FII-UAIC at SemEval-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text Using CNN

no code implementations • SEMEVAL 2020 • Lavinia Aparaschivei, Andrei Palihovici, Daniela G{\^\i}fu

The {``}Sentiment Analysis for Code-Mixed Social Media Text{''} task at the SemEval 2020 competition focuses on sentiment analysis in code-mixed social media text , specifically, on the combination of English with Spanish (Spanglish) and Hindi (Hinglish).

Sentiment Analysis

Using Crowdsourced Exercises for Vocabulary Training to Expand ConceptNet

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.

Chatbot

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

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.