We present LOME, a system for performing multilingual information extraction. Given a text document as input, our core system identifies spans of textual entity and event mentions with a FrameNet (Baker et al., 1998) parser. It subsequently performs coreference resolution, fine-grained entity typing, and temporal relation prediction between events. By doing so, the system constructs an event and entity focused knowledge graph. We can further apply third-party modules for other types of annotation, like relation extraction. Our (multilingual) first-party modules either outperform or are competitive with the (monolingual) state-of-the-art. We achieve this through the use of multilingual encoders like XLM-R (Conneau et al., 2020) and leveraging multilingual training data. LOME is available as a Docker container on Docker Hub. In addition, a lightweight version of the system is accessible as a web demo.
@article{arxiv.2101.12175,
title = {LOME: Large Ontology Multilingual Extraction},
author = {Patrick Xia and Guanghui Qin and Siddharth Vashishtha and Yunmo Chen and Tongfei Chen and Chandler May and Craig Harman and Kyle Rawlins and Aaron Steven White and Benjamin Van Durme},
journal= {arXiv preprint arXiv:2101.12175},
year = {2021}
}