LOME: Large Ontology Multilingual Extraction

TL;DR

LOME is a multilingual information extraction system that constructs knowledge graphs from texts by identifying entities, events, and relations, outperforming monolingual models through multilingual encoders and training.

Contribution

The paper introduces LOME, a multilingual extraction system that integrates multiple NLP tasks to build knowledge graphs, using multilingual encoders and data for improved performance.

Findings

LOME outperforms or matches monolingual state-of-the-art systems.

Uses multilingual encoders like XLM-R for better cross-lingual performance.

Provides accessible Docker and web demo implementations.

Abstract

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.