  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.
