Name
Abstract | ||
|---|---|---|
When NLP is used to support research in the humanities, new methodological issues come into play. NLP methods may introduce a bias in their analysis that can influence the results of the hypothesis a humanities scholar is testing. This paper addresses this issue in the context of BiographyNet a multi-disciplinary project involving NLP, Linked Data and history. We introduce the project to the NLP community. We argue that it is essential for historians to get insight into the provenance of information, including how information was extracted from text by NLP tools. |
Year | Venue | Keywords |
|---|---|---|
2014 | LREC 2014 - NINTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION | Digital history,provenance modeling,Linked Data |
Field | DocType | Citations |
Computer science,Linked data,Comparative historical research,Artificial intelligence,Natural language processing | Conference | 4 |
PageRank | References | Authors |
0.63 | 9 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Antske Fokkens | 1 | 63 | 12.75 |
| Serge Ter Braake | 2 | 14 | 2.46 |
| Niels Ockeloen | 3 | 16 | 2.84 |
| Piek Vossen | 4 | 387 | 61.59 |
| Susan Legêne | 5 | 40 | 3.44 |
| Guus Schreiber | 6 | 1448 | 150.58 |