Name
Abstract | ||
|---|---|---|
In this paper we describe our machine learning approach to the generation of referring expressions. As our algorithm we use memory-based learning. Our results show that in case of predicting the TYPE of the expression, having one general classifier gives the best results. On the contrary, when predicting the full set of properties of an expression, a combined set of specialized classifiers for each subdomain gives the best performance. |
Year | Venue | Keywords |
|---|---|---|
2008 | INLG | specialized classifier,repeated reference,full set,combined set,best result,memory-based learning,best performance,general classifier |
Field | DocType | Citations |
Expression (mathematics),Computer science,Artificial intelligence,Classifier (linguistics),Machine learning | Conference | 1 |
PageRank | References | Authors |
0.38 | 1 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Iris Hendrickx | 1 | 285 | 30.91 |
| Walter Daelemans | 2 | 2019 | 269.73 |
| Kim Luyckx | 3 | 88 | 8.27 |
| Roser Morante | 4 | 442 | 33.20 |
| Vincent Van Asch | 5 | 109 | 7.70 |