# Multilingual Open Relation Extraction Using Cross-lingual Projection

arXiv: https://arxiv.org/abs/1503.06450 · HTML: https://mlsanity.com/paper/1503.06450/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Mar 2015.

## Built on by (most cited first, 7 of 7)

- [NusaCrowd: Open Source Initiative for Indonesian NLP Resources](https://mlsanity.com/paper/2212.09648/) (2022)
- [A Comprehensive Survey on Relation Extraction: Recent Advances and New Frontiers](https://mlsanity.com/paper/2306.02051/) (2023)
- [Cross-Lingual Named Entity Recognition Using Parallel Corpus: A New Approach Using XLM-RoBERTa Alignment](https://mlsanity.com/paper/2101.11112/) (2021)
- [Multilingual Relation Classification via Efficient and Effective Prompting](https://mlsanity.com/paper/2210.13838/) (2022)
- [MultiTACRED: A Multilingual Version of the TAC Relation Extraction Dataset](https://mlsanity.com/paper/2305.04582/) (2023)
- [Building a Japanese Document-Level Relation Extraction Dataset Assisted by Cross-Lingual Transfer](https://mlsanity.com/paper/2404.16506/) (2024)
- [Cross-lingual Relation Extraction with Large Language Models: Zero-Shot, Few-Shot, and Fine-Tuned Evaluation on Romanian](https://mlsanity.com/paper/2606.31718/) (2026)

## Abstract (arXiv)

Open domain relation extraction systems identify relation and argument phrases in a sentence without relying on any underlying schema. However, current state-of-the-art relation extraction systems are available only for English because of their heavy reliance on linguistic tools such as part-of-speech taggers and dependency parsers. We present a cross-lingual annotation projection method for language independent relation extraction. We evaluate our method on a manually annotated test set and present results on three typologically different languages. We release these manual annotations and extracted relations in 61 languages from Wikipedia.