Name
Abstract | ||
|---|---|---|
In this paper we present the first corpus where one million Dutch words from a variety of text genres have been annotated with semantic roles. 500K have been completely manually verified and used as training material to automatically label another 500K. All data has been annotated following an adapted version of the PropBank guidelines. The corpus's rich text type diversity and the availability of manually verified syntactic dependency structures allowed us to experiment with an existing semantic role labeler for Dutch. In order to test the system's portability across various domains, we experimented with training on individual domains and compared this with training on multiple domains by adding more data. Our results show that training on large data sets is necessary but that including genre-specific training material is also crucial to optimize classification. We observed that a small amount of in-domain training data is already sufficient to improve our semantic role labeler. |
Year | Venue | Keywords |
|---|---|---|
2012 | LREC 2012 - EIGHTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION | corpus annotation,semantic role labeling,cross-domain |
Field | DocType | Citations |
Training set,Data set,Annotation,Information retrieval,Computer science,PropBank,Artificial intelligence,Software portability,Natural language processing,Syntax,Semantic role labeling,Rich Text Format | Conference | 1 |
PageRank | References | Authors |
0.42 | 8 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Orphée De Clercq | 1 | 117 | 9.61 |
| Véronique Hoste | 2 | 319 | 35.92 |
| Paola Monachesi | 3 | 122 | 17.96 |