Name
Abstract | ||
|---|---|---|
To retrieve relevant documents from an enormous document collection, we usually utilize the similarity or distance measure between a query and the docu- ments, or apply document clustering techniques to the document collection and partition it into relevant doc- ument groups. For patent retrieval, however, it is dif- ficult to retrieve documents by using query terms only, because complex terminologies specific to patents ap- pear in them. One approach to solving this problem is to use query expansion techniques. We have ex- tended the usual vector space model by utilizing co- clustering techniques. We generate a hierarchy of clusters by applying these techniques to the document collection with different levels of cluster granularity. The query is then expanded by using this hierarchy of clusters. We participated in the NTCIR-5 Patent Re- trieval Task (Document Retrieval Subtask) using our system and present the effectiveness of our approach for patent retrieval with experiments using the NTCIR- 4 and NTCIR-5 test collections. |
Year | Venue | Field |
|---|---|---|
2005 | NTCIR | Data mining,Cluster (physics),Query expansion,Information retrieval,Computer science,Document clustering,Ranking (information retrieval),Vector space model,Document retrieval,Granularity,Hierarchy |
DocType | Citations | PageRank |
Conference | 3 | 0.43 |
References | Authors | |
2 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hironori Doi | 1 | 45 | 3.34 |
| Yohei Seki | 2 | 223 | 18.84 |
| M. Aono | 3 | 643 | 60.79 |