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| Content Provider | Springer Nature Link |
|---|---|
| Author | Shoaib, Muhammad Daud, Ali Khiyal, Malik Sikandar Hayat |
| Copyright Year | 2015 |
| Abstract | In many real-life text mining applications such as clustering academic documents, citation matching and author name disambiguation (AND), similar publications are grouped together by exploiting similarity among them. Most of AND approaches, especially unsupervised ones, focus on either proposing new/alternate algorithms and/or using new/alternate sources of information. Researchers from digital library community pay least attention to similarity measures. They try ready-made alternate measures to estimate optimum similarity among publications. These ready-made measures may not provide real picture of similarity among publications. In this work, we propose four similarity measures specially designed for author names, co-authors and short and long text segments. Our proposed measures provide more realistic picture of similarity among publications than previous measures. Our proposed measures can be applied in many real-life scenarios where either name entities (not necessarily human names) or text documents or both are compared in pairwise fashion. We compare our measures with Jaccard coefficient and state-of-the-art cosine measure based on vector space model. Experiments on synthetic and real data show that our proposed measures are more logical and realistic than baseline methods. |
| Starting Page | 1591 |
| Ending Page | 1605 |
| Page Count | 15 |
| File Format | |
| ISSN | 13198025 |
| Journal | Arabian Journal for Science and Engineering |
| Volume Number | 40 |
| Issue Number | 6 |
| e-ISSN | 21914281 |
| Language | English |
| Publisher | Springer Berlin Heidelberg |
| Publisher Date | 2015-04-30 |
| Publisher Place | Berlin, Heidelberg |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Entity resolution Bibliographic databases Syntactic similarity Vector space model Engineering Science |
| Content Type | Text |
| Resource Type | Article |
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