Name
Abstract | ||
|---|---|---|
KnowNER is a multilingual Named Entity Recognition (NER) system that leverages different degrees of external knowledge. A novel modular framework divides the knowledge into four categories according to the depth of knowledge they convey. Each category consists of a set of features automatically generated from different information sources (such as a knowledge-base, a list of names or document-specific semantic annotations) and is used to train a conditional random field (CRF). Since those information sources are usually multilingual, KnowNER can be easily trained for a wide range of languages. In this paper, we show that the incorporation of deeper knowledge systematically boosts accuracy and compare KnowNER with state-of-the-art NER approaches across three languages (i.e., English, German and Spanish) performing amongst state-of-the art systems in all of them. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Computation and Language | Conditional random field,Computer science,Natural language processing,Artificial intelligence,Modular design,Named-entity recognition,German |
DocType | Volume | Citations |
Journal | abs/1709.03544 | 0 |
PageRank | References | Authors |
0.34 | 11 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dominic Seyler | 1 | 9 | 4.29 |
| Tatiana Dembelova | 2 | 0 | 0.34 |
| Luciano Del Corro | 3 | 147 | 6.91 |
| Johannes Hoffart | 4 | 1362 | 52.62 |
| Gerhard Weikum | 5 | 12710 | 2146.01 |