This paper describes the SemEval 2018 Shared Task on Hypernym Discovery. We put forward this task as a complementary benchmark for modeling hypernymy, a problem which has traditionally been cast as a binary classification task, taking a pair of candidate words as input. Instead, our reformulated task is defined as follows: given an input term, retrieve (or discover) its suitable hypernyms from a target corpus. We proposed five different subtasks covering three languages (English, Spanish, and Italian), and two specific domains of knowledge in English (Medical and Music). Participants were allowed to compete in any or all of the subtasks. Overall, a total of 11 teams participated, with a total of 39 different systems submitted through all subtasks. Data, results and further information about the task can be found at https://competitions. codalab. org/competitions/17119.
SemEval-2018 Task 9: Hypernym Discovery / Jose, Camacho-Collados; DELLI BOVI, Claudio; Espinosa Anke, Luis; Oramas, Sergio; Pasini, Tommaso; Santus, Enrico; Shwartz, Vered; Navigli, Roberto; Saggion, Horacio. - ELETTRONICO. - (2018), pp. 712-724. (Intervento presentato al convegno Proceedings of The 12th International Workshop on Semantic Evaluation tenutosi a New Orleans, Luisiana, USA.).
SemEval-2018 Task 9: Hypernym Discovery
Jose Camacho-Collados
;Claudio Delli Bovi
;Tommaso Pasini
;Roberto Navigli
;
2018
Abstract
This paper describes the SemEval 2018 Shared Task on Hypernym Discovery. We put forward this task as a complementary benchmark for modeling hypernymy, a problem which has traditionally been cast as a binary classification task, taking a pair of candidate words as input. Instead, our reformulated task is defined as follows: given an input term, retrieve (or discover) its suitable hypernyms from a target corpus. We proposed five different subtasks covering three languages (English, Spanish, and Italian), and two specific domains of knowledge in English (Medical and Music). Participants were allowed to compete in any or all of the subtasks. Overall, a total of 11 teams participated, with a total of 39 different systems submitted through all subtasks. Data, results and further information about the task can be found at https://competitions. codalab. org/competitions/17119.| File | Dimensione | Formato | |
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