Name
Abstract | ||
|---|---|---|
Kernel-based learning algorithms have been shown to achieve state-of-the-art results in many Natural Language Processing (NLP) tasks. We present KELP, a Java framework that supports the implementation of both kernel-based learning algorithms and kernel functions over generic data representation, e.g. vectorial data or discrete structures. The framework has been designed to decouple kernel functions and learning algorithms: once a new kernel function has been implemented it can be adopted in all the available kernel-machine algorithms. The platform includes different Online and Batch Learning algorithms for Classification, Regression and Clustering, as well as several Kernel functions, ranging from vector-based to structural kernels. This paper will show the main aspects of the framework by applying it to different NLP tasks. |
Year | Venue | DocType |
|---|---|---|
2015 | PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING (ACL-IJCNLP 2015): SYSTEM DEMONSTRATIONS | Conference |
Volume | Citations | PageRank |
P15-4 | 15 | 0.65 |
References | Authors | |
14 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Simone Filice | 1 | 89 | 8.75 |
| Giuseppe Castellucci | 2 | 67 | 8.41 |
| Danilo Croce | 3 | 314 | 39.05 |
| Roberto Basili | 4 | 1308 | 155.68 |