Name
Abstract | ||
|---|---|---|
Many entity recognition approaches classify recognised entities into a limited set of coarse-grained entity types. However, for deeper natural language analysis and end-user tasks, fine-grained entity types are more useful. For example, while standard named entity recognition may determine that an entity is a person knowing whether that entity is a politician or an actor is important for determining whether, in a subsequent relation extraction task, a relation should be acts or governs. Currently, fine-grained entity typing has only been investigated for English. In this paper, we present a fine-grained entity typing system for Dutch and Spanish using training data extracted from Wikipedia and DBpedia. Our system achieves comparable performance to English with an F(1 )measure of .90 on over 40 types for both Dutch and Spanish. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-59888-8_23 | Lecture Notes in Artificial Intelligence |
Field | DocType | Volume |
Training set,Computer science,Natural language analysis,Typing,Artificial intelligence,Natural language processing,Named-entity recognition,Distributed computing,Relationship extraction | Conference | 10318 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
17 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marieke van Erp | 1 | 284 | 24.19 |
| Piek Vossen | 2 | 387 | 61.59 |