Name
Abstract | ||
|---|---|---|
This paper describes our SVM-based system and the techniques we used to adapt the approach for the specifics of the F-term patent classification subtask at NTCIR-6 Patent Retrieval Task. Our system obtained the best results according to two of the three measures used for performance evaluation. Moreover, the re- sults from some additional experiments demonstrate that our system has benefited from the SVM adapta- tions which we carried out. It also benefited from us- ing the full patent text in addition to using the F-term description as extra training material. However, our results using an SVM variant designed for hierarchical classification were much worse than those achieved with flat SVM classification. At the end of the paper we discuss the possible reasons for this, in the context of the F-term classification task. |
Year | Venue | Field |
|---|---|---|
2007 | NTCIR | Data mining,Patent retrieval,Support vector machine,Patent classification,Artificial intelligence,Engineering,Machine learning |
DocType | Citations | PageRank |
Conference | 3 | 0.43 |
References | Authors | |
4 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yaoyong Li | 1 | 393 | 26.55 |
| Kalina Bontcheva | 2 | 2538 | 211.33 |
| Hamish Cunningham | 3 | 2426 | 255.41 |