Guided Distant Supervision for Multilingual Relation Extraction Data: Adapting to a New Language

arXiv:2403.17143v1 Announce Type: cross
Abstract: Relation extraction is essential for extracting and understanding biographical information in the context of digital humanities and related subjects. There is a growing interest in the community to build datasets capable of training machine learning models to extract relationships. However, annotating such datasets can be expensive and time-consuming, in addition to being limited to English. This paper applies guided distant supervision to create a large biographical relationship extraction dataset for German. Our dataset, composed of …

abstract arxiv build community context cs.cl cs.lg data datasets digital digital humanities extract extraction however humanities information language machine machine learning machine learning models multilingual relationships supervision training type understanding