@inproceedings{nicolas2021,

title = {An Experiment on Implicitly Crowdsourcing Expert Knowledge about Romanian Synonyms from Language Learners},

author = {Lionel Nicolas and Lavinia Nicoleta Aparaschivei and Verena Lyding and Christos Rodosthenous and Federico Sangati and Alexander König and Corina Forascu},

editor = {David Alfter and Elena Volodina and Ildikó Pilán and Johannes Graënand Lars Borin},

url = {https://ep.liu.se/ecp/177/001/ecp2021177001.pdf},

year  = {2021},

date = {2021-05-31},

booktitle = {Proceedings of the 10th Workshop on Natural Language Processing for Computer Assisted Language Learning (NLP4CALL 2021)},

journal = {Proceedings of the 10th Workshop on Natural Language Processing for Computer Assisted Language Learning (NLP4CALL 2021)},

volume = {47},

pages = {1-14},

publisher = {Linköping Electronic Conference Proceedings},

keywords = {crowdsourcing, enetCollect, Language learning},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{nicolas-etal-2020-creating,

title = {Creating Expert Knowledge by Relying on Language Learners: a Generic Approach for Mass-Producing Language Resources by Combining Implicit Crowdsourcing and Language Learning},

author = {Lionel Nicolas and Verena Lyding and Claudia Borg and Corina Forascu and Karën Fort and Katerina Zdravkova and Iztok Kosem and Jaka Čibej and Špela Arhar Holdt and Alice Millour and Alexander König and Christos Rodosthenous and Federico Sangati and Umair ul Hassan and Anisia Katinskaia and Anabela Barreiro and Lavinia Aparaschivei and Yaakov HaCohen-Kerner},

url = {https://www.christosrodosthenous.info/wp-content/uploads/2020/05/2020.lrec-1.34-1.pdf

https://www.aclweb.org/anthology/2020.lrec-1.34},

isbn = {979-10-95546-34-4},

year  = {2020},

date = {2020-01-01},

booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference},

pages = {268--278},

publisher = {European Language Resources Association},

address = {Marseille, France},

abstract = {We introduce in this paper a generic approach to combine implicit crowdsourcing and language learning in order to mass-produce language resources (LRs) for any language for which a crowd of language learners can be involved. We present the approach by explaining its core paradigm that consists in pairing specific types of LRs with specific exercises, by detailing both its strengths and challenges, and by discussing how much these challenges have been addressed at present. Accordingly, we also report on on-going proof-of-concept efforts aiming at developing the first prototypical implementation of the approach in order to correct and extend an LR called ConceptNet based on the input crowdsourced from language learners. We then present an international network called the European Network for Combining Language Learning with Crowdsourcing Techniques (enetCollect) that provides the context to accelerate the implementation of this generic approach. Finally, we exemplify how it can be used in several language learning scenarios to produce a multitude of NLP resources and how it can therefore alleviate the long-standing NLP issue of the lack of LRs.},

keywords = {crowdsourcing, enetCollect, Language learning},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{rodosthenous-etal-2020-using,

title = {Using Crowdsourced Exercises for Vocabulary Training to Expand ConceptNet},

author = {Christos Rodosthenous and Verena Lyding and Federico Sangati and Alexander König and Umair ul Hassan and Lionel Nicolas and Jolita Horbacauskiene and Anisia Katinskaia and Lavinia Aparaschivei},

url = {https://www.christosrodosthenous.info/wp-content/uploads/2020/05/2020.lrec-1.38.pdf

https://www.aclweb.org/anthology/2020.lrec-1.38},

isbn = {979-10-95546-34-4},

year  = {2020},

date = {2020-01-01},

booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference},

pages = {307--316},

publisher = {European Language Resources Association},

address = {Marseille, France},

abstract = {In this work, we report on a crowdsourcing experiment conducted using the V-TREL vocabulary trainer which is accessed via a Telegram chatbot interface to gather knowledge on word relations suitable for expanding ConceptNet. V-TREL is built on top of a generic architecture implementing the implicit crowdsourding paradigm in order to offer vocabulary training exercises generated from the commonsense knowledge-base ConceptNet and -- in the background -- to collect and evaluate the learners' answers to extend ConceptNet with new words. In the experiment about 90 university students learning English at C1 level, based on Common European Framework of Reference for Languages (CEFR), trained their vocabulary with V-TREL over a period of 16 calendar days. The experiment allowed to gather more than 12,000 answers from learners on different question types. In this paper we present in detail the experimental setup and the outcome of the experiment, which indicates the potential of our approach for both crowdsourcing data as well as fostering vocabulary skills.},

keywords = {ConceptNet, enetCollect, Language learning},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{enetcollect1,

title = {Designing a Prototype Architecture for Crowdsourcing Language Resources},

author = {Christos Rodosthenous and Verena Lyding and Alexander König and Jolita Horbacauskiene and Anisia Katinskaia and Umair ul Hassan and Nicos Isaak and Federico Sangati and Lionel Nicolas},

editor = {Thierry Declerck and John P. McCrae},

url = {http://ceur-ws.org/Vol-2402/paper4.pdf},

year  = {2019},

date = {2019-07-10},

booktitle = { Proceedings of the Poster Session of the 2nd Conference on Language, Data and Knowledge (LDK 2019)},

volume = {Vol-2402},

pages = {17--23},

publisher = {CEUR},

abstract = {We present an architecture for crowdsourcing 

language resources from language learners and a prototype implementation of it as a vocabulary trainer. The vocabulary trainer relies on lexical resources from the ConceptNet semantic network to generate exercises while using the learners' answers to improve the resources used for the exercise generation.},

keywords = {Commonsense Knowledge, ConceptNet, crowdsourcing, enetCollect, Knowledge Bases, Language learning, Language Resources, Lexicon},

pubstate = {published},

tppubtype = {inproceedings}

}