Name
Title | ||
|---|---|---|
A German Corpus for Fine-Grained Named Entity Recognition and Relation Extraction of Traffic and Industry Events. |
Abstract | ||
|---|---|---|
Monitoring mobility- and industry-relevant events is important in areas such as personal travel planning and supply chain management, but extracting events pertaining to specific companies, transit routes and locations from heterogeneous, high-volume text streams remains a significant challenge. This work describes a corpus of German-language documents which has been annotated with fine-grained geo-entities, such as streets, stops and routes, as well as standard named entity types. It has also been annotated with a set of 15 traffic- and industry-related n-ary relations and events, such as accidents, traffic jams, acquisitions, and strikes. The corpus consists of newswire texts, Twitter messages, and traffic reports from radio stations, police and railway companies. It allows for training and evaluating both named entity recognition algorithms that aim for fine-grained typing of geo-entities, as well as n-ary relation extraction systems. |
Year | Venue | Field |
|---|---|---|
2018 | LREC | Computer science,Speech recognition,Artificial intelligence,Natural language processing,Named-entity recognition,Relationship extraction,German |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Martin Schiersch | 1 | 0 | 0.34 |
| Veselina Mironova | 2 | 2 | 1.07 |
| Maximilian Schmitt | 3 | 0 | 1.01 |
| Philippe Thomas | 4 | 238 | 12.94 |
| aleksandra gabryszak | 5 | 5 | 2.79 |
| Leonhard Hennig | 6 | 72 | 10.62 |