into three takes areas: time, collecting and costs data, money. analyzing Yet, the data, next and an dinferring the fastidious informat stepion is fromhow data.to turn Each data one into of products. these tasks Therefore, requires this special field personnel, grabs the attentiontakes time, of andmany costs research money. groups Yet, thein academia next and asthe well fastidious as industry step .is In how the tolast turn decades, data into data products.-driven approachesTherefore, this came field into grabs existence the attention and gained of many more research popularity groups because in academia they requireas well asmuch industry less .human In the lasteffort. decades, Natural data Language-driven Processingapproaches (NLP)came intois strongly existence among and gainedthe fields more influenced popularity by becausedata. The they growth require of datamuch is lessbehind human the performanceeffort. Natural improvement Language Processing of most (NLP) NLP isapplications strongly among such theas fieldsmachine influenced translation by data. and The automatic growth ofspeech data isrecogn behindition. the Consequently,performance improvement many NLP applications of most NLPare frequently applications moving such from as rulemachine-based translationsystems and and knowledge automatic-based speech methods recogn to ition.data- drivenConsequently, approaches. many However, NLP applications collected dataare frequentlythat are based moving on undefined from rule design-based criteria systems or and on technicallyknowledge -unsuitablebased methods forms to wil datal be- udrivenseless. approaches. Also, they However, will be neglectedcollected dataif the that size are isbased not onenough undefined to perform design criteriathe required or on technicallyanalysis and unsuitable to infer formsthe accurate will be information.useless. Also, The they chief will purpose be neglected of this overviewif the size is tois shednot someenough lights to performon the vital the rolerequired of data analysis in various and fields to infer and thegive accurate a better uinformation.nderstanding The of chiefdata inpurpose light ofof NLP.this overview Expressly, is toit sheddescribes some whatlights happen on the vitalto data role during of data its in life various-cycle: fields building, and give processing, a better analyzing,understanding and ofexploring data in phases.light of NLP. Expressly, it describes what happen to data during its life-cycle: building, processing, analyzing, and exploring phases.
© 2018 The Authors. Published by Elsevier B.V. © 2018 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/). Selection and © 2018 The Authors. Published by Elsevier B.V. Thispeer-review is an open under access responsibility article under of theInternational CC BY-NC Neural-ND licenseNetwork (http://creativecommons.org/licenses/by Society Morocco Regional Chapter. -nc-nd/3.0/). Selection andThis peer is an-review open access under articleresponsibility under the of CCInternational BY-NC-ND Neural license Network (http://creativecommons.org/licenses/by Society Morocco Regional Chapter.-nc -nd/3.0/). Selection andKeywords: peer-review Data science; under Naturalresponsibility language of processing; International Data Neuraldriven approches; Network Corpora;Society MoroccoMachine learning Regional Chapter. Keywords: Data science; Natural language processing; Data driven approches; Corpora; Machine learning
1. Introduction 1. Introduction Recently, a variety of Natural Language Processing (NLP) applications are based on data-driven methods such as neuralRecently, networks a variety and Hidden of Natural Markow Language Models Processing (HMMs) (NLP) [1]. Since applications the progress are based in most on data of -thesedriven methods methods is such driven as neural networks and Hidden Markow Models (HMMs) [1]. Since the progress in most of these methods is driven
* Corresponding author. Tel.: +212618417420. *E -Correspondingmail address: [email protected] author. Tel.: +212618417420. E-mail address: [email protected] 1877-0509© 2018 The Authors. Published by Elsevier B.V. This1877 -is0509© an open 2018 access The Authors. article Publishedunder the by CC Elsevier BY-NC B.V.-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/). Selection andThis peer is an-review open access under articleresponsibility under the of CCInternational BY-NC-ND Neural license Network (http://creativecommons.org/licenses/by Society Morocco Regional Chapter.-nc -nd/3.0/). Selection and peer-review under responsibility of International Neural Network Society Morocco Regional Chapter.
1877-0509 © 2018 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/). Selection and peer-review under responsibility of International Neural Network Society Morocco Regional Chapter. 10.1016/j.procs.2018.01.101
10.1016/j.procs.2018.01.101 1877-0509 Available online at www.sciencedirect.com Available online at www.sciencedirect.com ScienceDirect Imad Zeroual et al. / Procedia Computer Science 127 (2018) 82–91 83 ScienceDirect Imad Zeroual / Procedia Computer Science 00 (2018) 000–000 Procedia Computer Science 00 (2018) 000–000
Procedia Computer Science 00 (2018) 000–000 www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia The First International Conference On Intelligent Computing in Data Sciences The First International Conference On Intelligent Computing in Data Sciences Data science in light of natural language processing: An overview Data science in light of natural language processing: An overview Imad Zerouala* and Abdelhak Lakhouajaa Imad Zerouala* and Abdelhak Lakhouajaa a Faculty of Sciences, Mohamed First University, Av Med VI BP 717, Oujda 60000, Morocco aFaculty of Sciences, Mohamed First University, Av Med VI BP 717, Oujda 60000, Morocco dictionaries such as the “Dictio Abstract Scottish Tongue”, the “Middle English Dictionary”, the “Dictionary of Old English”, and the “Oxford English Abstract Dictionary” The focus of data scientists is essentially divided into three areas: collecting data, analyzing data, and inferring information from data.The focus Each ofone data of thesescientists tasks is requires essentially special divided personnel, into three takes areas: time, collecting and costs data, money. analyzing Yet, the data, next and an dinferring the fastidious informat stepion is fromhow todata. turn Each data one into of products. these tasks Therefore, requires this special field personnel, grabs the attentiontakes time, of andmany costs research money. groups Yet, thein academia next and asthe well fastidious as industry step .is In how the Nation lastto turn decades, data into data products.-driven approachesTherefore, this came field into grabs existence the attention and gained of many more research popularity groups because in academia they requireas well asmuch industry less .human In the effort.last decades, Natural data Language-driven Processingapproaches (NLP)came intois strongly existence among and gainedthe fields more influenced popularity by becausedata. The they growth require of datamuch is lessbehind human the may not need to become a part of the learners’ output or the teacher performanceeffort. Natural improvement Language Processing of most (NLP) NLP isapplications strongly among such theas fieldsmachine influenced translation by data. and The automatic growth ofspeech data isrecogn behindition. the performance improvement of most NLP applications such as machine translation and automatic speech recognition. Consequently, many NLP applications are frequently moving from rule-based systems and knowledge-based methods to data- drivenConsequently, approaches. many However, NLP applications collected dataare frequentlythat are based moving on undefined from rule design-based criteria systems or and on technicallyknowledge -unsuitablebased methods forms to wil datal be- udrivenseless. approaches. Also, they However, will be neglectedcollected dataif the that size are isbased not onenough undefined to perform design criteriathe required or on technicallyanalysis and unsuitable to infer formsthe accurate will be information.useless. Also, The they chief will purpose be neglected of this overviewif the size is tois shednot someenough lights to performon the vital the rolerequired of data analysis in various and fields to infer and thegive accurate a better uinformation.nderstanding The of chiefdata inpurpose light ofof NLP.this overview Expressly, is toit sheddescribes some whatlights happen on the vitalto data role during of data its in life various-cycle: fields building, and give processing, a better analyzing,understanding and ofexploring data in phases.light of NLP. Expressly, it describes what happen to data during its life-cycle: building, processing, analyzing, and exploring phases. © 2018 The Authors. Published by Elsevier B.V. This© 2018 is an The open Authors. access Published article under by Elsevierthe CC BY B.V.-NC -ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/). Selection DOI andThis peer is an-review open access under articleresponsibility under the of CCInternational BY-NC-ND Neural license Network (http://creativecommons.org/licenses/by Society Morocco Regional Chapter.-nc -nd/3.0/). Selection andKeywords: peer-review Data science; under Naturalresponsibility language of processing; International Data Neuraldriven approches; Network Corpora;Society MoroccoMachine learning Regional Chapter. Keywords: Data science; Natural language processing; Data driven approches; Corpora; Machine learning 1. Introduction 1. Introduction Recently, a variety of Natural Language Processing (NLP) applications are based on data-driven methods such as 2. Life-cycle of data in NLP neuralRecently, networks a variety and Hidden of Natural Markow Language Models Processing (HMMs) (NLP) [1]. Since applications the progress are based in most on data of -thesedriven methods methods is such driven as neural networks and Hidden Markow Models (HMMs) [1]. Since the progress in most of these methods is driven * Corresponding author. Tel.: +212618417420. E* -Correspondingmail address: [email protected] author. Tel.: +212618417420. E-mail address: [email protected] 1877-0509© 2018 The Authors. Published by Elsevier B.V. Manning This1877 -is0509© an open 2018 access The Authors. article Publishedunder the by CC Elsevier BY-NC B.V.-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/). Selection andThis peer is an-review open access under articleresponsibility under the of CCInternational BY-NC-ND Neural license Network (http://creativecommons.org/licenses/by Society Morocco Regional Chapter.-nc -nd/3.0/). Selection and peer-review under responsibility of International Neural Network Society Morocco Regional Chapter. 2.1. Data design, sources, and format
a 84 Imad Zeroual et al. / Procedia Computer Science 127 (2018) 82–91 –
ith respect to the phenomena under in estigation f there are no specific criteria, the corpus should be designed for a general use to suit most applications. In the 90’s, primarily with the completion of the ritish ational orpus , basic guidelines in corpus design and compilation ha e been set by rele ant specialists – ater, expanded these guidelines in ten fundamental criteria hich are considered as core principles such as representati eness, balance, and homogeneity ased on these design criteria, corpora builders should identify the data source genres and si e The selection and finding suitable resources is much more complicated or example, as stressed by , the components of general corpora typically are representati e of arious genres, hilst speciali ed corpora can be limited to highlight only one genre or a family of genres egarding the si e and balance, claims that it is not al ays possible to collect data in similar or e en sufficient uantities for each text category represented in the corpus this is often the case ith historical corpora o e er, se eral sources can be used to build corpora corpus, in one hand, may consist of a single boo li e the one de eloped by to build an ontology of pulmonary diseases, and the one used for common sense no ledge enhanced embeddings to sol e pronoun disambiguation problems On the other hand, corpora are usually de eloped using a number of boo s e g , Shamela , or editions of a particular ne spaper e g , ecently, corpora builders, in particular indi iduals, use the eb to build a ery large corpora in a short time and ith lo cost o e er, e must be cautious hen dra ing data from the eb, especially if the aim is to build a balanced corpus in hich the language data ha e to be dra n from a ide range of sources egarding our sur ey, e proposed ell no n sources boo s, eb, maga ines , offering the possibility to select multiple choices or adding ne sources that are not listed The results are presented in Table
Table The used sources to build corpora
Sources of corpora umber of corpora boo s boo s, Official rints, e spapers, Maga ines Dictionaries uman generated e spapers ecords ideo subtitles eb eb, boo s, Official rints, Old Manuscripts, e spapers, Maga ines eb, uman generated eb, e spapers, Maga ines Total
Text encoding or mar up is one of the main tas s during data collection Typically, corpora consist of electronic ersions of texts ta en from arious sources Therefore, a confusion may arise due to different codes used for mar up Since the s, the Standard enerali ed Mar up anguage S M has become increasingly accepted as a standard ay of encoding electronic texts sing S M is considered as a basis for data preparation it facilitates the portability of corpora, enabling them to be reused in different contexts on different e uipment, thus sa ing the cost of repeated typesetting Since S M can be complex for some corpora builders and users, an Extensible Mar up anguage M that as deri ed from S M contains a limited feature set to ma e it simpler to use mong the different M standards, the formation of the Text Encoding nitiati e TE b has to be seen as the first, the most comprehensi e, and mature encoding standard for machine readable texts Since its launch in , the TE has become the reference platform for the representation of machine readable texts TE aims to encode as
b http tei c org Imad Zeroual et al. / Procedia Computer Science 127 (2018) 82–91 85 – –
ith respect to the phenomena under in estigation f there are no specific criteria, the corpus should be many possi le iews e. ., te t components as physical, typo raphical or lin istic o ects of a te t as possi le. he designed for a general use to suit most applications. In the 90’s, primarily with the completion of the ritish I , which was the c rrent e ition at the time this article was first s mitte , was release on o em er , 00 . ational orpus , basic guidelines in corpus design and compilation ha e been set by rele ant specialists – ater, expanded these guidelines in ten fundamental criteria hich are considered as core principles such as representati eness, balance, and homogeneity ased on these design criteria, corpora builders should identify the data source genres and si e The selection and he earlier corpora that occ rre efore the 9 0s are calle pre electronic eca se they were not comp teri e . finding suitable resources is much more complicated or example, as stressed by , the components of he roots of e elopin a corp s can e trace ac to 9 when the erman lin ist assem le a lar e general corpora typically are representati e of arious genres, hilst speciali ed corpora can be limited to highlight corp s that contains million wor s . ase on this corp s, co l p lish the first nown wor only one genre or a family of genres egarding the si e and balance, claims that it is not al ays possible fre ency list sin wor co ntin . In oin so, he nee e the help of o er fi e tho san assistants o er a perio of to collect data in similar or e en sufficient uantities for each text category represented in the corpus this is often years to process the corp s , 9 . owe er, the first corp s to e i en that name was the rown orp s 0, , the case ith historical corpora o e er, se eral sources can be used to build corpora corpus, in one hand, may e elope an release in 9 . It consists of te ts amo ntin to o er one million to ens after compilin a sin le consist of a single boo li e the one de eloped by to build an ontology of pulmonary diseases, year of p lication 9 . and the one used for common sense no ledge enhanced embeddings to sol e pronoun disambiguation problems n erstan a ly eno h, il in corpora is time cons min an often iffic lt to n erta e re ar in the si e of On the other hand, corpora are usually de eloped using a number of boo s e g , Shamela , or editions of a ata an the a opte compilation metho s. hro h this s r ey, it was possi le to ather information a o t the particular ne spaper e g , ecently, corpora builders, in particular indi iduals, use the eb to build a ery metho s se to il 00 corpora in relation to their si e an the a era e time cons me rin the compilation large corpora in a short time and ith lo cost o e er, e must be cautious hen dra ing data from the proce re. he p rpose of this st y is not to etermine the est way to il a corp s. Instea , we wo l li e to eb, especially if the aim is to build a balanced corpus in hich the language data ha e to be dra n from a ide now what rele ant metho s are se an how lon they too to complete the proce re. a le e hi its the range of sources o taine res lts. egarding our sur ey, e proposed ell no n sources boo s, eb, maga ines , offering the possibility to select multiple choices or adding ne sources that are not listed The results are presented in Table a le . ompilation metho s of corpora
Table The used sources to build corpora ompilation metho s orpora si e of to ens of corpora era e time cons me
00 years Sources of corpora umber of corpora years months boo s boo s, Official rints, e spapers, Maga ines 0 years months Dictionaries an al 0 years
uman generated 00 years e spapers 00 years ecords n years ideo subtitles eb 00 years
eb, boo s, Official rints, Old Manuscripts, e spapers, Maga ines years eb, uman generated 0 years months eb, e spapers, Maga ines 0 years Total emi a tomate 0 00 years
Text encoding or mar up is one of the main tas s during data collection Typically, corpora consist of electronic 00 0 years ersions of texts ta en from arious sources Therefore, a confusion may arise due to different codes used for mar up Since the s, the Standard enerali ed Mar up anguage S M has become increasingly accepted as n years a standard ay of encoding electronic texts sing S M is considered as a basis for data preparation it facilitates n 0 years months the portability of corpora, enabling them to be reused in different contexts on different e uipment, thus sa ing the 0 years cost of repeated typesetting Since S M can be complex for some corpora builders and users, an Extensible Mar up anguage M that as deri ed from S M contains a limited feature set to ma e it simpler to use 0 years b mong the different M standards, the formation of the Text Encoding nitiati e TE has to be seen as the first, 00 years months the most comprehensi e, and mature encoding standard for machine readable texts Since its launch in , the tomate TE has become the reference platform for the representation of machine readable texts TE aims to encode as 00 years 0 months n years months
n 9 years b http tei c org 86 Imad Zeroual et al. / Procedia Computer Science 127– (2018) 82–91
00 years 9 months
months row so rcin 0 years
n ince 00
amifie approach 00 ays
a le e hi its the s mmary of compilation metho s mentione in the s r ey. e can o ser e that corpora il ers s ally rely on three ma or metho s he first an the ol est metho e er, the man al metho . he ine ita le res lt after the appearance of comp ters, the a tomatic metho . his latter is not completely acc rate, eca se the crawle ata are often plicate on the e an nee to e cleane , filtere , an con erte into the ri ht format. herefore, a man al e ition is s se ently performe . his is the semi a tomatic metho . oncernin the crow so rcin metho , it is catchin p. he crow so rcin was the res lt of the a ancement of i ital technolo y an the Internet in the mi 000s. It is mainly an online so rcin mo el in which in i i als or or ani ations se contri tions from online comm nities for pro lem sol in an reso rces pro ction . ne of the main platforms used in crowdsourcing is Amazon’s Mechanical Turkc e. ., , . he last o ser e metho is the gamified approach or “Gamification”. It is a new comin metho to , it was first mentione in 00 an start to e se in literat re in 0 0 e. ., . y efinition, amification is the se of ame esi n elements in nongame contexts to increase users’ motivations towards given activities . ho h amifie approaches are ase on the same strate y as crow so rcin , in this latter, the participants recei e money to increase moti ation whereas amification incenti i es them y ettin entertaine . It can e s rmise that pro cin a corp s with a consi era le si e an ariety re ires years of efforts re ar less of the se metho . In the conte t of , the first an the most way to collect meanin f l an hi h ality of ata is to hire e pert lin ists to man ally il or annotate corpora howe er, it ta es time, an costs money. he s r ey shows that of man ally ilt corpora contain less than 0 million wor s an it ta es more than fo r years to complete them whereas, of a tomatically ilt corpora with a si e that aries from 00 million to illions are complete in less than the same perio . rther, the a tomatic metho s mar e a consi era le pro ress lately, mainly e to the recent a ances in eep learnin technolo ies e. ., eep ne ral networ s , especially when it comes to ealin with ery lar e ata an access to information. he “Intelli ent ersonal ssistants” li e pple iri, oo le ssistant, icrosoft ortana, ma on cho, etc. are certainly s fficient e amples of the si nificant s ccess achie e sin these metho s. enerally, the semi a tomate metho s are a com ination of oth man al an a tomate metho s, the aim is to raise the ality of ata within a feasi le reasona le time an cost. In oin so, at first sta e, the ata are collecte or annotate a tomatically an later e ite y e perts. It worth mentionin that the shortest time cons me for il in a corp s is achie e y the amifie approach, this metho can e promisin , especially it com ines some ey feat res of the other metho s. or e ample, it is fast as well as an a tomate metho an pro i es oo ality res lts yet, it is ase on the same strate y as crow so rcin , t its cost oes not scale with ata si e.
fter the ata compilation, a set of eneral analyses an te t processin are essential for scientists. enerally, corp s annotation is a process e ote to assi n lin istic feat res to lan a e ata 9 . he annotation implies to se stan ar s, or at least, a well ela orate set of proce res that is wi ely accepte an fre ently se into common corp s creation an processin tools. er the last years, si nificant efforts ha e een p t into e elopin proce res for a tomatic annotation an te t processin . he aim is to reatly re ce the h man inter ention an astly e pan s the empirical asis. hile some corpora il ers attempt to annotate their collecte ata or processin e istin ones, many others still o not other with processin tas s an they st pro ce the corpora in raw format. rther, one of the main
c https www.mt r .com Imad Zeroual et al. / Procedia Computer Science 127 (2018) 82–91 87 – –
00 years 9 months aims eing addressed in is developing new forms of annotation and improving the accurac of automatic annotation. The following tasks are some ke features of corpus annotation the are considered sophisticated as a months row so rcin part of a asic data processing workflow and their design is more contentious. The can e performed manuall 0 years expert linguists to ensure the ualit of annotation or the ma e done automaticall to allow an extensive and n ince 00 difficult anal ses to e carried out. To name a few amifie approach 00 ays • emmatization The purpose of lemmatization process is to reduce surface words to their canonical form called lemma this latter is a dictionar lookup form. The lemma relates different word forms that have the same a le e hi its the s mmary of compilation metho s mentione in the s r ey. e can o ser e that corpora meaning . urther using lemmatization is found to e efficient in particular for information retrieval . il ers s ally rely on three ma or metho s he first an the ol est metho e er, the man al metho . he • art of speech o tagging It is a asic task in corpus linguistics and an underl ing condition to support man ine ita le res lt after the appearance of comp ters, the a tomatic metho . his latter is not completely acc rate, su se uent applications. It aims to assign a morphos ntactical features to each word in a sentence eca se the crawle ata are often plicate on the e an nee to e cleane , filtere , an con erte into the according to the context also it allows simple s ntactic searches to e performed . ri ht format. herefore, a man al e ition is s se ently performe . his is the semi a tomatic metho . oncernin • arsing The natural successor to o tagging is parsing. asicall it provides a dependenc tree as an output. the crow so rcin metho , it is catchin p. he crow so rcin was the res lt of the a ancement of i ital ere the goal is predicting for each sentence or clause an a stract representation of the grammatical entities and technolo y an the Internet in the mi 000s. It is mainly an online so rcin mo el in which in i i als or the relations etween them. onse uentl the parser assigns a full la elled s ntactic tree or racketing of or ani ations se contri tions from online comm nities for pro lem sol in an reso rces pro ction . ne of constituents to sentences of the corpus . the main platforms used in crowdsourcing is Amazon’s Mechanical Turkc e. ., , . he last o ser e metho According to the surve Ta le presents the different annotation processes performed during corpora uilding. is the gamified approach or “Gamification”. It is a new comin metho to , it was first mentione in 00 ote that man corpora ma contain parts of oth annotated texts and raw data. an start to e se in literat re in 0 0 e. ., . y efinition, amification is the se of ame esi n elements in nongame contexts to increase users’ motivations towards given activities . ho h amifie Ta le . T pes of corpora annotation approaches are ase on the same strate y as crow so rcin , in this latter, the participants recei e money to increase moti ation whereas amification incenti i es them y ettin entertaine . It can e s rmise that pro cin a corp s with a consi era le si e an ariety re ires years of efforts aw texts re ar less of the se metho . In the conte t of , the first an the most way to collect meanin f l an hi h Texts with metadata ality of ata is to hire e pert lin ists to man ally il or annotate corpora howe er, it ta es time, an costs artiall annotated money. he s r ey shows that of man ally ilt corpora contain less than 0 million wor s an it ta es more than fo r years to complete them whereas, of a tomatically ilt corpora with a si e that aries from 00 ull annotation million to illions are complete in less than the same perio . rther, the a tomatic metho s mar e a consi era le arsed pro ress lately, mainly e to the recent a ances in eep learnin technolo ies e. ., eep ne ral networ s , emantic annotation especially when it comes to ealin with ery lar e ata an access to information. he “Intelli ent ersonal arts of aw texts and Texts with metadata ssistants” li e pple iri, oo le ssistant, icrosoft ortana, ma on cho, etc. are certainly s fficient arts of aw texts and ull annotation e amples of the si nificant s ccess achie e sin these metho s. enerally, the semi a tomate metho s are a arts of Texts with metadata and artiall annotated com ination of oth man al an a tomate metho s, the aim is to raise the ality of ata within a feasi le reasona le time an cost. In oin so, at first sta e, the ata are collecte or annotate a tomatically an arts of Texts with metadata and ull annotation later e ite y e perts. It worth mentionin that the shortest time cons me for il in a corp s is achie e y the arts of arsed and artiall annotated texts amifie approach, this metho can e promisin , especially it com ines some ey feat res of the other metho s. arts of arsed and ull o annotated texts or e ample, it is fast as well as an a tomate metho an pro i es oo ality res lts yet, it is ase on the same Total strate y as crow so rcin , t its cost oes not scale with ata si e. As seen in Ta le of corpora contain texts with metadata and with the same rate we have corpora with full annotation which means the contain at least lemmatized and o tagged data. A small rate corpora has een totall or partiall parsed and onl one corpus has een semanticall annotated. These results are expected fter the ata compilation, a set of eneral analyses an te t processin are essential for scientists. knowing that parsing and sentiment anal sis re uire an advanced level knowledge of linguistic theories and rules. enerally, corp s annotation is a process e ote to assi n lin istic feat res to lan a e ata 9 . he annotation owever automatic sentiment anal sis has een increased in recent ears due to the rapid data growth in social implies to se stan ar s, or at least, a well ela orate set of proce res that is wi ely accepte an fre ently se networks especiall twitter. into common corp s creation an processin tools. er the last years, si nificant efforts ha e een p t into egarding the data anal sis the are pro a l the most important procedures in data lifec cle that could help to e elopin proce res for a tomatic annotation an te t processin . he aim is to reatly re ce the h man solve issues support theories o serve phenomena extract valua le information and produce useful and relia le inter ention an astly e pan s the empirical asis. applications in academic commercial and pu lic sectors. ext we briefly state the basis analysis used in : hile some corpora il ers attempt to annotate their collecte ata or processin e istin ones, many others still o not other with processin tas s an they st pro ce the corpora in raw format. rther, one of the main • oncordance It aims to search the text and finds ever occurrence of each word and displa s all the occurrences with its immediate context in a corpus. urther oncordances can e produced in several formats ut the most usual form is the e ord in ontext I concordance . The first concordance completed in was c https www.mt r .com that of ugo of aro ominican monk and cardinal died . It has een said that monks to have 88 Imad Zeroual et al. / Procedia Computer Science 127 (2018) 82–91 – –
een engaged upon its preparation. As the asis of their work the used the text of the atin ulgate the standard ner ils n r us linguisti s in urgh in urgh ni ersit ress i le of the Middle Ages in estern urope. in lair reli inar re en ati ns n r us t l g aila le tt www l i nr t r ust r ust t l • ord fre uenc procedure It aims to produce lists of words and their fre uenc in the corpus. Moreover it is i er nra e en r us linguisti s n estigating language stru ture an use a ri ge ni ersit ress feasi le to produce word fre uenc lists using a o tagged corpus not merel their orthographic status. or in lair r us an te t asi rin i les e inguist r ra ui e ra t – example eech et al use the tagged ritish ational orpus to uild the word fre uencies list in written and eling t r us linguisti s an internati nal han alter e ru ter ane harlet aulent uil ing an nt l g ul nar iseases with natural language r essing t ls using te tual spoken nglish. r ra nt e n – • ollocation statistics It is a procedure to calculate statistical information a out the association the strength of iua iang inga hu ei ua nsense n wle ge nhan e e ings r l ing r n un collocation and the comparative fre uencies of word forms in a corpus or multiple corpora. nlike the previous isa iguati n r le s in in gra he a hallenge r i re r r i elin agi w an h i an el ha ela arge ale ist ri al ra i r us r i re r anal tical procedures collocation re uires a igsized corpus. therwise the anal st cannot cope with the r i availa le data. aa uri ies uli a e he e i r una u iri agh uani ra i ree an art In addition deeper anal sis lead to produce applications that help performing sophisticated tasks effectivel a e as a r us ing e n the n gra n ussian u er h l in n r ati n etrie al – ringer that no individual or using knowledge ased methods can do etter not to mention that the could re uire ears or ar he e t n ing nitiati e ears a u ulate wis an its tential r a right uture n anguage decades of efforts. As examples of those applications Information etrieval I summarization and Machine e hn l gies igital u anities Translation MT . appl ing I we can search and retrieve relevant information re uired among enormous data h rshee lha i siri e el ing t ewritten ra i r us with ulti nts n r ee ings the e.g. search engines even if the source documents are written in a language while the user’s ueries are in nternati nal r sh n ultilingual ngers he ist r an rin i les a ular ntr l as it a e ts in general an nglish in arti ular he ist another one . ummarization anal sis can turn large num ers of documents into shorter versions without i changing meaning of the original texts. This is useful for a uestion answering s stem whereas the summarization enne n intr u ti n t r us linguisti s utle ge generates a sort of summar of the answer primaril if the uestion is wh or how t pe uestion e.g. . n the ran is u era re uen anal sis nglish usage other hand the enefit from online education is still limited language arriers since the content is usuall unst n r us inguisti s ist ri al e el ent n l l inguist ra ha r w s ur ing ile nline i rar generated in nglish. Machine translation can ridge the language gap providing an initial translation which can l a rus hwit reating language res ur es r un er res ur e languages eth l gies an e eri ents with e later postedited translators e.g. . ra i ang es ur al – a ernal ure h hi h argu ent is re n in ing nal ing an re i ting n in ingness e argu ents using i ire ti nal n r ee ings the th nnual eeting the ss iati n r utati nal inguisti s 3. Conclusion er ual l ah a h ua a a i i ati n r ra i atural anguage r essing eas int ra ti e rans a h earn rti ntell In this paper we have riefl presented distinct stages in the lifec cle of data in . The goal of this overview Lund, L., O’Regan, P.: Gamifying Natural Language uisiti n uantitati e stu n we ish ant n s while e a ining the e e ts nsensus ri en rewar s is to help readers to etter understand the data design collection annotation and anal sis procedures taking an ia ee ee uil ing a senti ent r us using a ga i ie ra ew r n u an i an te hn l g n r ati n experiment from alread pu lished corpora of the most prominent pro ects of resource rich–poor languages. In order e hn l g uni ati n an ntr l n ir n ent an anage ent nternati nal n eren e n – to make the surve rich in information we targeted wellknown corpora in the literature and those pu licl availa le et we covered man languages in purpose to ensure that the surve is alanced as much as possi le. eter ing i n hale a e r ga e esign ele ents t ga e ulness e ining ga i i ati n n r ee ings the th internati nal a a e i in re n eren e n isi ning uture e ia en ir n ents – inall this overview is alwa s a su ect to make further progress and more detailed descriptions and to a critical arsi e ee h ner r us ann tati n linguisti in r ati n r uter te t r ra a l r ran is understanding of the conceptual technical and practical scope of data in light of . ttia an ena ith ell ish i ti nar r ra i n le tr ni le i gra h in the st entur thin ing utsi e the a er r ee ings the e e n eren e t er allinn st nia – er ual a h ua a ra i in r ati n retrie al te ing r le ati ati n n ntelligent ste s an uter isi n – e r References er ual a h ua a elah i war s a stan ar art ee h tagset r the ra i language ing au ni ut n i – . Armstrong . hurch . Isa elle . Manzi . Tzoukermann . arowsk . atural language processing using ver large corpora. sar at e ah ler i re arsing r h l gi all ri h languages ntr u ti n t the s e ial issue ut pringer cience usiness Media . inguist – . u . others Introducing corpus ased translation studies. pringer . ri le n r an ing ile nline i rar . efever . oste . emeval task rosslingual word sense disam iguation. roc em val. – . ee h a s n thers r re uen ies in written an s en nglish ase n the ritish ati nal r us utle ge . i . orascu . l a M. Giannakopoulos G. Multidocument multilingual summarization corpus preparation part Ara ic r t et ler tr h an ear h engines n r ati n retrie al in ra ti e is n esle ea ing english greek chinese romanian. resented at the . hatta har a al ar ar uer ranslati n r r ss anguage n r ati n etrie al using ultilingual r lusters . ing . ong . . hao . . eal A. . . chmaltz M. u . ntaxtree aligner A we ased parallel tree alignment toolkit. In avelet Anal sis and attern ecognition I A International onference on. pp. – . I . a a a lath .K., Lasantha, W.A.N., Premachandra, K., Nugaliyadde, A., Mallawarachchi, Y.: “Mahoshadha”, the Sinhala . othman . ingland . adford . Murph T. urran . . earning multilingual named entit recognition from ikipedia. Artif. agge r us ase uesti n nswering ste n r ee ings irst nternati nal n eren e n n r ati n an uni ati n Intell. – . e hn l g r ntelligent ste s lu e – ringer . inclair . Intuition and annotation–the discussion continues. ang. omput. – . ansen l ala u an ara sustaina le gl al s luti n r a essi ilit were unities lunteers n . allida M. Matthiessen .M. Matthiessen . An introduction to functional grammar. outledge . nternati nal n eren e n ni ersal ess in u an uter ntera ti n – ringer . Milfull I. Mutual Illumination The ictionar of ld nglish and the ngoing evision of the xford nglish ictionar . lorilegium. – . . ation I. . . Teaching learning voca ular . oston einle engage earning . . irsch erg . Manning . . Advances in natural language processing. cience. – . Appendix: Corpora of the survey . Manning . . artofspeech tagging from to is it time for some linguistics In International onference on Intelligent Text rocessing and omputational inguistics. pp. – . pringer . Corpora language(s) Corpus reference (URL or DOI) . eech G. orpora and theories of linguistic performance. ir. orpus inguist. – . l a at ra i r us ra i htt atal g elra in r u t in h r u ts i . all . . Automated text anal sis autionar tales. it. inguist. omput. – . l in ree an ut h htt ur let rug nl ann r trees . eech G. million words of nglish the ritish ational orpus . ang. es. – . ara languages htt alt ri rg res ur es e r us ra uis ra i nglish an ren h htt www a lwe rg anth l g Imad Zeroual et al. / Procedia Computer Science 127 (2018) 82–91 89 – –
een engaged upon its preparation. As the asis of their work the used the text of the atin ulgate the standard ner ils n r us linguisti s in urgh in urgh ni ersit ress i le of the Middle Ages in estern urope. in lair reli inar re en ati ns n r us t l g aila le tt www l i nr t r ust r ust t l • ord fre uenc procedure It aims to produce lists of words and their fre uenc in the corpus. Moreover it is i er nra e en r us linguisti s n estigating language stru ture an use a ri ge ni ersit ress feasi le to produce word fre uenc lists using a o tagged corpus not merel their orthographic status. or in lair r us an te t asi rin i les e inguist r ra ui e ra t – example eech et al use the tagged ritish ational orpus to uild the word fre uencies list in written and eling t r us linguisti s an internati nal han alter e ru ter ane harlet aulent uil ing an nt l g ul nar iseases with natural language r essing t ls using te tual spoken nglish. r ra nt e n – • ollocation statistics It is a procedure to calculate statistical information a out the association the strength of iua iang inga hu ei ua nsense n wle ge nhan e e ings r l ing r n un collocation and the comparative fre uencies of word forms in a corpus or multiple corpora. nlike the previous isa iguati n r le s in in gra he a hallenge r i re r r i elin agi w an h i an el ha ela arge ale ist ri al ra i r us r i re r anal tical procedures collocation re uires a igsized corpus. therwise the anal st cannot cope with the r i availa le data. aa uri ies uli a e he e i r una u iri agh uani ra i ree an art In addition deeper anal sis lead to produce applications that help performing sophisticated tasks effectivel a e as a r us ing e n the n gra n ussian u er h l in n r ati n etrie al – ringer that no individual or using knowledge ased methods can do etter not to mention that the could re uire ears or ar he e t n ing nitiati e ears a u ulate wis an its tential r a right uture n anguage decades of efforts. As examples of those applications Information etrieval I summarization and Machine e hn l gies igital u anities Translation MT . appl ing I we can search and retrieve relevant information re uired among enormous data h rshee lha i siri e el ing t ewritten ra i r us with ulti nts n r ee ings the e.g. search engines even if the source documents are written in a language while the user’s ueries are in nternati nal r sh n ultilingual ngers he ist r an rin i les a ular ntr l as it a e ts in general an nglish in arti ular he ist another one . ummarization anal sis can turn large num ers of documents into shorter versions without i changing meaning of the original texts. This is useful for a uestion answering s stem whereas the summarization enne n intr u ti n t r us linguisti s utle ge generates a sort of summar of the answer primaril if the uestion is wh or how t pe uestion e.g. . n the ran is u era re uen anal sis nglish usage other hand the enefit from online education is still limited language arriers since the content is usuall unst n r us inguisti s ist ri al e el ent n l l inguist ra ha r w s ur ing ile nline i rar generated in nglish. Machine translation can ridge the language gap providing an initial translation which can l a rus hwit reating language res ur es r un er res ur e languages eth l gies an e eri ents with e later postedited translators e.g. . ra i ang es ur al – a ernal ure h hi h argu ent is re n in ing nal ing an re i ting n in ingness e argu ents using i ire ti nal n r ee ings the th nnual eeting the ss iati n r utati nal inguisti s 3. Conclusion er ual l ah a h ua a a i i ati n r ra i atural anguage r essing eas int ra ti e rans a h earn rti ntell In this paper we have riefl presented distinct stages in the lifec cle of data in . The goal of this overview Lund, L., O’Regan, P.: Gamifying Natural Language uisiti n uantitati e stu n we ish ant n s while e a ining the e e ts nsensus ri en rewar s is to help readers to etter understand the data design collection annotation and anal sis procedures taking an ia ee ee uil ing a senti ent r us using a ga i ie ra ew r n u an i an te hn l g n r ati n experiment from alread pu lished corpora of the most prominent pro ects of resource rich–poor languages. In order e hn l g uni ati n an ntr l n ir n ent an anage ent nternati nal n eren e n – to make the surve rich in information we targeted wellknown corpora in the literature and those pu licl availa le et we covered man languages in purpose to ensure that the surve is alanced as much as possi le. eter ing i n hale a e r ga e esign ele ents t ga e ulness e ining ga i i ati n n r ee ings the th internati nal a a e i in re n eren e n isi ning uture e ia en ir n ents – inall this overview is alwa s a su ect to make further progress and more detailed descriptions and to a critical arsi e ee h ner r us ann tati n linguisti in r ati n r uter te t r ra a l r ran is understanding of the conceptual technical and practical scope of data in light of . ttia an ena ith ell ish i ti nar r ra i n le tr ni le i gra h in the st entur thin ing utsi e the a er r ee ings the e e n eren e t er allinn st nia – er ual a h ua a ra i in r ati n retrie al te ing r le ati ati n n ntelligent ste s an uter isi n – e r References er ual a h ua a elah i war s a stan ar art ee h tagset r the ra i language ing au ni ut n i – . Armstrong . hurch . Isa elle . Manzi . Tzoukermann . arowsk . atural language processing using ver large corpora. sar at e ah ler i re arsing r h l gi all ri h languages ntr u ti n t the s e ial issue ut pringer cience usiness Media . inguist – . u . others Introducing corpus ased translation studies. pringer . ri le n r an ing ile nline i rar . efever . oste . emeval task rosslingual word sense disam iguation. roc em val. – . ee h a s n thers r re uen ies in written an s en nglish ase n the ritish ati nal r us utle ge . i . orascu . l a M. Giannakopoulos G. Multidocument multilingual summarization corpus preparation part Ara ic r t et ler tr h an ear h engines n r ati n retrie al in ra ti e is n esle ea ing english greek chinese romanian. resented at the . hatta har a al ar ar uer ranslati n r r ss anguage n r ati n etrie al using ultilingual r lusters . ing . ong . . hao . . eal A. . . chmaltz M. u . ntaxtree aligner A we ased parallel tree alignment toolkit. In avelet Anal sis and attern ecognition I A International onference on. pp. – . I . a a a lath .K., Lasantha, W.A.N., Premachandra, K., Nugaliyadde, A., Mallawarachchi, Y.: “Mahoshadha”, the Sinhala . othman . ingland . adford . Murph T. urran . . earning multilingual named entit recognition from ikipedia. Artif. agge r us ase uesti n nswering ste n r ee ings irst nternati nal n eren e n n r ati n an uni ati n Intell. – . e hn l g r ntelligent ste s lu e – ringer . inclair . Intuition and annotation–the discussion continues. ang. omput. – . ansen l ala u an ara sustaina le gl al s luti n r a essi ilit were unities lunteers n . allida M. Matthiessen .M. Matthiessen . An introduction to functional grammar. outledge . nternati nal n eren e n ni ersal ess in u an uter ntera ti n – ringer . Milfull I. Mutual Illumination The ictionar of ld nglish and the ngoing evision of the xford nglish ictionar . lorilegium. – . . ation I. . . Teaching learning voca ular . oston einle engage earning . . irsch erg . Manning . . Advances in natural language processing. cience. – . Appendix: Corpora of the survey . Manning . . artofspeech tagging from to is it time for some linguistics In International onference on Intelligent Text rocessing and omputational inguistics. pp. – . pringer . Corpora language(s) Corpus reference (URL or DOI) . eech G. orpora and theories of linguistic performance. ir. orpus inguist. – . l a at ra i r us ra i htt atal g elra in r u t in h r u ts i . all . . Automated text anal sis autionar tales. it. inguist. omput. – . l in ree an ut h htt ur let rug nl ann r trees . eech G. million words of nglish the ritish ational orpus . ang. es. – . ara languages htt alt ri rg res ur es e r us ra uis ra i nglish an ren h htt www a lwe rg anth l g 90 Imad Zeroual et al. / Procedia Computer Science 127 (2018) 82–91 –
. df age Ara ic nglish Parallel News Ara ic, nglish htt s: catalog.ldc.u enn.edu L Ara ic ree an Ara ic htt s: catalog.ldc.u enn.edu L Aranea We or ora languages htt : unesco.uni a.s guest AR A ROMANS AL nglish, rench, talian htt : catalog.elra.info roduct info. h roducts id AR R nglish htt : www. ro ects.alc.manchester.ac.u archer ar en en Ara ic htt s: www.s etchengine.co.u artenten cor us A rench, nglish htt : rali.iro.umontreal.ca rali fr A oL talian nglish htt : cor ora.ficlit.uni o.it OL nglish, hinese htt s: catalog.ldc.u enn.edu L rown nglish htt : clu.uni.no icame rown cm.html ul ree an ulgarian htt : www. ultree an .org L nglish, German, utch htt s: catalog.ldc.u enn.edu L L rish, Latin, nglish, rench, S anish, L htt : www.ucc.ie celt talian, Pro en al, utch, anish MP lico Portuguese htt : www.linguateca. t MPu lico N L or us Portuguese htt : cintil.ul. t on ote nglish htt : www.cs.cornell.edu home llee data con ote.html ORGA Galician htt : cor us.cir .es corga OR S talian htt : cor ora.ficlit.uni o.it OR S O S talian htt : cor ora.ficlit.uni o.it Gree nglish, ulgarian nglish, htt : dl.acm.org citation.cfm id or ora for e ontent rofessionals Slo ene nglish, and Ser ian nglish OK N or us S R N OS S anish htt s: i an ladimir.githu .io sitio cor us ironia or us del s a ol S anish htt : www.cor usdeles anol.org hist gen or us del s a ol We S anish htt : www.cor usdeles anol.org we dial or us do Portugu s Portuguese htt : www.cor usdo ortugues.org hist gen or us do Portugu s We Portuguese htt : www.cor usdo ortugues.org we dial or us of S o en Lithuanian Lithuanian htt : donelaitis. du.lt sa ytines al os te stynas RA R nglish, rench, S anish htt : catalog.elra.info roduct info. h roducts id R A S anish cor us.rae.es creanet.html roatian National or us roatian . hinese, nglish, Gree , Polish, and aniel cor us . . . . Russian eReKo German htt : www .ids mannheim.de l ro e te dere o i.html deWa German htt : wac y.sslmit.uni o.it ia OR S talian htt : cor ora.ficlit.uni o.it M A or us languages htt : o us.lingfil.uu.se M A. h nglish Gigaword nglish htt s: catalog.ldc.u enn.edu ldc t uro arl uro ean languages htt : www.statmt.org euro arl rante t rench htt : www.frante t.fr frWa rench htt : wac y.sslmit.uni o.it Ge RePa German, and rench htt : catalog.elra.info roduct info. h roducts id G N A nglish htt : www.nactem.ac.u meta nowledge download. h Glo al nglish Monitor or us nglish htt : www.cor us. ham.ac.u ccl glo al.htm G M Georgtown ni ersity nglish htt s: cor ling.uis.georgetown.edu gum Multilayer cor us elsin i or us nglish htt : www.helsin i.fi arieng oR cor ora elsin i or us G nglish htt : www.ucl.ac.u english usage ro ects ice g nternational or us of nglish nglish htt : www.ucl.ac.u english usage ro ects ice.htm nternational or us of Learner htt : www.fltr.ucl.ac. e fltr germ etan cecl ecl languages nglish L Pro ects cle icle.htm N RS nglish, rench, German htt : arts. righton.ac.u staff raf sal ie intersect itWa talian htt : wac y.sslmit.uni o.it KA S Ara ic . s KA a h e endency ree an Ka a h htt s: githu .com ni ersal e endencies Ka a h Ka a h Language or us Ka a h htt : a cor us. lcwe en Korean National or us Korean htt : www.se ong.or. r Kor us K anish htt : ordnet.d or usd en set language en KS A Ara ic htt : sucor us. su.edu.sa Lancaster Parsed or us nglish htt : clu.uni.no icame lan e s.html htt : uni ersal.elra.info roduct info. h cPath ro Lancaster Leeds ree an nglish ducts id LASSY utch htt : odur.let.rug.nl annoord Lassy Imad Zeroual et al. / Procedia Computer Science 127 (2018) 82–91 91 – –
. df age L A Mandarin hinese htt : www.li ac.org Ara ic nglish Parallel News Ara ic, nglish htt s: catalog.ldc.u enn.edu L Malay oncordance Pro ect Malay htt : mc .anu.edu.au htt : www.cs.cornell.edu eo le a o mo ie re iew Ara ic ree an Ara ic htt s: catalog.ldc.u enn.edu L Mo ie Re iew ata nglish Aranea We or ora languages htt : unesco.uni a.s guest data Ara ic, hinese, ech, nglish, AR A ROMANS AL nglish, rench, talian htt : catalog.elra.info roduct info. h roducts id MultiLing Multilingual Multi htt : multiling.iit.demo ritos.gr ages iew tas mms rench, Gree , e rew, indi, AR R nglish htt : www. ro ects.alc.manchester.ac.u archer ocument Summari ation or us multi document summari ation data and information Romanian, S anish ar en en Ara ic htt s: www.s etchengine.co.u artenten cor us nglish, rench, S anish, Ara ic, A rench, nglish htt : rali.iro.umontreal.ca rali fr A Multi N htt : www.euromatri lus.net multi un Russian, hinese, German oL talian nglish htt : cor ora.ficlit.uni o.it OL nglish, hinese htt s: catalog.ldc.u enn.edu L Negra German htt : www.coli.uni saarland.de ro ects sf negra cor us rown nglish htt : clu.uni.no icame rown cm.html N MLAR or us Ara ic htt : www.rdi eg.com Pro ects nemlar.htm ul ree an ulgarian htt : www. ultree an .org OntoNotes nglish, hinese, Ara ic htt s: catalog.ldc.u enn.edu L L nglish, German, utch htt s: catalog.ldc.u enn.edu L L rish, Latin, nglish, rench, S anish, PANA A nglish, rench, Gree htt : catalog.elra.info roduct info. h roducts id L htt : www.ucc.ie celt talian, Pro en al, utch, anish Parallela nglish Polish htt : aralela.clarin l.eu MP lico Portuguese htt : www.linguateca. t MPu lico Par nglish, rench, talian htt s: githu .com msang artut re o N L or us Portuguese htt : cintil.ul. t Penn ree an nglish htt s: catalog.ldc.u enn.edu ldc t on ote nglish htt : www.cs.cornell.edu home llee data con ote.html Polarity nglish . N M. . ORGA Galician htt : cor us.cir .es corga P PARL or us Portuguese htt : catalog.elra.info roduct info. h roducts id OR S talian htt : cor ora.ficlit.uni o.it OR S O S talian htt : cor ora.ficlit.uni o.it Russian collection Russian htt : cor us.leeds.ac.u ruscor ora.html Gree nglish, ulgarian nglish, htt : dl.acm.org citation.cfm id Russian National or us Russian htt : www.ruscor ora.ru or ora for e ontent rofessionals Slo ene nglish, and Ser ian nglish OK N North Saami, South Saami, Aanaar S KOR htt : gtwe .uit.no or c lang en or us S R N OS S anish htt s: i an ladimir.githu .io sitio cor us ironia Saami, Lule Saami, S olt Saami or us del s a ol S anish htt : www.cor usdeles anol.org hist gen or us del s a ol We S anish htt : www.cor usdeles anol.org we dial Sinica hinese htt : c i .iis.sinica.edu.tw K P eng ersion cor us.htm or us do Portugu s Portuguese htt : www.cor usdo ortugues.org hist gen Slo a National or us Slo a htt : or us. uls.sa a.s or us do Portugu s We Portuguese htt : www.cor usdo ortugues.org we dial Syntactic ata ase for modern or us of S o en Lithuanian Lithuanian htt : donelaitis. du.lt sa ytines al os te stynas S anish htt : www. ds.usc.es S anish S RA R nglish, rench, S anish htt : catalog.elra.info roduct info. h roducts id R A S anish cor us.rae.es creanet.html he American National or us nglish htt : www.anc.org roatian National or us roatian . hinese, nglish, Gree , Polish, and he an of nglish nglish htt : www .lingsoft.fi doc engcg an of nglish.html aniel cor us . . . . Russian eReKo German htt : www .ids mannheim.de l ro e te dere o i.html he ritish National or us nglish htt : www.natcor .o .ac.u deWa German htt : wac y.sslmit.uni o.it he ech National or us ech htt s: www. or us.c ia OR S talian htt : cor ora.ficlit.uni o.it M A or us languages htt : o us.lingfil.uu.se M A. h he ellenic National or us Gree htt : hnc.ils .gr find.as nglish Gigaword nglish htt s: catalog.ldc.u enn.edu ldc t uro arl uro ean languages htt : www.statmt.org euro arl he hungarian gigaword cor us ungarian htt : cor us.nytud.hu mns inde eng.html rante t rench htt : www.frante t.fr frWa rench htt : wac y.sslmit.uni o.it he nternational or us of Ara ic Ara ic htt : www. i ale .org ica Ge RePa German, and rench htt : catalog.elra.info roduct info. h roducts id he Lancaster or us of Mandarin htt : www.lancaster.ac.u fass ro ects cor us L M defaul hinese G N A nglish htt : www.nactem.ac.u meta nowledge download. h hinese t.htm Glo al nglish Monitor or us nglish htt : www.cor us. ham.ac.u ccl glo al.htm G M Georgtown ni ersity he National or us of Polish Polish htt : n . l nglish htt s: cor ling.uis.georgetown.edu gum Multilayer cor us he New Yor imes Annotated nglish htt s: catalog.ldc.u enn.edu L elsin i or us nglish htt : www.helsin i.fi arieng oR cor ora elsin i or us or us G nglish htt : www.ucl.ac.u english usage ro ects ice g htt : www.ims.uni G R or us German nternational or us of nglish nglish htt : www.ucl.ac.u english usage ro ects ice.htm stuttgart.de forschung ressourcen or ora tiger.html nternational or us of Learner htt : www.fltr.ucl.ac. e fltr germ etan cecl ecl languages nglish L Pro ects cle icle.htm PS R om lete nglish htt s: catalog.ldc.u enn.edu L A N RS nglish, rench, German htt : arts. righton.ac.u staff raf sal ie intersect htt : www.sfs.uni a tree an German itWa talian htt : wac y.sslmit.uni o.it tue ingen.de en ascl resources cor ora tue a d .html KA S Ara ic . s ur ish National or us ur ish htt : www.tnc.org.tr KA a h e endency ree an Ka a h htt s: githu .com ni ersal e endencies Ka a h htt s: www.u .tu darmstadt.de data argumentation Ka a h Language or us Ka a h htt : a cor us. lcwe en KP on Arg nglish Korean National or us Korean htt : www.se ong.or. r mining u con arg cor us Kor us K anish htt : ordnet.d or usd en set language en u Wa nglish htt : wac y.sslmit.uni o.it KS A Ara ic htt : sucor us. su.edu.sa Lancaster Parsed or us nglish htt : clu.uni.no icame lan e s.html Wi i edia: ata ase More than languages htt s: en.wi i edia.org wi i Wi i edia: ata ase download htt : uni ersal.elra.info roduct info. h cPath ro W languages htt s: wit .f .eu Lancaster Leeds ree an nglish ducts id WO A nglish htt : wor sho .coli s.org wochat LASSY utch htt : odur.let.rug.nl annoord Lassy