| Título |
Dependency Language Modeling Using KNN and PLSI |
| Tipo |
Congreso |
| Sub-tipo |
SCOPUS |
| Descripción |
Lecture Notes in Computer Science |
| Resumen |
In this paper we present a comparison of two language models based on dependency triples. We explore using the verb only for predicting the most plausible argument as in selectional preferences, as well as using both the verb and argument for predicting another argument. This latter causes a problem of data sparseness that must be solved by different techniques for data smoothing. Based on our results on the K-Nearest Neighbor model (KNN) algorithm we conclude that adding more information is useful for attaining higher precision, while the PLSI model was inconveniently sensitive to this information, yielding better results for the simpler model (using the verb only). Our results suggest that combining the strengths of both algorithms would provide best results. |
| Observaciones |
8th Mexican International Conference on Artificial Intelligence, MICAI 2009; Guanajuato; Mexico; 9 November 2009 through 13 November 2009; Code 78796; ISBN: 3642052576;978-364205257-6 |
| Lugar |
Guanajuato |
| País |
Mexico |
| No. de páginas |
136-144 |
| Vol. / Cap. |
5845 |
| Inicio |
2009-11-09 |
| Fin |
2009-11-13 |
| ISBN/ISSN |
3642052576;978-36420 |
