Memory-Based Learning: Using Similarity for Smoothing
| dc.creator | Zavrel, Jakub | |
| dc.creator | Daelemans, Walter | |
| dc.date | 1997-05-12 | |
| dc.date.accessioned | 2026-07-07T09:10:48Z | |
| dc.date.available | 2026-07-07T09:10:48Z | |
| dc.description | This paper analyses the relation between the use of similarity in Memory-Based Learning and the notion of backed-off smoothing in statistical language modeling. We show that the two approaches are closely related, and we argue that feature weighting methods in the Memory-Based paradigm can offer the advantage of automatically specifying a suitable domain-specific hierarchy between most specific and most general conditioning information without the need for a large number of parameters. We report two applications of this approach: PP-attachment and POS-tagging. Our method achieves state-of-the-art performance in both domains, and allows the easy integration of diverse information sources, such as rich lexical representations. | |
| dc.description | 8 pages, uses aclap.sty, To appear in Proc. ACL/EACL 97 | |
| dc.identifier | https://arxiv.org/abs/cmp-lg/9705010 | |
| dc.identifier | http://arxiv.org/abs/cmp-lg/9705010 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/151460 | |
| dc.subject | Computation and Language | |
| dc.title | Memory-Based Learning: Using Similarity for Smoothing | |
| dc.type | text |