Cross-Lingual Entity Linking

XL-BEL is a benchmark for cross-lingual biomedical entity linking (XL-BEL). The benchmark spans 10 typologically diverse languages.

SV-Ident comprises 4,248 sentences from social science publications in English and German. The data is the official data for the Shared Task: “Survey Variable Identification in Social Science Publications” (SV-Ident) 2022. Sentences are labeled with variables that are mentioned either explicitly or implicitly. The dataset supports the following tasks: - Variable Detection: identifying whether a sentence contains a variable mention or not. - Variable Disambiguation: identifying which variable from a given vocabulary is mentioned in a sentence.

LEMONADE is a large, expert-annotated dataset for event extraction from news articles in 20 languages: English, Spanish, Arabic, French, Italian, Russian, German, Turkish, Burmese, Indonesian, Ukrainian, Korean, Portuguese, Dutch, Somali, Nepali, Chinese, Persian, Hebrew, and Japanese.

This dataset contains a collection of texts from publications from a broad range of social science domains (e.g., economics, politics, psychology, etc.). The texts are annotated with labels for Survey Item Linking (SIL), an Entity Linking (EL) task. SIL is divided into two sub-tasks: Mention Detection (MD), a binary text classification task, and Entity Disambiguation (ED), a sentence similarity task. Sentences that mention survey items are labeled with the IDs of entities from a knowledge base (GSIM). SILD contains 20,454 sentences in English and German from 100 publications.