Name
Abstract | ||
|---|---|---|
In this work, we discuss the importance of external knowledge for performing Named Entity Recognition (NER). We present a novel modular framework that 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 knowledgebase, a list of names, or document-specific semantic annotations. Further, we show the effects on performance when incrementally adding deeper knowledge and discuss effectiveness/efficiency trade-offs. |
Year | Venue | Field |
|---|---|---|
2018 | PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 2 | Computer science,Artificial intelligence,Natural language processing,Named-entity recognition |
DocType | Volume | Citations |
Conference | P18-2 | 0 |
PageRank | References | Authors |
0.34 | 0 | 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 |