Name
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. |
Year | Venue | DocType |
|---|---|---|
2021 | EACL | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
10 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Patrick Xia | 1 | 0 | 0.68 |
| Guanghui Qin | 2 | 1 | 1.36 |
| Siddharth Vashishtha | 3 | 0 | 0.34 |
| Yunmo Chen | 4 | 0 | 0.68 |
| Tongfei Chen | 5 | 0 | 0.34 |
| Chandler May | 6 | 0 | 0.68 |
| Craig Harman | 7 | 185 | 11.41 |
| Kyle Rawlins | 8 | 14 | 0.98 |
| Aaron Steven White | 9 | 0 | 0.68 |
| Benjamin Van Durme | 10 | 1268 | 92.32 |