EnetCollect-related projects
This page provides the list of funded projects related to enetCollect, some of which received support from enetCollect at proposal time.
Callector
Callector is a Geneva University project funded by the Swiss National Science Foundation under its COST program. The project will be closely linked with the enetCollect COST project. Its goal is to develop a platform for crowdsourced construction of speech-enabled CALL content. This content will initially be hosted using Geneva University’s CALL-SLT Lite, developed under a previous SNSF project. The project officially started on April 1 2018 and will continue until December 31 2021. Callector received support from enetCollect at proposal time.
External links to the project
Official website
Dedicated googlegroup
Contact person in enetCollect for this project:
Manny Rayner
The Multimodal Child Language Acquisition
The Multimodal Child Language Acquisition is a network of researchers from the University of Copenhagen and from the Chinese University of Hong Kong and the Hong Kong University. It is founded by the Danish Research Councils with start in March 2019 and end in February 2021. The network will mainly address research related to the use and role of gestures in child language acquisition focusing on bilingual children (English/Chinese, Danish/Chinese, Danish/English), but themes related to enetCollect, such as the analysis of code switching and code mixing in the various language combinations using NLP, and the use of crowdsourcing will also be addressed.
External links to the project
Not available yet.
Contact person in enetCollect for this project:
Costanza Navarretta
Large-Scale Information Extraction and Gamification for Crowdsourced Language Learning
The project aims at contributing to the activities of COST Action CA 16105 – enetCollect – in the areas related to large-scale information extraction from the web and gamification of the preparation and the annotation of multimedia content suitable for various language learning activities. We will explore advanced methods for web-scale crawling and semantic enrichment of the collected material as well as modern human-computer interaction technologies engaging users in the process of acquisition of languages in the form of a game.
External links to the project
Grant page
Project page
Contact person in enetCollect for this project:
Pavel Smrz
From parallel corpora to multilingual exercises – Making use of large text collections and crowdsourcing techniques for innovative autonomous language learning applications
Over the last few decades, there has been a shift in language teaching, from focused grammar and vocabulary learning to more intuitive use of the foreign language through examples. Large collections of translated texts, which are increasingly freely available, provide language learners with a variety of examples of word usage and grammar. The aim of the project is to find out which of these translations are most suitable for this purpose. To do so, we will combine findings from other areas with the judgments of language learners and teachers. We will take advantage of the qualities that a good example should have to automatically find appropriate translations in large collections of text and to generate exercises.
External links to the project
Grant page
Contact person in enetCollect for this project:
LegiCrowd – Sharing thoughts on Terms & Conditions
There is an ongoing trail of research, projects, standards… by actors from a wide range of academic and industrial fields including philosophers, computer scientists, lawyers, mathematicians, linguists, economists… regarding the transparency of Websites’ Terms & Conditions agreements. This project is an attempt to collect and publish information about ToS Transparency efforts among and across various fields. It is also an effort to share information, opinions, thoughts among researchers and, more widely, actors about ToS Transparency and promote collaboration among them.
External links to the project
official website
Contact person in enetCollect for this project:
Alain couillault
Empirical foundations for digitally-supported development of writing skills
The aim of this project is to support teachers who correct and grade student writing. Research has shown that giving individualised, iterative, goal-oriented and formative feedback on writings leads to best literacy results. This requires a lot of time from teachers, which they often don’t have. The project proposes to enhance digital support of teachers’ work by automatic error identification by tools for automatic identificiation and categorization of errors and digitally-supported models of providing feedback. Advances in the field of natural language processing, machine learning, and corpus linguistics make this an attainable goal, which is attested by a variety of digital tools, prototypes, and portals currently being designed.
Contact person in enetCollect for this project:
Spela Arhar Holdt