2025 CONLL CoNLL 2025

Spatial relation marking across languages: extraction, evaluation, analysis

Abstract

AbstractThis paper presents a novel task, detecting Spatial Relation Markers (SRMs, like English _**in** the bag_), across languages, alongside a model for this task, RUIMTE. Using a massively parallel corpus of Bible translations, the model is evaluated against existing and baseline models on the basis of a novel evaluation set. The model presents high quality SRM extraction, and an accurate identification of situations where language have zero-marked SRMs.

🌉 Interdisciplinary Bridge — Interdisciplinary and Natural Language Processing
🧭 Keyword Pioneer — spatial relation marker
🐝 Cross-Pollinator — Artificial Intelligence, Data Science & Analytics, Interdisciplinary, Machine Learning, Natural Language Processing, Speech & Audio