Binary classification problem, consisting in determmine whether a text or message is sexist or not. It includes any type of sexist expression or related phenomena, like descriptive or reported assertions where the sexist message is a report or a description of a sexist event. In particular, we consider two labels:
- Sexist: the tweet or gab expresses sexist behaviours or discourses.
- Non-Sexist: the tweet or gab does not express any sexist behaviour or discourse.
Publicación
Francisco Rodríguez-Sánchez, Jorge Carrillo-de-Albornoz, Laura Plaza, Adrián Mendieta-Aragón, Guillermo Marco-Remón, Maryna Makeienko, María Plaza, Julio Gonzalo, Damiano Spina, Paolo Rosso (2022) Overview of EXIST 2022: sEXism Identification in Social neTworks. Procesamiento del Lenguaje Natural, Revista nº 69, septiembre de 2022, pp. 229-240.
Idioma
Inglés
URL Tarea
NLP topic
Tarea abstracta
Dataset
Año
2022
Enlace publicación
Métrica Ranking
Accuracy
Mejores resultados para la tarea
Sistema | Precisión | Recall | F1 Ordenar ascendente | Accuracy | ICM |
---|---|---|---|---|---|
avacaondata_3 | 0.8454 | 0.8454 | 0.8454 | 0.8500 | 0.52 |
avacaondata_1 | 0.8454 | 0.8454 | 0.8454 | 0.8500 | 0.52 |
SINAI-TL_1 | 0.8179 | 0.8252 | 0.8200 | 0.8231 | 0.45 |
SINAI-TL_3 | 0.8173 | 0.8224 | 0.8192 | 0.8231 | 0.44 |
I2C_1 | 0.8161 | 0.8253 | 0.8171 | 0.8192 | 0.44 |
AI-UPV_3 | 0.8140 | 0.8212 | 0.8161 | 0.8192 | 0.43 |
CIMATCOLMEX_3 | 0.8116 | 0.8175 | 0.8136 | 0.8173 | 0.43 |
CIMATCOLMEX_2 | 0.8060 | 0.8126 | 0.8081 | 0.8115 | 0.41 |
multiaztertest_2 | 0.8024 | 0.7997 | 0.8009 | 0.8077 | 0.39 |
ELiRF-VRAIN_3 | 0.7956 | 0.7975 | 0.7965 | 0.8019 | 0.37 |
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