LOME: Large Ontology Multilingual Extraction

Patrick Xia; Guanghui Qin; Siddharth Vashishtha; Yunmo Chen; Tongfei Chen; Chandler May; Craig Harman; Kyle Rawlins; Aaron Steven White; Benjamin Van Durme

arXiv · 2101.12175

LOME: Large Ontology Multilingual Extraction

Patrick Xia·Guanghui Qin·Siddharth Vashishtha·Yunmo Chen·Tongfei Chen·Chandler May·Craig Harman·Kyle Rawlins·Aaron Steven White·Benjamin Van Durme

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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.

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Patrick Xia, Guanghui Qin, Siddharth Vashishtha, Yunmo Chen, Tongfei Chen, Chandler May, Craig Harman, Kyle Rawlins, Aaron Steven White, Benjamin Van Durme. 2021-01-28. LOME: Large Ontology Multilingual Extraction. https://arxiv.org/abs/2101.12175

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