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| Content Provider | ACM Digital Library |
|---|---|
| Author | Ramakrishnan, Ganesh Sindhwani, Vikas Bairi, Ramakrishna B. |
| Abstract | Identifying the right choice of categories for organizing and representing a large digital library of documents is a challenging task. A completely automated approach to category creation from the underlying collection could be prone to noise. On the other hand, an absolutely manual approach to the creation of categories could be cumbersome and expensive. Through this work, we propose an intermediate solution, in which, a global, collaboratively-developed Knowledge Graph of categories can be adapted to a local document categorization problem effectively. We model our classification problem as that of inferring structured labels in an Associative Markov Network meta-model over SVMs, where the label space is derived from a large global category graph. We propose a joint Active Learning model over the label and the document spaces in order to incorporate active labeling feedback from the users to train the model parameters. |
| Starting Page | 132 |
| Ending Page | 141 |
| Page Count | 10 |
| File Format | |
| ISBN | 9781450326278 |
| DOI | 10.1145/2628194.2628237 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2014-07-07 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Topic identification Personalization Active learning Multi-label classification Text categorization Large scale text classification |
| Content Type | Text |
| Resource Type | Article |
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