GeoLingIt is the first shared task on geolocation of linguistic variation in Italy from social media posts comprising content in language varieties other than standard Italian (i.e., regional Italian, and languages and dialects of Italy). The task is articulated into two subtasks of increasing complexity for which only textual content is allowed: i) coarse-grained geolocation, aiming at predicting the region in which the variety expressed in the post is spoken, and ii) fine-grained geolocation, aiming at predicting its exact coordinates. Both tasks can be either at the country level (standard track) or restricted to a linguistic area of choice (special track). GeoLingIt has attracted wide interest at the Evalita 2023 evaluation campaign with 37 registrations and 35 submitted runs. In this paper, we present the task and data, the evaluation criteria, the participants' results, an analysis of their approaches, and the main insights from the shared task.

GeoLingIt at EVALITA 2023: Overview of the Geolocation of Linguistic Variation in Italy Task

Alan Ramponi
;
Camilla Casula
2023-01-01

Abstract

GeoLingIt is the first shared task on geolocation of linguistic variation in Italy from social media posts comprising content in language varieties other than standard Italian (i.e., regional Italian, and languages and dialects of Italy). The task is articulated into two subtasks of increasing complexity for which only textual content is allowed: i) coarse-grained geolocation, aiming at predicting the region in which the variety expressed in the post is spoken, and ii) fine-grained geolocation, aiming at predicting its exact coordinates. Both tasks can be either at the country level (standard track) or restricted to a linguistic area of choice (special track). GeoLingIt has attracted wide interest at the Evalita 2023 evaluation campaign with 37 registrations and 35 submitted runs. In this paper, we present the task and data, the evaluation criteria, the participants' results, an analysis of their approaches, and the main insights from the shared task.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11582/341288
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