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 descendente | Accuracy | ICM |
|---|---|---|---|---|---|
| avacaondata_2 | 0.5625 | 0.0551 | 0.1002 | 0.0558 | -0.92 |
| xaiTUD_1 | 0.5122 | 0.5071 | 0.4157 | 0.4500 | -0.60 |
| LPtower_3 | 0.4975 | 0.4974 | 0.4962 | 0.5038 | -0.53 |
| SINAI-TL_2 | 0.6212 | 0.6064 | 0.6059 | 0.6365 | -0.16 |
| UNED-UPM_2 | 0.7133 | 0.6947 | 0.6573 | 0.6596 | 0.00 |
| CompLingKnJ_2 | 0.6870 | 0.6875 | 0.6872 | 0.6962 | 0.05 |
| UNED-UPM_1 | 0.7287 | 0.7196 | 0.6898 | 0.6904 | 0.08 |
| BASELINE | 0.7123 | 0.7095 | 0.7107 | 0.7212 | 0.12 |
| NIT Agartala NLP Team_1 | 0.7145 | 0.7145 | 0.7145 | 0.7231 | 0.13 |
| shm2022_2 | 0.7433 | 0.7508 | 0.7406 | 0.7423 | 0.21 |
