Name
Abstract | ||
|---|---|---|
In previous work, supertag disambiguation has been presented as a robust partial parsing technique. In this paper we present two approaches: contextual models, which exploit a variety of features in order to improve supertag performance, and class-based models, which assign sets of supertags to words in order to substantially improve accuracy with only a slight increase in ambiguity. |
Year | DOI | Venue |
|---|---|---|
1999 | 10.3115/977035.977061 | EACL |
Keywords | Field | DocType |
supertag performance,slight increase,contextual model,new model,previous work,supertag disambiguation,robust partial parsing technique,class-based model | Computer science,Exploit,Natural language processing,Artificial intelligence,Parsing,Ambiguity,Machine learning | Conference |
Citations | PageRank | References |
20 | 2.42 | 15 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| john chen | 1 | 197 | 26.31 |
| Srinivas Bangalore | 2 | 1319 | 157.37 |
| K. Vijay-Shanker | 3 | 2057 | 192.47 |