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| Content Provider | Springer Nature Link |
|---|---|
| Author | Al Shboul, Bashar Myaeng, Sung Hyon |
| Copyright Year | 2013 |
| Abstract | Relevance feedback methods generally suffer from topic drift caused by word ambiguities and synonymous uses of words. Topic drift is an important issue in patent information retrieval as people tend to use different expressions describing similar concepts causing low precision and recall at the same time. Furthermore, failing to retrieve relevant patents to an application during the examination process may cause legal problems caused by granting an existing invention. A possible cause of topic drift is utilizing a relevance feedback-based search method. As a way to alleviate the inherent problem, we propose a novel query phrase expansion approach utilizing semantic annotations in Wikipedia pages, trying to enrich queries with phrases disambiguating the original query words. The idea was implemented for patent search where patents are classified into a hierarchy of categories, and the analyses of the experimental results showed not only the positive roles of phrases and words in retrieving additional relevant documents through query expansion but also their contributions to alleviating the query drift problem. More specifically, our query expansion method was compared against relevance-based language model, a state-of-the-art query expansion method, to show its superiority in terms of MAP on all levels of the classification hierarchy. |
| Starting Page | 430 |
| Ending Page | 451 |
| Page Count | 22 |
| File Format | |
| ISSN | 13864564 |
| Journal | Information Retrieval |
| Volume Number | 17 |
| Issue Number | 5-6 |
| e-ISSN | 15737659 |
| Language | English |
| Publisher | Springer Netherlands |
| Publisher Date | 2013-11-21 |
| Publisher Place | Dordrecht |
| Access Restriction | Subscribed |
| Subject Keyword | Patent search Phrase-based query expansion Wikipedia categories Clarity Retrievability Information Storage and Retrieval Document Preparation and Text Processing Data Mining and Knowledge Discovery Data Structures, Cryptology and Information Theory Pattern Recognition |
| Content Type | Text |
| Resource Type | Article |
| Subject | Library and Information Sciences Information Systems |
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