EXIST 2022: Sexism detection (EN)

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.

Competición

EXIST 2022: sEXism Identification in Social neTworks

Idioma

Inglés

URL Tarea

http://nlp.uned.es/exist2022/

NLP topic

detección de odio

Tarea abstracta

Clasificación

Dataset

EXIST-2022-ES

Año

2022

Enlace publicación

http://journal.sepln.org/sepl…

Métrica Ranking

Accuracy

Mejores resultados para la tarea

Sistema	Precisión	Recall	F1	Accuracy	ICM
AI-UPV_2	0.7718	0.7799	0.7680	0.7692	0.30
besiguenza_1	0.7457	0.7508	0.7472	0.7519	0.23
AI-UPV_3	0.8140	0.8212	0.8161	0.8192	0.43
CIMATCOLMEX_2	0.8060	0.8126	0.8081	0.8115	0.41
AIT_FHSTP_1	0.7705	0.7788	0.7693	0.7712	0.30
CIMATCOLMEX_3	0.8116	0.8175	0.8136	0.8173	0.43
AIT_FHSTP_2	0.7636	0.7696	0.7534	0.7538	0.26
CompLingKnJ_1	0.7727	0.7778	0.7744	0.7788	0.31
CompLingKnJ_2	0.6870	0.6875	0.6872	0.6962	0.05
2539404758	0.7756	0.7822	0.7772	0.7808	0.32