no code implementations • SemEval (NAACL) 2022 • Yaakov HaCohen-Kerner, Ilan Meyrowitsch, Matan Fchima
Our experiments indicate that pre-processing stage is a must for a successful model.
no code implementations • SemEval (NAACL) 2022 • Yaakov HaCohen-Kerner, Matan Fchima, Ilan Meyrowitsch
In this paper, we describe our submissions to SemEval-2022 contest.
no code implementations • SEMEVAL 2020 • Moshe Uzan, Yaakov HaCohen-Kerner
Our best submission was a model we built for offensive language identification in Danish using Random Forest.
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 • SEMEVAL 2019 • Yaakov HaCohen-Kerner, Elyashiv Shayovitz, Shalom Rochman, Eli Cahn, Gal Didi, Ziv Ben-David
In this paper, we describe our submissions to SemEval-2019 contest.
no code implementations • SEMEVAL 2019 • Yaakov HaCohen-Kerner, Ziv Ben-David, Gal Didi, Eli Cahn, Shalom Rochman, Elyashiv Shayovitz
We tackled all three sub-tasks in this task {``}OffensEval - Identifying and Categorizing Offensive Language in Social Media{''}.
no code implementations • COLING 2016 • Chaya Liebeskind, Yaakov HaCohen-Kerner
Identification of Multi-Word Expressions (MWEs) lies at the heart of many natural language processing applications.
no code implementations • LREC 2016 • Chaya Liebeskind, Yaakov HaCohen-Kerner
The lexical resource enables to sample a set of positive examples for Hebrew VN-MWEs.