1 code implementation • 1 Nov 2022 • Haris Mansoor, Sarwan Ali, Shafiq Alam, Muhammad Asad Khan, Umair ul Hassan, Imdadullah Khan
In this paper, we analyze the effect on fairness in the context of graph data (node attributes) imputation using different embedding and neural network methods.
no code implementations • LREC 2020 • Lionel Nicolas, Verena Lyding, Claudia Borg, Corina Forascu, Kar{\"e}n Fort, Katerina Zdravkova, Iztok Kosem, Jaka {\v{C}}ibej, {\v{S}}pela Arhar Holdt, Alice Millour, Alex K{\"o}nig, er, Christos Rodosthenous, Federico Sangati, Umair ul Hassan, Anisia Katinskaia, Anabela Barreiro, Lavinia Aparaschivei, Yaakov HaCohen-Kerner
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.
no code implementations • LREC 2020 • Christos Rodosthenous, Verena Lyding, Federico Sangati, Alex K{\"o}nig, er, Umair ul Hassan, Lionel Nicolas, Jolita Horbacauskiene, Anisia Katinskaia, Lavinia Aparaschivei
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.
no code implementations • 27 Dec 2019 • Sarwan Ali, Muhammad Ahmad, Umair ul Hassan, Muhammad Asad Khan, Shafiq Alam, Imdadullah Khan
Data analysis require a pairwise proximity measure over objects.
no code implementations • RANLP 2019 • Verena Lyding, Christos Rodosthenous, Federico Sangati, Umair ul Hassan, Lionel Nicolas, Alex K{\"o}nig, er, Jolita Horbacauskiene, Anisia Katinskaia
In this paper, we present our work on developing a vocabulary trainer that uses exercises generated from language resources such as ConceptNet and crowdsources the responses of the learners to enrich the language resource.